{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-max-0-153-4-retriever-r04-20260924T221413Z", "taskChecksum": "2934a28187546465c2858ba8747e6b6ee72cb7012e2d9c510947578af71a3781", "source": { "format": "codex-rollout", "sha256": "2980d6bdb86c7998e8ecbf7306a3e8f889d654652d0a202a083fa3530a0aeab9", "records": 1184, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 168, "item_completed:AgentMessage": 19, "token_count": 178 } }, "sessionId": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T00:25:21.104Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:21.104Z", "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": "2026-09-25T00:25:22.562Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:22.562Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T00:25:22.562Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T00:25:22.562Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-25T00:25:22.562Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:22.562Z", "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": "2026-09-25T00:25:22.562Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:22.562Z", "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": "2026-09-25T00:25:22.563Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:22.563Z", "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": "2026-09-25T00:25:22.563Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T00:25:22.564Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:22.565Z", "data": { "type": "turn_context", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-25T00:25:22.582Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:25:22.582Z", "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": "2026-09-25T00:25:25.342Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:25.342Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:25:26.433Z", "text": "I’ll first profile the event windows, catalog, eligibility masks, and evaluator semantics. Then I’ll build a scalable event-sampled two-tower baseline, measure it publicly, and iterate while keeping every artifact under `/app/solution/`." }, { "kind": "tool", "timestamp": "2026-09-25T00:25:28.088Z", "callId": "call_pAxzLM3I1oMsFLPDfZJkrh2o", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lah /task/data && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:25:28.157Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\ntotal 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# 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": "2026-09-25T00:25:28.121Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf72c3ac87d29ac364de9968da08", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:28.150Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a88997aa-5360-4980-9ac2-e09abec688df", "process_id": "26185", "command": [ "/bin/sh", "-lc", "ls -lah /task/data && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lah /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,260p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,220p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# 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": 8080 }, "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T00:25:29.835Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:29.835Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:25:31.006Z", "text": "The evaluator confirms that only the top 10 affect quality, while all 100 outputs must remain cutoff-eligible. I’m now quantifying overlap, repeat behavior, recency, and preference-state signal so the tower inputs and sampled objective match the actual discovery labels." }, { "kind": "tool", "timestamp": "2026-09-25T00:25:35.341Z", "callId": "call_JfxRjTgNAo6nsSEjJL9uAamg", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\\nprint('\\\\nNPZ')\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=mask.sum(1)\\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:25:35.429Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:25:35.392Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f5d057d-ed70-4d24-96fe-e13ef2c2a20a", "process_id": "35860", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 7180 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:35.428Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf78725c87d2b688ff2b7c760cd9", "usage": { "input_tokens": 19151, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 352, "reasoning_output_tokens": 46, "total_tokens": 19503 }, "turn_token_usage": { "input_tokens": 33727, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 605, "reasoning_output_tokens": 143, "total_tokens": 34332 }, "thread_token_usage": { "input_tokens": 33727, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 605, "reasoning_output_tokens": 143, "total_tokens": 34332 } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T00:25:36.988Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:36.988Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:25:37.794Z", "text": "The default shell interpreter is not the task’s pinned Python environment, so I’m locating the provided runtime before doing any modeling; no packages will be installed or changed." }, { "kind": "tool", "timestamp": "2026-09-25T00:25:39.566Z", "callId": "call_JbAVbnwc34Ctn16ZbLeJ8Gep", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -50; ls -la /task; ls -la /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:25:39.661Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:25:39.598Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf7faa5087d2ab70c5a291f557d6", "usage": { "input_tokens": 19559, "cached_input_tokens": 18944, "cache_write_input_tokens": 0, "output_tokens": 181, "reasoning_output_tokens": 34, "total_tokens": 19740 }, "turn_token_usage": { "input_tokens": 53286, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 786, "reasoning_output_tokens": 177, "total_tokens": 54072 }, "thread_token_usage": { "input_tokens": 53286, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 786, "reasoning_output_tokens": 177, "total_tokens": 54072 } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:39.657Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6a03c07b-2dd2-4f94-92a2-27f6b4e46851", "process_id": "78072", "command": [ "/bin/sh", "-lc", "command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; ls -la /task; ls -la /app" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; ls -la /task; ls -la /app" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7980 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T00:25:41.587Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:41.587Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:25:43.269Z", "callId": "call_eA0Bz2LQp9pq96B9N04rUgiM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:25:44.064Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\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=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\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": "2026-09-25T00:25:43.291Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf83e6f487d29198a363e601cf24", "usage": { "input_tokens": 20009, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 141, "reasoning_output_tokens": 36, "total_tokens": 20150 }, "turn_token_usage": { "input_tokens": 73295, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 927, "reasoning_output_tokens": 213, "total_tokens": 74222 }, "thread_token_usage": { "input_tokens": 73295, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 927, "reasoning_output_tokens": 213, "total_tokens": 74222 } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:44.062Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-638d17c3-aee9-48cb-93e3-9fc4475bfe5d", "process_id": "90967", "command": [ "/bin/sh", "-lc", "find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\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=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\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/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\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=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\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": 628445346 }, "formatted_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\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=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T00:25:45.376Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:45.376Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:25:47.507Z", "callId": "call_GkE6TY0OHlUII1atqozg4ToF", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\\nimport pandas,torch,pyarrow,numpy\\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:25:51.421Z", "content": "Script completed\nWall time 3.9 seconds\nOutput:\n\n/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:20 ..\n-r--r--r-- 1 root root 0 Sep 25 00:20 .lock\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 bin\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 etc\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 include\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 lib\nlrwxrwxrwx 1 root root 3 Sep 25 00:20 lib64 -> lib\n-r--r--r-- 1 root root 171 Sep 25 00:20 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 .\ndr-xr-xr-x 1 root root 4096 Sep 25 00:20 ..\n-r--r--r-- 1 root root 9033 Sep 25 00:20 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 25 00:20 activate\n-r--r--r-- 1 root root 920 Sep 25 00:20 activate.csh\n-r--r--r-- 1 root root 2195 Sep 25 00:20 activate.fish\n-r-xr-xr-x 1 root root 302 Sep 25 00:20 chardetect\n-r-xr-xr-x 1 root root 304 Sep 25 00:20 dotenv\n-r-xr-xr-x 1 root root 308 Sep 25 00:20 f2py\n-r-xr-xr-x 1 root root 299 Sep 25 00:20 idna\n-r-xr-xr-x 1 root root 297 Sep 25 00:20 isympy\n-r-xr-xr-x 1 root root 305 Sep 25 00:20 jsonschema\n-r-xr-xr-x 1 root root 311 Sep 25 00:20 jupyter\n-r-xr-xr-x 1 root root 311 Sep 25 00:20 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Sep 25 00:20 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Sep 25 00:20 jupyter-trust\n-r-xr-xr-x 1 root root 311 Sep 25 00:20 jupytext\n-r-xr-xr-x 1 root root 315 Sep 25 00:20 jupytext-config\n-r-xr-xr-x 1 root root 301 Sep 25 00:20 kaggle\n-r-xr-xr-x 1 root root 312 Sep 25 00:20 markdown-it\n-r-xr-xr-x 1 root root 325 Sep 25 00:20 normalizer\n-r-xr-xr-x 1 root root 308 Sep 25 00:20 numpy-config\n-r-xr-xr-x 1 root root 313 Sep 25 00:20 pip\n-r-xr-xr-x 1 root root 313 Sep 25 00:20 pip3\n-r-xr-xr-x 1 root root 313 Sep 25 00:20 pip3.12\nlrwxrwxrwx 1 root root 21 Sep 25 00:20 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 25 00:20 python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 25 00:20 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Sep 25 00:20 slugify\n-r-xr-xr-x 1 root root 321 Sep 25 00:20 torchfrtrace\n-r-xr-xr-x 1 root root 312 Sep 25 00:20 torchrun\n-r-xr-xr-x 1 root root 299 Sep 25 00:20 tqdm\n2.3.3 2.8.0+cpu 18.1.0 2.2.6\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:25:47.570Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf88430887d2bfc96d82e6ac88ff", "usage": { "input_tokens": 20686, "cached_input_tokens": 19840, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 16, "total_tokens": 20829 }, "turn_token_usage": { "input_tokens": 93981, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1070, "reasoning_output_tokens": 229, "total_tokens": 95051 }, "thread_token_usage": { "input_tokens": 93981, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1070, "reasoning_output_tokens": 229, "total_tokens": 95051 } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:51.417Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-075a3cfd-763d-45b7-8eae-bb6be237b1c3", "process_id": "96507", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 745136212 }, "formatted_output": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T00:25:56.115Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:56.115Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:26:01.389Z", "callId": "call_KHn0SxCTTPIc4J0rbjDUgTQA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas,torch\\nprint(pandas.__version__, torch.__version__)\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\\nprint('\\\\nNPZ')\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=mask.sum(1)\\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:26:07.857Z", "content": "Script completed\nWall time 6.5 seconds\nOutput:\n\n2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:01.478Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf91914887d2ad8e7d574b371c03", "usage": { "input_tokens": 21883, "cached_input_tokens": 20480, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 67, "total_tokens": 22251 }, "turn_token_usage": { "input_tokens": 115864, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 1438, "reasoning_output_tokens": 296, "total_tokens": 117302 }, "thread_token_usage": { "input_tokens": 115864, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 1438, "reasoning_output_tokens": 296, "total_tokens": 117302 } } }, { "kind": "context", "timestamp": "2026-09-25T00:26:07.847Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-607e85c2-0f2a-4436-80a1-1b2ca909583f", "process_id": "18288", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch\nprint(pandas.__version__, torch.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch\nprint(pandas.__version__, torch.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 293138061 }, "formatted_output": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T00:26:13.911Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:13.911Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:26:19.265Z", "callId": "call_68E5rZTmtJsQTd7lhxcp9BqN", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\ntar=pd.read_parquet(D/'target_users.parquet')\\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\\n del x\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:26:22.379Z", "content": "Script completed\nWall time 3.1 seconds\nOutput:\n\n/bin/sh: 1: free: not found\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\noverlay 96G 15G 82G 16% /\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:19.314Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfa0397487d280fdd547c06aa6e4", "usage": { "input_tokens": 23052, "cached_input_tokens": 21760, "cache_write_input_tokens": 0, "output_tokens": 553, "reasoning_output_tokens": 247, "total_tokens": 23605 }, "turn_token_usage": { "input_tokens": 138916, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 543, "total_tokens": 140907 }, "thread_token_usage": { "input_tokens": 138916, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 543, "total_tokens": 140907 } } }, { "kind": "context", "timestamp": "2026-09-25T00:26:22.376Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-efd0d06d-6ad5-4962-ab1f-d9678a99132f", "process_id": "34958", "command": [ "/bin/sh", "-lc", "free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ntar=pd.read_parquet(D/'target_users.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\n del x\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ntar=pd.read_parquet(D/'target_users.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\n del x\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n", "stderr": "", "aggregated_output": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 946130479 }, "formatted_output": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T00:26:34.580Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:34.580Z", "text": "" } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T00:26:40.640Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:40.640Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:26:54.911Z", "callId": "call_vkWXmQkoqNGptVO1evDsX3mu", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time, resource\\nfrom pathlib import Path\\nD=Path('/task/data')\\nt0=time.time()\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\\n# truths\\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\\ntruth['catalog']=cols>=0; truth['eligible']=elig\\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\\net=truth[truth.eligible]\\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\\nprint('loading train pairs')\\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\\nh=h[h.uid.isin(target)].drop_duplicates()\\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\\n# compare eligibility exclusion to history for sample\\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\\nvals=[]\\nfor u in ss:\\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:27:16.321Z", "content": "Script completed\nWall time 21.4 seconds\nOutput:\n\ntruth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:54.947Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfaeb82487d28172dd0b4510c04d", "usage": { "input_tokens": 24326, "cached_input_tokens": 22912, "cache_write_input_tokens": 0, "output_tokens": 1756, "reasoning_output_tokens": 769, "total_tokens": 26082 }, "turn_token_usage": { "input_tokens": 163242, "cached_input_tokens": 148736, "cache_write_input_tokens": 0, "output_tokens": 3747, "reasoning_output_tokens": 1312, "total_tokens": 166989 }, "thread_token_usage": { "input_tokens": 163242, "cached_input_tokens": 148736, "cache_write_input_tokens": 0, "output_tokens": 3747, "reasoning_output_tokens": 1312, "total_tokens": 166989 } } }, { "kind": "context", "timestamp": "2026-09-25T00:27:16.317Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e366be08-eef8-4bc7-b72a-646bfeb24b37", "process_id": "77318", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data')\nt0=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\n# truths\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\ntruth['catalog']=cols>=0; truth['eligible']=elig\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\net=truth[truth.eligible]\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\nprint('loading train pairs')\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nh=h[h.uid.isin(target)].drop_duplicates()\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\n# compare eligibility exclusion to history for sample\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\nvals=[]\nfor u in ss:\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data')\nt0=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\n# truths\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\ntruth['catalog']=cols>=0; truth['eligible']=elig\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\net=truth[truth.eligible]\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\nprint('loading train pairs')\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nh=h[h.uid.isin(target)].drop_duplicates()\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\n# compare eligibility exclusion to history for sample\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\nvals=[]\nfor u in ss:\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n", "stderr": "", "aggregated_output": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 241610421 }, "formatted_output": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T00:27:26.509Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:27:26.509Z", "text": "" } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-25T00:27:31.845Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:27:31.845Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:27:33.087Z", "text": "Eligibility is essentially “catalog items never previously listened to,” which makes the task true discovery rather than replay prediction. Public labels are sparse (4,263 scored users), so I’m establishing popularity and recency baselines first, then the neural model will be trained on many rolling seven-day pseudo-futures from the training period." }, { "kind": "tool", "timestamp": "2026-09-25T00:27:40.662Z", "callId": "call_dJdeYxJs1PkGOQRdAWgsr0V8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\\n# variants intrinsic catalog columns\\norders={\\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\\n}\\nfor name,order in orders.items():\\n out=[]\\n for r,u in enumerate(t):\\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\\n take=order[mask[order]][:100]\\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\\n print(p)\\nPY\\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:10.840Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:27:40.727Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfe4978c87d2978d0898d509ef96", "usage": { "input_tokens": 26547, "cached_input_tokens": 24192, "cache_write_input_tokens": 0, "output_tokens": 1305, "reasoning_output_tokens": 802, "total_tokens": 27852 }, "turn_token_usage": { "input_tokens": 189789, "cached_input_tokens": 172928, "cache_write_input_tokens": 0, "output_tokens": 5052, "reasoning_output_tokens": 2114, "total_tokens": 194841 }, "thread_token_usage": { "input_tokens": 189789, "cached_input_tokens": 172928, "cache_write_input_tokens": 0, "output_tokens": 5052, "reasoning_output_tokens": 2114, "total_tokens": 194841 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-25T00:28:13.407Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:13.407Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:28:14.396Z", "callId": "call_nC5R3VFWjPOIUYXBfwoE5tc9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution; ps aux | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:14.467Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ntotal 0\n/bin/sh: 1: ps: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:14.438Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3b2470f-e505-45d1-815c-a43028b39641", "process_id": "80387", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution; ps aux | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution; ps aux | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 0\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "total 0\n/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3541 }, "formatted_output": "total 0\n/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:28:14.466Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c01b1b0487d2b4cfdd46f1acc50a", "usage": { "input_tokens": 27875, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 73, "total_tokens": 28009 }, "turn_token_usage": { "input_tokens": 217664, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 5186, "reasoning_output_tokens": 2187, "total_tokens": 222850 }, "thread_token_usage": { "input_tokens": 217664, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 5186, "reasoning_output_tokens": 2187, "total_tokens": 222850 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-25T00:28:18.823Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:18.823Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:28:25.545Z", "callId": "call_w927YK6ndPY4z4tr8JYyCho0", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np,time\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution')\\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:28:26.710Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=98244", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:25.596Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c01ed36887d2994d421fc029e9ed", "usage": { "input_tokens": 28047, "cached_input_tokens": 27648, "cache_write_input_tokens": 0, "output_tokens": 554, "reasoning_output_tokens": 170, "total_tokens": 28601 }, "turn_token_usage": { "input_tokens": 245711, "cached_input_tokens": 226944, "cache_write_input_tokens": 0, "output_tokens": 5740, "reasoning_output_tokens": 2357, "total_tokens": 251451 }, "thread_token_usage": { "input_tokens": 245711, "cached_input_tokens": 226944, "cache_write_input_tokens": 0, "output_tokens": 5740, "reasoning_output_tokens": 2357, "total_tokens": 251451 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-25T00:28:28.307Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:28.307Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:28:29.103Z", "callId": "call_WZBOlLaBRV5YNOFECI6SwmpK", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":98244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:45.525Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\nload\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:29.229Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c02b0b6087d2b9dca4bf12d99f1b", "usage": { "input_tokens": 28629, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 28683 }, "turn_token_usage": { "input_tokens": 274340, "cached_input_tokens": 254848, "cache_write_input_tokens": 0, "output_tokens": 5794, "reasoning_output_tokens": 2364, "total_tokens": 280134 }, "thread_token_usage": { "input_tokens": 274340, "cached_input_tokens": 254848, "cache_write_input_tokens": 0, "output_tokens": 5794, "reasoning_output_tokens": 2364, "total_tokens": 280134 } } }, { "kind": "context", "timestamp": "2026-09-25T00:28:45.523Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d121910f-2b29-4908-8230-4d08c7ea493d", "process_id": "98244", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n", "stderr": "", "aggregated_output": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n", "exit_code": 0, "duration": { "secs": 19, "nanos": 814245128 }, "formatted_output": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n" } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T00:28:46.905Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:46.905Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:28:48.922Z", "callId": "call_W9UCvA8OtZdOiJy6NWYsN7TD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:28:50.087Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=13115", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:48.993Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c03dc94487d290194c55f966ef4b", "usage": { "input_tokens": 28750, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 139, "reasoning_output_tokens": 18, "total_tokens": 28889 }, "turn_token_usage": { "input_tokens": 303090, "cached_input_tokens": 283264, "cache_write_input_tokens": 0, "output_tokens": 5933, "reasoning_output_tokens": 2382, "total_tokens": 309023 }, "thread_token_usage": { "input_tokens": 303090, "cached_input_tokens": 283264, "cache_write_input_tokens": 0, "output_tokens": 5933, "reasoning_output_tokens": 2382, "total_tokens": 309023 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:28:51.651Z", "callId": "call_wteWCH0IcBSVOXsw1VAM06X2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":13115,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:58.857Z", "content": "Script completed\nWall time 7.2 seconds\nOutput:\n\n{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:51.722Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c042493087d294f8163864b4e8e5", "usage": { "input_tokens": 28917, "cached_input_tokens": 28544, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 28962 }, "turn_token_usage": { "input_tokens": 332007, "cached_input_tokens": 311808, "cache_write_input_tokens": 0, "output_tokens": 5978, "reasoning_output_tokens": 2382, "total_tokens": 337985 }, "thread_token_usage": { "input_tokens": 332007, "cached_input_tokens": 311808, "cache_write_input_tokens": 0, "output_tokens": 5978, "reasoning_output_tokens": 2382, "total_tokens": 337985 } } }, { "kind": "context", "timestamp": "2026-09-25T00:28:58.855Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-26a8dec0-0a25-45b0-bd5d-3eadcd1e5167", "process_id": "13115", "command": [ "/bin/sh", "-lc", "for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 769024769 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T00:29:03.217Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:03.217Z", "text": "" } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T00:29:10.332Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:10.332Z", "text": "" } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T00:29:15.794Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:15.794Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:29:22.169Z", "callId": "call_fX7X9dMRLPTKo8nfuNjKLimC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); C=24786800\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\\n# correct intrinsic correlations and recent global candidate counters\\nfor family in ['listens','likes']:\\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\\n ci=idx.get_indexer(x.item_id)\\n valid=ci>=0\\n for days in [7,14,30,60,90,180,365]:\\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\\n cnt=np.bincount(ci[m],minlength=len(ids))\\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\\n print(family,len(x))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:29:23.335Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n8\r\nMemTotal: 15988972 kB\r\nMemAvailable: 14883732 kB\r\n\nSESSION_ID=49448", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:22.202Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c04b0c8c87d2a906da433f25bc6c", "usage": { "input_tokens": 29129, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 1247, "reasoning_output_tokens": 886, "total_tokens": 30376 }, "turn_token_usage": { "input_tokens": 361136, "cached_input_tokens": 340608, "cache_write_input_tokens": 0, "output_tokens": 7225, "reasoning_output_tokens": 3268, "total_tokens": 368361 }, "thread_token_usage": { "input_tokens": 361136, "cached_input_tokens": 340608, "cache_write_input_tokens": 0, "output_tokens": 7225, "reasoning_output_tokens": 3268, "total_tokens": 368361 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-25T00:29:24.623Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:24.623Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T00:29:25.302Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-80db3804-51f1-4264-9bd6-0d97c8e018f9", "process_id": "49448", "command": [ "/bin/sh", "-lc", "getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\n# correct intrinsic correlations and recent global candidate counters\nfor family in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\n ci=idx.get_indexer(x.item_id)\n valid=ci>=0\n for days in [7,14,30,60,90,180,365]:\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\n cnt=np.bincount(ci[m],minlength=len(ids))\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\n print(family,len(x))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\n# correct intrinsic correlations and recent global candidate counters\nfor family in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\n ci=idx.get_indexer(x.item_id)\n valid=ci>=0\n for days in [7,14,30,60,90,180,365]:\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\n cnt=np.bincount(ci[m],minlength=len(ids))\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\n print(family,len(x))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 43484159\r\nlikes 814831\r\n", "stderr": "", "aggregated_output": "listens 43484159\r\nlikes 814831\r\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 968350610 }, "formatted_output": "listens 43484159\r\nlikes 814831\r\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T00:29:25.304Z", "callId": "call_uVW7c8q5aqReVCDg2bAQAvyT", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":49448,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:29:25.378Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nlistens 43484159\r\nlikes 814831\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:25.376Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c06384dc87d2996306393291971a", "usage": { "input_tokens": 30428, "cached_input_tokens": 28928, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 30485 }, "turn_token_usage": { "input_tokens": 391564, "cached_input_tokens": 369536, "cache_write_input_tokens": 0, "output_tokens": 7282, "reasoning_output_tokens": 3278, "total_tokens": 398846 }, "thread_token_usage": { "input_tokens": 391564, "cached_input_tokens": 369536, "cache_write_input_tokens": 0, "output_tokens": 7282, "reasoning_output_tokens": 3278, "total_tokens": 398846 } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-25T00:29:29.408Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:29.408Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:29:39.788Z", "callId": "call_EvY5uutnH7xkvXDUyKsf0nLL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,pandas as pd,numpy as np\\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\\n# score order against elig bits, top100 placeholder\\ndef ev(s):\\n order=np.argsort(-s,kind='stable'); ranked={}\\n for r,u in enumerate(cs.uids):\\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\\n return score_rankings(ranked,truth)['quality_ndcg@10']\\nvals={'pop':pop,'opop':opop}\\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\\nfor n,s in vals.items(): print(n,ev(s))\\nprint('blends')\\n# normalize ranks or z log counts combinations\\nfor d in [7,14,30,60,90,180]:\\n for a in [0,.1,.25,.5,1,2,4]:\\n # log recent rate with long prior\\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\\n print('d a',d,a,ev(s))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:29:40.954Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=39005", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:39.888Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c065bda487d2942bf76175234bed", "usage": { "input_tokens": 30520, "cached_input_tokens": 30208, "cache_write_input_tokens": 0, "output_tokens": 744, "reasoning_output_tokens": 160, "total_tokens": 31264 }, "turn_token_usage": { "input_tokens": 422084, "cached_input_tokens": 399744, "cache_write_input_tokens": 0, "output_tokens": 8026, "reasoning_output_tokens": 3438, "total_tokens": 430110 }, "thread_token_usage": { "input_tokens": 422084, "cached_input_tokens": 399744, "cache_write_input_tokens": 0, "output_tokens": 8026, "reasoning_output_tokens": 3438, "total_tokens": 430110 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-25T00:29:41.967Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:41.967Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:29:42.649Z", "callId": "call_zeCUzApJvcNpxcjTl3bACCPq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":39005,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:30:12.657Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\npop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:42.719Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c07520c487d2aa097a48618783ca", "usage": { "input_tokens": 31292, "cached_input_tokens": 30336, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 31345 }, "turn_token_usage": { "input_tokens": 453376, "cached_input_tokens": 430080, "cache_write_input_tokens": 0, "output_tokens": 8079, "reasoning_output_tokens": 3444, "total_tokens": 461455 }, "thread_token_usage": { "input_tokens": 453376, "cached_input_tokens": 430080, "cache_write_input_tokens": 0, "output_tokens": 8079, "reasoning_output_tokens": 3444, "total_tokens": 461455 } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-25T00:30:15.012Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:15.012Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:15.708Z", "callId": "call_PLqt3sFYm6PJgOLnAYIXOTtC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":39005,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:30:22.248Z", "content": "Script completed\nWall time 6.5 seconds\nOutput:\n\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:15.797Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c094e20087d2a2ec95af37d9331b", "usage": { "input_tokens": 31888, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 124, "reasoning_output_tokens": 77, "total_tokens": 32012 }, "turn_token_usage": { "input_tokens": 485264, "cached_input_tokens": 461184, "cache_write_input_tokens": 0, "output_tokens": 8203, "reasoning_output_tokens": 3521, "total_tokens": 493467 }, "thread_token_usage": { "input_tokens": 485264, "cached_input_tokens": 461184, "cache_write_input_tokens": 0, "output_tokens": 8203, "reasoning_output_tokens": 3521, "total_tokens": 493467 } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:22.246Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a69f1448-5365-4f8e-8d42-2a66106c3aaf", "process_id": "39005", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\n# score order against elig bits, top100 placeholder\ndef ev(s):\n order=np.argsort(-s,kind='stable'); ranked={}\n for r,u in enumerate(cs.uids):\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\n return score_rankings(ranked,truth)['quality_ndcg@10']\nvals={'pop':pop,'opop':opop}\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\nfor n,s in vals.items(): print(n,ev(s))\nprint('blends')\n# normalize ranks or z log counts combinations\nfor d in [7,14,30,60,90,180]:\n for a in [0,.1,.25,.5,1,2,4]:\n # log recent rate with long prior\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\n print('d a',d,a,ev(s))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\n# score order against elig bits, top100 placeholder\ndef ev(s):\n order=np.argsort(-s,kind='stable'); ranked={}\n for r,u in enumerate(cs.uids):\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\n return score_rankings(ranked,truth)['quality_ndcg@10']\nvals={'pop':pop,'opop':opop}\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\nfor n,s in vals.items(): print(n,ev(s))\nprint('blends')\n# normalize ranks or z log counts combinations\nfor d in [7,14,30,60,90,180]:\n for a in [0,.1,.25,.5,1,2,4]:\n # log recent rate with long prior\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\n print('d a',d,a,ev(s))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n", "stderr": "", "aggregated_output": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n", "exit_code": 0, "duration": { "secs": 42, "nanos": 293739801 }, "formatted_output": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n" } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-25T00:30:32.506Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:32.506Z", "text": "" } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T00:30:41.848Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:41.848Z", "text": "" } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-25T00:30:49.468Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:49.468Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:30:50.974Z", "text": "The strongest non-personalized signal is sharply time-local: seven-day organic likes/strong listens score about 0.029–0.031 versus 0.0107 for lifetime popularity. I’ll retain that as an item-tower trend feature and train the personalized dimensions on cutoff-aligned rolling snapshots, so the deployed score remains one exact dot product." }, { "kind": "tool", "timestamp": "2026-09-25T00:30:54.849Z", "callId": "call_RY9aCKLXrGfO5Fd23qE6Ltg2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:30:56.016Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=71661", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:54.915Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c09e797c87d2ad634fdc1e60fb99", "usage": { "input_tokens": 32239, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 1760, "reasoning_output_tokens": 1457, "total_tokens": 33999 }, "turn_token_usage": { "input_tokens": 517503, "cached_input_tokens": 492928, "cache_write_input_tokens": 0, "output_tokens": 9963, "reasoning_output_tokens": 4978, "total_tokens": 527466 }, "thread_token_usage": { "input_tokens": 517503, "cached_input_tokens": 492928, "cache_write_input_tokens": 0, "output_tokens": 9963, "reasoning_output_tokens": 4978, "total_tokens": 527466 } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:56.959Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ffc87bd-66c8-42ef-a3b7-542f429fc108", "process_id": "71661", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n", "stderr": "", "aggregated_output": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 945515223 }, "formatted_output": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T00:30:59.655Z", "callId": "call_R4Mh1ow6l1xJCUlncsXPfNTu", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71661,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:30:59.727Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nlistens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:59.726Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c0c0356087d2885cc2fdf9b2b32c", "usage": { "input_tokens": 34027, "cached_input_tokens": 32000, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 34072 }, "turn_token_usage": { "input_tokens": 551530, "cached_input_tokens": 524928, "cache_write_input_tokens": 0, "output_tokens": 10008, "reasoning_output_tokens": 4978, "total_tokens": 561538 }, "thread_token_usage": { "input_tokens": 551530, "cached_input_tokens": 524928, "cache_write_input_tokens": 0, "output_tokens": 10008, "reasoning_output_tokens": 4978, "total_tokens": 561538 } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-25T00:31:10.161Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:10.161Z", "text": "" } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-25T00:31:19.484Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:19.484Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-25T00:31:28.811Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:28.811Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-25T00:31:38.132Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:38.132Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-25T00:31:47.464Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:47.464Z", "text": "" } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-25T00:31:56.771Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:56.771Z", "text": "" } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-25T00:32:04.295Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:04.295Z", "text": "" } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-25T00:32:13.592Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:13.592Z", "text": "" } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-25T00:32:22.906Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:22.906Z", "text": "" } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-25T00:32:32.336Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:32.336Z", "text": "" } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-25T00:32:37.749Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:37.749Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:35:18.021Z", "callId": "call_mdPCpd6cxE8ZYq0usAyzg1BZ", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a history-only neural two-tower discovery ranker.\\n+\\n+The script intentionally works from event records. It never constructs a\\n+user-by-item matrix: rolling examples are histories followed by a future\\n+organic discovery, and training uses in-batch sampled-softmax negatives.\\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 dataclass\\n+from pathlib import Path\\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+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+VALIDATION_CUTOFF = 25_393_400\\n+GAP = 1_800\\n+WINDOW = 7 * 24 * 60 * 60\\n+HISTORY_LEN = 160\\n+LIKED_LEN = 80\\n+DISLIKED_LEN = 32\\n+TARGET_LEN = 64\\n+PAD = 0\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+def catalog_index(values: np.ndarray, catalog_ids: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Map raw IDs to catalog rows; returns -1 outside the retrieval catalog.\\\"\\\"\\\"\\n+ return pd.Index(catalog_ids).get_indexer(values).astype(np.int32, copy=False)\\n+\\n+\\n+def load_family(stage: str, family: str, columns: list[str]) -> pd.DataFrame:\\n+ parts = [pd.read_parquet(DATA / f\\\"train_{family}.parquet\\\", columns=columns)]\\n+ if stage == \\\"final\\\":\\n+ parts.append(pd.read_parquet(DATA / f\\\"val_{family}.parquet\\\", columns=columns))\\n+ frame = pd.concat(parts, ignore_index=True)\\n+ # Each source is uid/time sorted, but concatenating the two windows is not.\\n+ if stage == \\\"final\\\":\\n+ frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ frame.reset_index(drop=True, inplace=True)\\n+ return frame\\n+\\n+\\n+def filter_catalog(frame: pd.DataFrame, catalog_ids: np.ndarray) -> pd.DataFrame:\\n+ mapped = catalog_index(frame.item_id.to_numpy(), catalog_ids)\\n+ keep = mapped >= 0\\n+ frame = frame.loc[keep].copy()\\n+ frame[\\\"ci\\\"] = mapped[keep]\\n+ return frame\\n+\\n+\\n+def group_bounds(sorted_uids: np.ndarray) -> dict[int, tuple[int, int]]:\\n+ if not len(sorted_uids):\\n+ return {}\\n+ starts = np.r_[0, np.flatnonzero(sorted_uids[1:] != sorted_uids[:-1]) + 1]\\n+ ends = np.r_[starts[1:], len(sorted_uids)]\\n+ return {int(sorted_uids[s]): (int(s), int(e)) for s, e in zip(starts, ends)}\\n+\\n+\\n+@dataclass\\n+class SnapshotArrays:\\n+ history: np.ndarray\\n+ history_weight: np.ndarray\\n+ liked: np.ndarray\\n+ disliked: np.ndarray\\n+ user_features: np.ndarray\\n+ targets: np.ndarray\\n+ grades: np.ndarray\\n+ cutoff_id: np.ndarray\\n+\\n+ def save(self, path: Path) -> None:\\n+ np.savez_compressed(\\n+ path,\\n+ history=self.history,\\n+ history_weight=self.history_weight,\\n+ liked=self.liked,\\n+ disliked=self.disliked,\\n+ user_features=self.user_features,\\n+ targets=self.targets,\\n+ grades=self.grades,\\n+ cutoff_id=self.cutoff_id,\\n+ )\\n+\\n+ @classmethod\\n+ def load(cls, path: Path) -> \\\"SnapshotArrays\\\":\\n+ with np.load(path) as z:\\n+ return cls(**{k: z[k] for k in z.files})\\n+\\n+\\n+def _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\\n+ out = np.zeros(length, dtype=np.uint16)\\n+ if len(items):\\n+ take = np.asarray(items[-length:], dtype=np.int64) + 1\\n+ out[-len(take) :] = take.astype(np.uint16)\\n+ return out\\n+\\n+\\n+def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\\n+ \\\"\\\"\\\"Create cutoff-correct rolling history -> organic-discovery examples.\\\"\\\"\\\"\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ latest = cutoff - GAP - WINDOW\\n+ cutoffs = np.arange(latest - (weeks - 1) * WINDOW, latest + 1, WINDOW, dtype=np.int64)\\n+ cutoffs = cutoffs[cutoffs > WINDOW]\\n+ print(f\\\"loading {stage} events for {len(cutoffs)} rolling cutoffs\\\", flush=True)\\n+\\n+ listens = load_family(\\n+ stage,\\n+ \\\"listens\\\",\\n+ [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ listens = filter_catalog(listens, catalog_ids)\\n+ # Compact dtypes matter for the 30M+ retained catalog events.\\n+ lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ li = listens.ci.to_numpy(dtype=np.int32, copy=False)\\n+ lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\\n+ lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\\n+ lb = group_bounds(lu)\\n+\\n+ action_parts: list[pd.DataFrame] = []\\n+ specs = [\\n+ (\\\"likes\\\", 0, 1),\\n+ (\\\"unlikes\\\", 0, 0),\\n+ (\\\"dislikes\\\", 1, 1),\\n+ (\\\"undislikes\\\", 1, 0),\\n+ ]\\n+ organic_likes: pd.DataFrame | None = None\\n+ for family, channel, state in specs:\\n+ a = load_family(stage, family, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ a = filter_catalog(a, catalog_ids)\\n+ if family == \\\"likes\\\":\\n+ organic_likes = a.loc[a.is_organic == 1, [\\\"uid\\\", \\\"timestamp\\\", \\\"ci\\\"]].copy()\\n+ a = a[[\\\"uid\\\", \\\"timestamp\\\", \\\"ci\\\"]].copy()\\n+ a[\\\"channel\\\"] = np.uint8(channel)\\n+ a[\\\"state\\\"] = np.uint8(state)\\n+ action_parts.append(a)\\n+ actions = pd.concat(action_parts, ignore_index=True)\\n+ actions.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ au = actions.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ at = actions.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ ai = actions.ci.to_numpy(dtype=np.int32, copy=False)\\n+ ac = actions.channel.to_numpy(dtype=np.uint8, copy=False)\\n+ ast = actions.state.to_numpy(dtype=np.uint8, copy=False)\\n+ ab = group_bounds(au)\\n+ assert organic_likes is not None\\n+ organic_likes.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ ou = organic_likes.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ ot = organic_likes.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ oi = organic_likes.ci.to_numpy(dtype=np.int32, copy=False)\\n+ ob = group_bounds(ou)\\n+\\n+ all_users = np.union1d(np.unique(lu), np.unique(au))\\n+ histories: list[np.ndarray] = []\\n+ history_weights: list[np.ndarray] = []\\n+ liked_rows: list[np.ndarray] = []\\n+ disliked_rows: list[np.ndarray] = []\\n+ feature_rows: list[np.ndarray] = []\\n+ target_rows: list[np.ndarray] = []\\n+ grade_rows: list[np.ndarray] = []\\n+ cutoff_rows: list[int] = []\\n+ inf_time = np.iinfo(np.int64).max\\n+\\n+ for user_no, uid in enumerate(all_users):\\n+ if int(uid) not in lb:\\n+ continue\\n+ s, e = lb[int(uid)]\\n+ uts, uitems, uorg, uratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ positive = uratio >= 50\\n+ pts = uts[positive]\\n+ pitems = uitems[positive]\\n+ porg = uorg[positive]\\n+ pratio = uratio[positive]\\n+ first_time = np.full(len(catalog_ids), inf_time, dtype=np.int64)\\n+ np.minimum.at(first_time, uitems, uts)\\n+\\n+ if int(uid) in ab:\\n+ a0, a1 = ab[int(uid)]\\n+ uat, uai, uac, uas = at[a0:a1], ai[a0:a1], ac[a0:a1], ast[a0:a1]\\n+ else:\\n+ uat = np.empty(0, np.int64)\\n+ uai = np.empty(0, np.int32)\\n+ uac = np.empty(0, np.uint8)\\n+ uas = np.empty(0, np.uint8)\\n+ if int(uid) in ob:\\n+ o0, o1 = ob[int(uid)]\\n+ uot, uoi = ot[o0:o1], oi[o0:o1]\\n+ else:\\n+ uot = np.empty(0, np.int64)\\n+ uoi = np.empty(0, np.int32)\\n+\\n+ liked_state: dict[int, int] = {}\\n+ disliked_state: dict[int, int] = {}\\n+ action_ptr = 0\\n+ for cutoff_no, c in enumerate(cutoffs):\\n+ while action_ptr < len(uat) and uat[action_ptr] < c:\\n+ item = int(uai[action_ptr])\\n+ state_map = liked_state if uac[action_ptr] == 0 else disliked_state\\n+ if uas[action_ptr]:\\n+ state_map[item] = int(uat[action_ptr])\\n+ else:\\n+ state_map.pop(item, None)\\n+ action_ptr += 1\\n+\\n+ hp = int(np.searchsorted(pts, c, side=\\\"left\\\"))\\n+ if hp < 10:\\n+ continue\\n+ hs = max(0, hp - HISTORY_LEN)\\n+ hist_i = pitems[hs:hp]\\n+ hist_t = pts[hs:hp]\\n+ # Smoothly favor recent, organic and deeply completed history.\\n+ raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\\n+ raw_w *= 1.0 + 0.25 * porg[hs:hp]\\n+ raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\\n+ raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\\n+ hist = np.zeros(HISTORY_LEN, dtype=np.uint16)\\n+ hw = np.zeros(HISTORY_LEN, dtype=np.float16)\\n+ hist[-len(hist_i) :] = (hist_i + 1).astype(np.uint16)\\n+ hw[-len(hist_i) :] = raw_w.astype(np.float16)\\n+\\n+ f0 = int(np.searchsorted(uts, c + GAP, side=\\\"left\\\"))\\n+ f1 = int(np.searchsorted(uts, c + GAP + WINDOW, side=\\\"left\\\"))\\n+ future_mask = (uorg[f0:f1] == 1) & (uratio[f0:f1] >= 80)\\n+ future_items = uitems[f0:f1][future_mask]\\n+ future_items = future_items[first_time[future_items] >= c]\\n+ grades: dict[int, int] = {int(x): 1 for x in future_items}\\n+ q0 = int(np.searchsorted(uot, c + GAP, side=\\\"left\\\"))\\n+ q1 = int(np.searchsorted(uot, c + GAP + WINDOW, side=\\\"left\\\"))\\n+ for item in uoi[q0:q1]:\\n+ if first_time[item] >= c:\\n+ grades[int(item)] = 3\\n+ if not grades:\\n+ continue\\n+\\n+ # Keep all high-value likes, then deterministic listens if unusually busy.\\n+ ordered_targets = sorted(grades, key=lambda x: (-grades[x], first_time[x], x))[:TARGET_LEN]\\n+ tr = np.zeros(TARGET_LEN, dtype=np.uint16)\\n+ gr = np.zeros(TARGET_LEN, dtype=np.uint8)\\n+ tr[: len(ordered_targets)] = np.asarray(ordered_targets, dtype=np.uint16) + 1\\n+ gr[: len(ordered_targets)] = np.asarray([grades[x] for x in ordered_targets], dtype=np.uint8)\\n+\\n+ liked_recent = [x for x, _ in sorted(liked_state.items(), key=lambda z: z[1])]\\n+ disliked_recent = [x for x, _ in sorted(disliked_state.items(), key=lambda z: z[1])]\\n+ lf = math.log1p(hp) / 10.0\\n+ recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\\n+ features = np.asarray(\\n+ [lf, recent_org, math.log1p(len(liked_state)) / 5.0, math.log1p(len(disliked_state)) / 3.0],\\n+ dtype=np.float32,\\n+ )\\n+ histories.append(hist)\\n+ history_weights.append(hw)\\n+ liked_rows.append(_pad_recent(liked_recent, LIKED_LEN))\\n+ disliked_rows.append(_pad_recent(disliked_recent, DISLIKED_LEN))\\n+ feature_rows.append(features)\\n+ target_rows.append(tr)\\n+ grade_rows.append(gr)\\n+ cutoff_rows.append(cutoff_no)\\n+ if user_no and user_no % 2000 == 0:\\n+ print(f\\\" users {user_no}/{len(all_users)} examples={len(histories)}\\\", flush=True)\\n+\\n+ del listens, actions, action_parts\\n+ result = SnapshotArrays(\\n+ history=np.stack(histories),\\n+ history_weight=np.stack(history_weights),\\n+ liked=np.stack(liked_rows),\\n+ disliked=np.stack(disliked_rows),\\n+ user_features=np.stack(feature_rows),\\n+ targets=np.stack(target_rows),\\n+ grades=np.stack(grade_rows),\\n+ cutoff_id=np.asarray(cutoff_rows, dtype=np.uint8),\\n+ )\\n+ print(\\n+ f\\\"built {len(result.history):,} examples, \\\"\\n+ f\\\"{np.count_nonzero(result.targets):,} relevant pairs\\\",\\n+ flush=True,\\n+ )\\n+ return result\\n+\\n+\\n+def item_features(catalog: pd.DataFrame) -> np.ndarray:\\n+ pos = catalog.positive_listens.to_numpy(dtype=np.float64)\\n+ org = catalog.organic_positive_listens.to_numpy(dtype=np.float64)\\n+ x = np.column_stack(\\n+ [\\n+ np.log1p(pos),\\n+ np.log1p(org),\\n+ (org + 5.0) / (pos + 10.0),\\n+ np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float64)),\\n+ ]\\n+ )\\n+ return ((x - x.mean(0)) / (x.std(0) + 1e-6)).astype(np.float32)\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.listen_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.like_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.dislike_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(3 * dim + 4, 2 * dim),\\n+ nn.GELU(),\\n+ nn.LayerNorm(2 * dim),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+\\n+ def forward(\\n+ self,\\n+ history: torch.Tensor,\\n+ history_weight: torch.Tensor,\\n+ liked: torch.Tensor,\\n+ disliked: torch.Tensor,\\n+ features: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ h = (self.listen_embedding(history) * history_weight.unsqueeze(-1)).sum(1)\\n+ lm = liked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\\n+ dm = disliked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\\n+ lp = self.like_embedding(liked).sum(1) / lm\\n+ dp = self.dislike_embedding(disliked).sum(1) / dm\\n+ return F.normalize(self.network(torch.cat([h, lp, dp, features], dim=1)), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, feature_dim: int, dim: int):\\n+ super().__init__()\\n+ self.id_embedding = nn.Embedding(n_items, dim)\\n+ self.feature_network = nn.Sequential(nn.Linear(feature_dim, dim), nn.GELU(), nn.Linear(dim, dim))\\n+ self.network = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim), nn.GELU(), nn.Linear(dim, dim))\\n+\\n+ def forward(self, item: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ base = self.id_embedding(item) + self.feature_network(features)\\n+ return F.normalize(base + self.network(base), dim=1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, feature_dim: int, dim: int):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, feature_dim, dim)\\n+ self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+\\n+def train_model(\\n+ snapshots: SnapshotArrays,\\n+ features: np.ndarray,\\n+ dim: int,\\n+ epochs: int,\\n+ seed: int,\\n+) -> TwoTower:\\n+ seed_everything(seed)\\n+ torch.set_num_threads(min(8, torch.get_num_threads()))\\n+ model = TwoTower(len(features), features.shape[1], dim)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ feat_t = torch.from_numpy(features)\\n+ n = len(snapshots.history)\\n+ batch_size = 384\\n+ rng = np.random.default_rng(seed)\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(n)\\n+ losses: list[float] = []\\n+ for start in range(0, n - batch_size + 1, batch_size):\\n+ ix = order[start : start + batch_size]\\n+ target = np.empty(batch_size, dtype=np.int64)\\n+ for j, row in enumerate(ix):\\n+ valid = np.flatnonzero(snapshots.targets[row])\\n+ grade = snapshots.grades[row, valid].astype(np.float64)\\n+ # Likes matter more, without letting the rare grade-3 channel dominate.\\n+ probability = np.where(grade == 3, 3.5, 1.0)\\n+ k = rng.choice(valid, p=probability / probability.sum())\\n+ target[j] = int(snapshots.targets[row, k]) - 1\\n+ h = torch.from_numpy(snapshots.history[ix].astype(np.int64))\\n+ hw = torch.from_numpy(snapshots.history_weight[ix].astype(np.float32))\\n+ liked = torch.from_numpy(snapshots.liked[ix].astype(np.int64))\\n+ disliked = torch.from_numpy(snapshots.disliked[ix].astype(np.int64))\\n+ uf = torch.from_numpy(snapshots.user_features[ix])\\n+ ti = torch.from_numpy(target)\\n+ u = model.user_tower(h, hw, liked, disliked, uf)\\n+ v = model.item_tower(ti, feat_t[ti])\\n+ logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\\n+ # A duplicated positive in the batch is another positive, not a negative.\\n+ same = ti[:, None].eq(ti[None, :])\\n+ same.fill_diagonal_(False)\\n+ logits = logits.masked_fill(same, -1e4)\\n+ loss = F.cross_entropy(logits, torch.arange(batch_size))\\n+ opt.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ opt.step()\\n+ losses.append(float(loss.detach()))\\n+ print(\\n+ f\\\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.4f} \\\"\\n+ f\\\"scale={model.logit_scale.exp().item():.2f}\\\",\\n+ flush=True,\\n+ )\\n+ return model.eval()\\n+\\n+\\n+def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ listens = load_family(\\n+ stage,\\n+ \\\"listens\\\",\\n+ [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ listens = filter_catalog(listens, catalog_ids)\\n+ lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ li = listens.ci.to_numpy(dtype=np.int32, copy=False)\\n+ lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\\n+ lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\\n+ bounds = group_bounds(lu)\\n+ pref_name = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pref = filter_catalog(pref, catalog_ids)\\n+ pref_group = {(int(u), str(state)): g.ci.to_numpy(dtype=np.int32) for (u, state), g in pref.groupby([\\\"uid\\\", \\\"state\\\"])}\\n+\\n+ history = np.zeros((len(targets), HISTORY_LEN), dtype=np.uint16)\\n+ weights = np.zeros((len(targets), HISTORY_LEN), dtype=np.float16)\\n+ liked = np.zeros((len(targets), LIKED_LEN), dtype=np.uint16)\\n+ disliked = np.zeros((len(targets), DISLIKED_LEN), dtype=np.uint16)\\n+ user_feat = np.zeros((len(targets), 4), dtype=np.float32)\\n+ for row, uid in enumerate(targets):\\n+ s, e = bounds[int(uid)]\\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ end = int(np.searchsorted(ts, cutoff, side=\\\"left\\\"))\\n+ positive = np.flatnonzero(ratio[:end] >= 50)\\n+ positive = positive[-HISTORY_LEN:]\\n+ raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\\n+ raw_w *= 1.0 + 0.25 * org[positive]\\n+ raw_w *= np.sqrt(np.minimum(ratio[positive], 120) / 50.0)\\n+ raw_w /= max(float(raw_w.sum()), 1e-8)\\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n+ lp = pref_group.get((int(uid), \\\"liked\\\"), np.empty(0, np.int32))\\n+ dp = pref_group.get((int(uid), \\\"disliked\\\"), np.empty(0, np.int32))\\n+ # Preference files lack activation times; popularity order is a stable overflow rule.\\n+ lp = np.sort(lp)[-LIKED_LEN:]\\n+ dp = np.sort(dp)[-DISLIKED_LEN:]\\n+ liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\\n+ disliked[row] = _pad_recent(dp.tolist(), DISLIKED_LEN)\\n+ user_feat[row] = [\\n+ math.log1p(len(positive)) / 10.0,\\n+ float(org[positive].mean()) if len(positive) else 0.0,\\n+ math.log1p(len(lp)) / 5.0,\\n+ math.log1p(len(dp)) / 3.0,\\n+ ]\\n+ return targets, history, weights, liked, disliked, user_feat\\n+\\n+\\n+@torch.inference_mode()\\n+def encode(model: TwoTower, users: tuple[np.ndarray, ...], features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ _, history, weights, liked, disliked, user_feat = users\\n+ chunks = []\\n+ for start in range(0, len(history), 512):\\n+ sl = slice(start, start + 512)\\n+ chunks.append(\\n+ model.user_tower(\\n+ torch.from_numpy(history[sl].astype(np.int64)),\\n+ torch.from_numpy(weights[sl].astype(np.float32)),\\n+ torch.from_numpy(liked[sl].astype(np.int64)),\\n+ torch.from_numpy(disliked[sl].astype(np.int64)),\\n+ torch.from_numpy(user_feat[sl]),\\n+ ).numpy()\\n+ )\\n+ ue = np.concatenate(chunks).astype(np.float32)\\n+ item_chunks = []\\n+ ft = torch.from_numpy(features)\\n+ for start in range(0, len(features), 2048):\\n+ ix = torch.arange(start, min(start + 2048, len(features)))\\n+ item_chunks.append(model.item_tower(ix, ft[ix]).numpy())\\n+ return ue, np.concatenate(item_chunks).astype(np.float32)\\n+\\n+\\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ idx = pd.Index(catalog_ids)\\n+ score = np.zeros(len(catalog_ids), dtype=np.float64)\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ cols = [\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ if family == \\\"listens\\\":\\n+ cols.append(\\\"played_ratio_pct\\\")\\n+ x = load_family(stage, family, cols)\\n+ mapped = idx.get_indexer(x.item_id)\\n+ mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\\n+ mask &= x.is_organic.to_numpy() == 1\\n+ if family == \\\"listens\\\":\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\\n+ lifetime = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").positive_listens.to_numpy(dtype=np.float64)\\n+ raw = score + prior * np.power(lifetime, 0.75)\\n+ # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\\n+ order = np.argsort(-raw, kind=\\\"stable\\\")\\n+ rank = np.empty(len(raw), dtype=np.int32)\\n+ rank[order] = np.arange(1, len(raw) + 1)\\n+ trend = -np.log(rank.astype(np.float64))\\n+ return ((trend - trend.mean()) / trend.std()).astype(np.float32)\\n+\\n+\\n+def relevant_truth() -> dict[int, dict[int, int]]:\\n+ z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ uid_row = {int(x): i for i, x in enumerate(z[\\\"uids\\\"])}\\n+ item_col = {int(x): i for i, x in enumerate(z[\\\"item_ids\\\"])}\\n+ truth: dict[int, dict[int, int]] = {}\\n+ for family, grade in [(\\\"val_listens.parquet\\\", 1), (\\\"val_likes.parquet\\\", 3)]:\\n+ x = pd.read_parquet(DATA / family)\\n+ mask = x.is_organic.to_numpy() == 1\\n+ if grade == 1:\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ for uid, item in x.loc[mask, [\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().itertuples(index=False, name=None):\\n+ r, c = uid_row.get(int(uid)), item_col.get(int(item))\\n+ if r is None or c is None or not ((int(z[\\\"packed_eligible\\\"][r, c // 8]) >> (c % 8)) & 1):\\n+ continue\\n+ truth.setdefault(int(uid), {})[int(item)] = max(grade, truth.get(int(uid), {}).get(int(item), 0))\\n+ return truth\\n+\\n+\\n+def ndcg10(items: np.ndarray, truth: dict[int, int]) -> float:\\n+ grades = np.asarray([truth.get(int(x), 0) for x in items[:10]], dtype=np.float64)\\n+ dcg = np.sum((2**grades - 1) / np.log2(np.arange(2, 12)))\\n+ ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\\n+\\n+\\n+def rank_and_tune(\\n+ stage: str,\\n+ targets: np.ndarray,\\n+ user_embedding: np.ndarray,\\n+ item_embedding: np.ndarray,\\n+ trend: np.ndarray,\\n+ output: Path,\\n+ forced_weight: float | None,\\n+) -> tuple[float, float | None]:\\n+ z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ assert np.array_equal(z[\\\"uids\\\"].astype(np.int64), targets)\\n+ masks = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(z[\\\"item_ids\\\"]), bitorder=\\\"little\\\").astype(bool)\\n+ truth = relevant_truth() if stage == \\\"validation\\\" else None\\n+ weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n+ best_score, best_weight, best_top = -1.0, None, None\\n+ all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\\n+ quality: dict[float, list[float]] = {float(w): [] for w in weights}\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ neural = user_embedding[start:end] @ item_embedding.T\\n+ for w in weights:\\n+ score = neural + float(w) * trend[None, :]\\n+ score[~masks[start:end]] = -np.inf\\n+ # Exact ordering is needed only within the selected top 100.\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ part_score = np.take_along_axis(score, part, axis=1)\\n+ order = np.argsort(-part_score, axis=1)\\n+ top = np.take_along_axis(part, order, axis=1)\\n+ all_top[float(w)][start:end] = top\\n+ if truth is not None:\\n+ for local, row in enumerate(range(start, end)):\\n+ gold = truth.get(int(targets[row]))\\n+ if gold:\\n+ quality[float(w)].append(ndcg10(z[\\\"item_ids\\\"][top[local]], gold))\\n+ if truth is not None:\\n+ for w in weights:\\n+ q = float(np.mean(quality[float(w)]))\\n+ print(f\\\"blend trend_weight={w:g} public_ndcg@10={q:.6f}\\\", flush=True)\\n+ if q > best_score:\\n+ best_score, best_weight = q, float(w)\\n+ best_top = all_top[best_weight]\\n+ else:\\n+ best_weight = float(weights[0])\\n+ best_top = all_top[best_weight]\\n+\\n+ frame = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(targets, 100).astype(np.int64),\\n+ \\\"item_id\\\": z[\\\"item_ids\\\"][best_top.reshape(-1)].astype(np.int64),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\\n+ }\\n+ )\\n+ frame.to_parquet(output, index=False)\\n+ return best_score, best_weight\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=8)\\n+ parser.add_argument(\\\"--dim\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--weeks\\\", type=int, default=30)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ parser.add_argument(\\\"--trend-weight\\\", type=float)\\n+ parser.add_argument(\\\"--rebuild-cache\\\", action=\\\"store_true\\\")\\n+ args = parser.parse_args()\\n+ started = time.time()\\n+ OUT.mkdir(exist_ok=True)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ features = item_features(catalog)\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w.npz\\\"\\n+ if cache.exists() and not args.rebuild_cache:\\n+ print(f\\\"loading snapshot cache {cache}\\\", flush=True)\\n+ snapshots = SnapshotArrays.load(cache)\\n+ else:\\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\\n+ snapshots.save(cache)\\n+ model = train_model(snapshots, features, args.dim, args.epochs, args.seed)\\n+ model_path = OUT / f\\\"two_tower_{args.stage}.pt\\\"\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"dim\\\": args.dim,\\n+ \\\"item_features\\\": features,\\n+ \\\"catalog_ids\\\": catalog_ids,\\n+ \\\"architecture\\\": \\\"history/preference MLP user tower + ID/feature MLP item tower\\\",\\n+ },\\n+ model_path,\\n+ )\\n+ users = build_inference_users(args.stage, catalog_ids)\\n+ ue, ie = encode(model, users, features)\\n+ trend = recent_trend(args.stage, catalog_ids)\\n+ output = OUT / (\\\"validation.parquet\\\" if args.stage == \\\"validation\\\" else \\\"submission.parquet\\\")\\n+ score, weight = rank_and_tune(args.stage, users[0], ue, ie, trend, output, args.trend_weight)\\n+ report = {\\n+ \\\"stage\\\": args.stage,\\n+ \\\"examples\\\": len(snapshots.history),\\n+ \\\"epochs\\\": args.epochs,\\n+ \\\"dimension\\\": args.dim + 1,\\n+ \\\"trend_weight\\\": weight,\\n+ \\\"public_ndcg@10\\\": score if args.stage == \\\"validation\\\" else None,\\n+ \\\"seconds\\\": round(time.time() - started, 1),\\n+ \\\"output\\\": str(output),\\n+ }\\n+ (OUT / f\\\"report_{args.stage}.json\\\").write_text(json.dumps(report, indent=2) + \\\"\\\\n\\\")\\n+ print(json.dumps(report, indent=2), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:35:18.082Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:18.032Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e6211ec8-c831-47f4-8d30-c529074ea4f4", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a history-only neural two-tower discovery ranker.\n\nThe script intentionally works from event records. It never constructs a\nuser-by-item matrix: rolling examples are histories followed by a future\norganic discovery, and training uses in-batch sampled-softmax negatives.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nVALIDATION_CUTOFF = 25_393_400\nGAP = 1_800\nWINDOW = 7 * 24 * 60 * 60\nHISTORY_LEN = 160\nLIKED_LEN = 80\nDISLIKED_LEN = 32\nTARGET_LEN = 64\nPAD = 0\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef catalog_index(values: np.ndarray, catalog_ids: np.ndarray) -> np.ndarray:\n \"\"\"Map raw IDs to catalog rows; returns -1 outside the retrieval catalog.\"\"\"\n return pd.Index(catalog_ids).get_indexer(values).astype(np.int32, copy=False)\n\n\ndef load_family(stage: str, family: str, columns: list[str]) -> pd.DataFrame:\n parts = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n if stage == \"final\":\n parts.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n frame = pd.concat(parts, ignore_index=True)\n # Each source is uid/time sorted, but concatenating the two windows is not.\n if stage == \"final\":\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n frame.reset_index(drop=True, inplace=True)\n return frame\n\n\ndef filter_catalog(frame: pd.DataFrame, catalog_ids: np.ndarray) -> pd.DataFrame:\n mapped = catalog_index(frame.item_id.to_numpy(), catalog_ids)\n keep = mapped >= 0\n frame = frame.loc[keep].copy()\n frame[\"ci\"] = mapped[keep]\n return frame\n\n\ndef group_bounds(sorted_uids: np.ndarray) -> dict[int, tuple[int, int]]:\n if not len(sorted_uids):\n return {}\n starts = np.r_[0, np.flatnonzero(sorted_uids[1:] != sorted_uids[:-1]) + 1]\n ends = np.r_[starts[1:], len(sorted_uids)]\n return {int(sorted_uids[s]): (int(s), int(e)) for s, e in zip(starts, ends)}\n\n\n@dataclass\nclass SnapshotArrays:\n history: np.ndarray\n history_weight: np.ndarray\n liked: np.ndarray\n disliked: np.ndarray\n user_features: np.ndarray\n targets: np.ndarray\n grades: np.ndarray\n cutoff_id: np.ndarray\n\n def save(self, path: Path) -> None:\n np.savez_compressed(\n path,\n history=self.history,\n history_weight=self.history_weight,\n liked=self.liked,\n disliked=self.disliked,\n user_features=self.user_features,\n targets=self.targets,\n grades=self.grades,\n cutoff_id=self.cutoff_id,\n )\n\n @classmethod\n def load(cls, path: Path) -> \"SnapshotArrays\":\n with np.load(path) as z:\n return cls(**{k: z[k] for k in z.files})\n\n\ndef _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\n out = np.zeros(length, dtype=np.uint16)\n if len(items):\n take = np.asarray(items[-length:], dtype=np.int64) + 1\n out[-len(take) :] = take.astype(np.uint16)\n return out\n\n\ndef build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n \"\"\"Create cutoff-correct rolling history -> organic-discovery examples.\"\"\"\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n latest = cutoff - GAP - WINDOW\n cutoffs = np.arange(latest - (weeks - 1) * WINDOW, latest + 1, WINDOW, dtype=np.int64)\n cutoffs = cutoffs[cutoffs > WINDOW]\n print(f\"loading {stage} events for {len(cutoffs)} rolling cutoffs\", flush=True)\n\n listens = load_family(\n stage,\n \"listens\",\n [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n listens = filter_catalog(listens, catalog_ids)\n # Compact dtypes matter for the 30M+ retained catalog events.\n lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\n lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\n li = listens.ci.to_numpy(dtype=np.int32, copy=False)\n lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\n lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\n lb = group_bounds(lu)\n\n action_parts: list[pd.DataFrame] = []\n specs = [\n (\"likes\", 0, 1),\n (\"unlikes\", 0, 0),\n (\"dislikes\", 1, 1),\n (\"undislikes\", 1, 0),\n ]\n organic_likes: pd.DataFrame | None = None\n for family, channel, state in specs:\n a = load_family(stage, family, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n a = filter_catalog(a, catalog_ids)\n if family == \"likes\":\n organic_likes = a.loc[a.is_organic == 1, [\"uid\", \"timestamp\", \"ci\"]].copy()\n a = a[[\"uid\", \"timestamp\", \"ci\"]].copy()\n a[\"channel\"] = np.uint8(channel)\n a[\"state\"] = np.uint8(state)\n action_parts.append(a)\n actions = pd.concat(action_parts, ignore_index=True)\n actions.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n au = actions.uid.to_numpy(dtype=np.uint32, copy=False)\n at = actions.timestamp.to_numpy(dtype=np.int64, copy=False)\n ai = actions.ci.to_numpy(dtype=np.int32, copy=False)\n ac = actions.channel.to_numpy(dtype=np.uint8, copy=False)\n ast = actions.state.to_numpy(dtype=np.uint8, copy=False)\n ab = group_bounds(au)\n assert organic_likes is not None\n organic_likes.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n ou = organic_likes.uid.to_numpy(dtype=np.uint32, copy=False)\n ot = organic_likes.timestamp.to_numpy(dtype=np.int64, copy=False)\n oi = organic_likes.ci.to_numpy(dtype=np.int32, copy=False)\n ob = group_bounds(ou)\n\n all_users = np.union1d(np.unique(lu), np.unique(au))\n histories: list[np.ndarray] = []\n history_weights: list[np.ndarray] = []\n liked_rows: list[np.ndarray] = []\n disliked_rows: list[np.ndarray] = []\n feature_rows: list[np.ndarray] = []\n target_rows: list[np.ndarray] = []\n grade_rows: list[np.ndarray] = []\n cutoff_rows: list[int] = []\n inf_time = np.iinfo(np.int64).max\n\n for user_no, uid in enumerate(all_users):\n if int(uid) not in lb:\n continue\n s, e = lb[int(uid)]\n uts, uitems, uorg, uratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n positive = uratio >= 50\n pts = uts[positive]\n pitems = uitems[positive]\n porg = uorg[positive]\n pratio = uratio[positive]\n first_time = np.full(len(catalog_ids), inf_time, dtype=np.int64)\n np.minimum.at(first_time, uitems, uts)\n\n if int(uid) in ab:\n a0, a1 = ab[int(uid)]\n uat, uai, uac, uas = at[a0:a1], ai[a0:a1], ac[a0:a1], ast[a0:a1]\n else:\n uat = np.empty(0, np.int64)\n uai = np.empty(0, np.int32)\n uac = np.empty(0, np.uint8)\n uas = np.empty(0, np.uint8)\n if int(uid) in ob:\n o0, o1 = ob[int(uid)]\n uot, uoi = ot[o0:o1], oi[o0:o1]\n else:\n uot = np.empty(0, np.int64)\n uoi = np.empty(0, np.int32)\n\n liked_state: dict[int, int] = {}\n disliked_state: dict[int, int] = {}\n action_ptr = 0\n for cutoff_no, c in enumerate(cutoffs):\n while action_ptr < len(uat) and uat[action_ptr] < c:\n item = int(uai[action_ptr])\n state_map = liked_state if uac[action_ptr] == 0 else disliked_state\n if uas[action_ptr]:\n state_map[item] = int(uat[action_ptr])\n else:\n state_map.pop(item, None)\n action_ptr += 1\n\n hp = int(np.searchsorted(pts, c, side=\"left\"))\n if hp < 10:\n continue\n hs = max(0, hp - HISTORY_LEN)\n hist_i = pitems[hs:hp]\n hist_t = pts[hs:hp]\n # Smoothly favor recent, organic and deeply completed history.\n raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\n raw_w *= 1.0 + 0.25 * porg[hs:hp]\n raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\n raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\n hist = np.zeros(HISTORY_LEN, dtype=np.uint16)\n hw = np.zeros(HISTORY_LEN, dtype=np.float16)\n hist[-len(hist_i) :] = (hist_i + 1).astype(np.uint16)\n hw[-len(hist_i) :] = raw_w.astype(np.float16)\n\n f0 = int(np.searchsorted(uts, c + GAP, side=\"left\"))\n f1 = int(np.searchsorted(uts, c + GAP + WINDOW, side=\"left\"))\n future_mask = (uorg[f0:f1] == 1) & (uratio[f0:f1] >= 80)\n future_items = uitems[f0:f1][future_mask]\n future_items = future_items[first_time[future_items] >= c]\n grades: dict[int, int] = {int(x): 1 for x in future_items}\n q0 = int(np.searchsorted(uot, c + GAP, side=\"left\"))\n q1 = int(np.searchsorted(uot, c + GAP + WINDOW, side=\"left\"))\n for item in uoi[q0:q1]:\n if first_time[item] >= c:\n grades[int(item)] = 3\n if not grades:\n continue\n\n # Keep all high-value likes, then deterministic listens if unusually busy.\n ordered_targets = sorted(grades, key=lambda x: (-grades[x], first_time[x], x))[:TARGET_LEN]\n tr = np.zeros(TARGET_LEN, dtype=np.uint16)\n gr = np.zeros(TARGET_LEN, dtype=np.uint8)\n tr[: len(ordered_targets)] = np.asarray(ordered_targets, dtype=np.uint16) + 1\n gr[: len(ordered_targets)] = np.asarray([grades[x] for x in ordered_targets], dtype=np.uint8)\n\n liked_recent = [x for x, _ in sorted(liked_state.items(), key=lambda z: z[1])]\n disliked_recent = [x for x, _ in sorted(disliked_state.items(), key=lambda z: z[1])]\n lf = math.log1p(hp) / 10.0\n recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\n features = np.asarray(\n [lf, recent_org, math.log1p(len(liked_state)) / 5.0, math.log1p(len(disliked_state)) / 3.0],\n dtype=np.float32,\n )\n histories.append(hist)\n history_weights.append(hw)\n liked_rows.append(_pad_recent(liked_recent, LIKED_LEN))\n disliked_rows.append(_pad_recent(disliked_recent, DISLIKED_LEN))\n feature_rows.append(features)\n target_rows.append(tr)\n grade_rows.append(gr)\n cutoff_rows.append(cutoff_no)\n if user_no and user_no % 2000 == 0:\n print(f\" users {user_no}/{len(all_users)} examples={len(histories)}\", flush=True)\n\n del listens, actions, action_parts\n result = SnapshotArrays(\n history=np.stack(histories),\n history_weight=np.stack(history_weights),\n liked=np.stack(liked_rows),\n disliked=np.stack(disliked_rows),\n user_features=np.stack(feature_rows),\n targets=np.stack(target_rows),\n grades=np.stack(grade_rows),\n cutoff_id=np.asarray(cutoff_rows, dtype=np.uint8),\n )\n print(\n f\"built {len(result.history):,} examples, \"\n f\"{np.count_nonzero(result.targets):,} relevant pairs\",\n flush=True,\n )\n return result\n\n\ndef item_features(catalog: pd.DataFrame) -> np.ndarray:\n pos = catalog.positive_listens.to_numpy(dtype=np.float64)\n org = catalog.organic_positive_listens.to_numpy(dtype=np.float64)\n x = np.column_stack(\n [\n np.log1p(pos),\n np.log1p(org),\n (org + 5.0) / (pos + 10.0),\n np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float64)),\n ]\n )\n return ((x - x.mean(0)) / (x.std(0) + 1e-6)).astype(np.float32)\n\n\nclass UserTower(nn.Module):\n def __init__(self, n_items: int, dim: int):\n super().__init__()\n self.listen_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.like_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.dislike_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.network = nn.Sequential(\n nn.Linear(3 * dim + 4, 2 * dim),\n nn.GELU(),\n nn.LayerNorm(2 * dim),\n nn.Linear(2 * dim, dim),\n )\n\n def forward(\n self,\n history: torch.Tensor,\n history_weight: torch.Tensor,\n liked: torch.Tensor,\n disliked: torch.Tensor,\n features: torch.Tensor,\n ) -> torch.Tensor:\n h = (self.listen_embedding(history) * history_weight.unsqueeze(-1)).sum(1)\n lm = liked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\n dm = disliked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\n lp = self.like_embedding(liked).sum(1) / lm\n dp = self.dislike_embedding(disliked).sum(1) / dm\n return F.normalize(self.network(torch.cat([h, lp, dp, features], dim=1)), dim=1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, n_items: int, feature_dim: int, dim: int):\n super().__init__()\n self.id_embedding = nn.Embedding(n_items, dim)\n self.feature_network = nn.Sequential(nn.Linear(feature_dim, dim), nn.GELU(), nn.Linear(dim, dim))\n self.network = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim), nn.GELU(), nn.Linear(dim, dim))\n\n def forward(self, item: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\n base = self.id_embedding(item) + self.feature_network(features)\n return F.normalize(base + self.network(base), dim=1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self, n_items: int, feature_dim: int, dim: int):\n super().__init__()\n self.user_tower = UserTower(n_items, dim)\n self.item_tower = ItemTower(n_items, feature_dim, dim)\n self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\n\n\ndef train_model(\n snapshots: SnapshotArrays,\n features: np.ndarray,\n dim: int,\n epochs: int,\n seed: int,\n) -> TwoTower:\n seed_everything(seed)\n torch.set_num_threads(min(8, torch.get_num_threads()))\n model = TwoTower(len(features), features.shape[1], dim)\n opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n feat_t = torch.from_numpy(features)\n n = len(snapshots.history)\n batch_size = 384\n rng = np.random.default_rng(seed)\n model.train()\n for epoch in range(epochs):\n order = rng.permutation(n)\n losses: list[float] = []\n for start in range(0, n - batch_size + 1, batch_size):\n ix = order[start : start + batch_size]\n target = np.empty(batch_size, dtype=np.int64)\n for j, row in enumerate(ix):\n valid = np.flatnonzero(snapshots.targets[row])\n grade = snapshots.grades[row, valid].astype(np.float64)\n # Likes matter more, without letting the rare grade-3 channel dominate.\n probability = np.where(grade == 3, 3.5, 1.0)\n k = rng.choice(valid, p=probability / probability.sum())\n target[j] = int(snapshots.targets[row, k]) - 1\n h = torch.from_numpy(snapshots.history[ix].astype(np.int64))\n hw = torch.from_numpy(snapshots.history_weight[ix].astype(np.float32))\n liked = torch.from_numpy(snapshots.liked[ix].astype(np.int64))\n disliked = torch.from_numpy(snapshots.disliked[ix].astype(np.int64))\n uf = torch.from_numpy(snapshots.user_features[ix])\n ti = torch.from_numpy(target)\n u = model.user_tower(h, hw, liked, disliked, uf)\n v = model.item_tower(ti, feat_t[ti])\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n # A duplicated positive in the batch is another positive, not a negative.\n same = ti[:, None].eq(ti[None, :])\n same.fill_diagonal_(False)\n logits = logits.masked_fill(same, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n opt.zero_grad(set_to_none=True)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n opt.step()\n losses.append(float(loss.detach()))\n print(\n f\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.4f} \"\n f\"scale={model.logit_scale.exp().item():.2f}\",\n flush=True,\n )\n return model.eval()\n\n\ndef build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n targets = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(dtype=np.int64)\n listens = load_family(\n stage,\n \"listens\",\n [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n listens = filter_catalog(listens, catalog_ids)\n lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\n lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\n li = listens.ci.to_numpy(dtype=np.int32, copy=False)\n lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\n lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\n bounds = group_bounds(lu)\n pref_name = \"validation_preference_state.parquet\" if stage == \"validation\" else \"test_preference_state.parquet\"\n pref = pd.read_parquet(DATA / pref_name)\n pref = filter_catalog(pref, catalog_ids)\n pref_group = {(int(u), str(state)): g.ci.to_numpy(dtype=np.int32) for (u, state), g in pref.groupby([\"uid\", \"state\"])}\n\n history = np.zeros((len(targets), HISTORY_LEN), dtype=np.uint16)\n weights = np.zeros((len(targets), HISTORY_LEN), dtype=np.float16)\n liked = np.zeros((len(targets), LIKED_LEN), dtype=np.uint16)\n disliked = np.zeros((len(targets), DISLIKED_LEN), dtype=np.uint16)\n user_feat = np.zeros((len(targets), 4), dtype=np.float32)\n for row, uid in enumerate(targets):\n s, e = bounds[int(uid)]\n ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n positive = np.flatnonzero(ratio[:end] >= 50)\n positive = positive[-HISTORY_LEN:]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n raw_w *= 1.0 + 0.25 * org[positive]\n raw_w *= np.sqrt(np.minimum(ratio[positive], 120) / 50.0)\n raw_w /= max(float(raw_w.sum()), 1e-8)\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n weights[row, -len(positive) :] = raw_w.astype(np.float16)\n lp = pref_group.get((int(uid), \"liked\"), np.empty(0, np.int32))\n dp = pref_group.get((int(uid), \"disliked\"), np.empty(0, np.int32))\n # Preference files lack activation times; popularity order is a stable overflow rule.\n lp = np.sort(lp)[-LIKED_LEN:]\n dp = np.sort(dp)[-DISLIKED_LEN:]\n liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\n disliked[row] = _pad_recent(dp.tolist(), DISLIKED_LEN)\n user_feat[row] = [\n math.log1p(len(positive)) / 10.0,\n float(org[positive].mean()) if len(positive) else 0.0,\n math.log1p(len(lp)) / 5.0,\n math.log1p(len(dp)) / 3.0,\n ]\n return targets, history, weights, liked, disliked, user_feat\n\n\n@torch.inference_mode()\ndef encode(model: TwoTower, users: tuple[np.ndarray, ...], features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n _, history, weights, liked, disliked, user_feat = users\n chunks = []\n for start in range(0, len(history), 512):\n sl = slice(start, start + 512)\n chunks.append(\n model.user_tower(\n torch.from_numpy(history[sl].astype(np.int64)),\n torch.from_numpy(weights[sl].astype(np.float32)),\n torch.from_numpy(liked[sl].astype(np.int64)),\n torch.from_numpy(disliked[sl].astype(np.int64)),\n torch.from_numpy(user_feat[sl]),\n ).numpy()\n )\n ue = np.concatenate(chunks).astype(np.float32)\n item_chunks = []\n ft = torch.from_numpy(features)\n for start in range(0, len(features), 2048):\n ix = torch.arange(start, min(start + 2048, len(features)))\n item_chunks.append(model.item_tower(ix, ft[ix]).numpy())\n return ue, np.concatenate(item_chunks).astype(np.float32)\n\n\ndef recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n idx = pd.Index(catalog_ids)\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n if family == \"listens\":\n cols.append(\"played_ratio_pct\")\n x = load_family(stage, family, cols)\n mapped = idx.get_indexer(x.item_id)\n mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\n mask &= x.is_organic.to_numpy() == 1\n if family == \"listens\":\n mask &= x.played_ratio_pct.to_numpy() >= 80\n score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\n lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\n raw = score + prior * np.power(lifetime, 0.75)\n # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\n order = np.argsort(-raw, kind=\"stable\")\n rank = np.empty(len(raw), dtype=np.int32)\n rank[order] = np.arange(1, len(raw) + 1)\n trend = -np.log(rank.astype(np.float64))\n return ((trend - trend.mean()) / trend.std()).astype(np.float32)\n\n\ndef relevant_truth() -> dict[int, dict[int, int]]:\n z = np.load(DATA / \"validation_eligible_candidates.npz\")\n uid_row = {int(x): i for i, x in enumerate(z[\"uids\"])}\n item_col = {int(x): i for i, x in enumerate(z[\"item_ids\"])}\n truth: dict[int, dict[int, int]] = {}\n for family, grade in [(\"val_listens.parquet\", 1), (\"val_likes.parquet\", 3)]:\n x = pd.read_parquet(DATA / family)\n mask = x.is_organic.to_numpy() == 1\n if grade == 1:\n mask &= x.played_ratio_pct.to_numpy() >= 80\n for uid, item in x.loc[mask, [\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False, name=None):\n r, c = uid_row.get(int(uid)), item_col.get(int(item))\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\n continue\n truth.setdefault(int(uid), {})[int(item)] = max(grade, truth.get(int(uid), {}).get(int(item), 0))\n return truth\n\n\ndef ndcg10(items: np.ndarray, truth: dict[int, int]) -> float:\n grades = np.asarray([truth.get(int(x), 0) for x in items[:10]], dtype=np.float64)\n dcg = np.sum((2**grades - 1) / np.log2(np.arange(2, 12)))\n ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n\n\ndef rank_and_tune(\n stage: str,\n targets: np.ndarray,\n user_embedding: np.ndarray,\n item_embedding: np.ndarray,\n trend: np.ndarray,\n output: Path,\n forced_weight: float | None,\n) -> tuple[float, float | None]:\n z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n assert np.array_equal(z[\"uids\"].astype(np.int64), targets)\n masks = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(z[\"item_ids\"]), bitorder=\"little\").astype(bool)\n truth = relevant_truth() if stage == \"validation\" else None\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n best_score, best_weight, best_top = -1.0, None, None\n all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\n for start in range(0, len(targets), 128):\n end = min(start + 128, len(targets))\n neural = user_embedding[start:end] @ item_embedding.T\n for w in weights:\n score = neural + float(w) * trend[None, :]\n score[~masks[start:end]] = -np.inf\n # Exact ordering is needed only within the selected top 100.\n part = np.argpartition(score, -100, axis=1)[:, -100:]\n part_score = np.take_along_axis(score, part, axis=1)\n order = np.argsort(-part_score, axis=1)\n top = np.take_along_axis(part, order, axis=1)\n all_top[float(w)][start:end] = top\n if truth is not None:\n for local, row in enumerate(range(start, end)):\n gold = truth.get(int(targets[row]))\n if gold:\n quality[float(w)].append(ndcg10(z[\"item_ids\"][top[local]], gold))\n if truth is not None:\n for w in weights:\n q = float(np.mean(quality[float(w)]))\n print(f\"blend trend_weight={w:g} public_ndcg@10={q:.6f}\", flush=True)\n if q > best_score:\n best_score, best_weight = q, float(w)\n best_top = all_top[best_weight]\n else:\n best_weight = float(weights[0])\n best_top = all_top[best_weight]\n\n frame = pd.DataFrame(\n {\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n \"item_id\": z[\"item_ids\"][best_top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\n }\n )\n frame.to_parquet(output, index=False)\n return best_score, best_weight\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n parser.add_argument(\"--epochs\", type=int, default=8)\n parser.add_argument(\"--dim\", type=int, default=48)\n parser.add_argument(\"--weeks\", type=int, default=30)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--trend-weight\", type=float)\n parser.add_argument(\"--rebuild-cache\", action=\"store_true\")\n args = parser.parse_args()\n started = time.time()\n OUT.mkdir(exist_ok=True)\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n features = item_features(catalog)\n cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w.npz\"\n if cache.exists() and not args.rebuild_cache:\n print(f\"loading snapshot cache {cache}\", flush=True)\n snapshots = SnapshotArrays.load(cache)\n else:\n snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\n snapshots.save(cache)\n model = train_model(snapshots, features, args.dim, args.epochs, args.seed)\n model_path = OUT / f\"two_tower_{args.stage}.pt\"\n torch.save(\n {\n \"state_dict\": model.state_dict(),\n \"dim\": args.dim,\n \"item_features\": features,\n \"catalog_ids\": catalog_ids,\n \"architecture\": \"history/preference MLP user tower + ID/feature MLP item tower\",\n },\n model_path,\n )\n users = build_inference_users(args.stage, catalog_ids)\n ue, ie = encode(model, users, features)\n trend = recent_trend(args.stage, catalog_ids)\n output = OUT / (\"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\")\n score, weight = rank_and_tune(args.stage, users[0], ue, ie, trend, output, args.trend_weight)\n report = {\n \"stage\": args.stage,\n \"examples\": len(snapshots.history),\n \"epochs\": args.epochs,\n \"dimension\": args.dim + 1,\n \"trend_weight\": weight,\n \"public_ndcg@10\": score if args.stage == \"validation\" else None,\n \"seconds\": round(time.time() - started, 1),\n \"output\": str(output),\n }\n (OUT / f\"report_{args.stage}.json\").write_text(json.dumps(report, indent=2) + \"\\n\")\n print(json.dumps(report, indent=2), flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:35:18.081Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c0c401c487d2b444116dbf6dfbc6", "usage": { "input_tokens": 34863, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 14283, "reasoning_output_tokens": 5376, "total_tokens": 49146 }, "turn_token_usage": { "input_tokens": 586393, "cached_input_tokens": 558720, "cache_write_input_tokens": 0, "output_tokens": 24291, "reasoning_output_tokens": 10354, "total_tokens": 610684 }, "thread_token_usage": { "input_tokens": 586393, "cached_input_tokens": 558720, "cache_write_input_tokens": 0, "output_tokens": 24291, "reasoning_output_tokens": 10354, "total_tokens": 610684 } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-25T00:35:19.768Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:35:19.768Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:35:21.044Z", "callId": "call_9wKXiRopwR7MMMx60cn3IFHi", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:35:21.217Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:21.132Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1c66de887d28d4dbb897b97dbcd", "usage": { "input_tokens": 49170, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 21, "total_tokens": 49270 }, "turn_token_usage": { "input_tokens": 635563, "cached_input_tokens": 593408, "cache_write_input_tokens": 0, "output_tokens": 24391, "reasoning_output_tokens": 10375, "total_tokens": 659954 }, "thread_token_usage": { "input_tokens": 635563, "cached_input_tokens": 593408, "cache_write_input_tokens": 0, "output_tokens": 24391, "reasoning_output_tokens": 10375, "total_tokens": 659954 } } }, { "kind": "context", "timestamp": "2026-09-25T00:35:21.212Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d2ad865-d3da-437b-803b-1792fb11349c", "process_id": "5642", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 2380 }, "formatted_output": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-25T00:35:22.540Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:35:22.540Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:35:24.513Z", "callId": "call_KSAWqIKI9mRrsImRlrXMMkkI", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2))))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:35:24.610Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:24.523Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d4cfb405-610a-4df9-b35d-88348c640867", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -538,3 +538,3 @@\n ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\n- return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2))))\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:35:24.609Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1c986d887d2884243a66b3fd59b", "usage": { "input_tokens": 49380, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 10, "total_tokens": 49509 }, "turn_token_usage": { "input_tokens": 684943, "cached_input_tokens": 642432, "cache_write_input_tokens": 0, "output_tokens": 24520, "reasoning_output_tokens": 10385, "total_tokens": 709463 }, "thread_token_usage": { "input_tokens": 684943, "cached_input_tokens": 642432, "cache_write_input_tokens": 0, "output_tokens": 24520, "reasoning_output_tokens": 10385, "total_tokens": 709463 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:35:27.339Z", "callId": "call_2KZPkXBGoCkKpTts98uI0Xqz", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:35:27.615Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:27.413Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1cce8c087d28fd8d38a34d428fe", "usage": { "input_tokens": 49533, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 0, "total_tokens": 49629 }, "turn_token_usage": { "input_tokens": 734476, "cached_input_tokens": 691584, "cache_write_input_tokens": 0, "output_tokens": 24616, "reasoning_output_tokens": 10385, "total_tokens": 759092 }, "thread_token_usage": { "input_tokens": 734476, "cached_input_tokens": 691584, "cache_write_input_tokens": 0, "output_tokens": 24616, "reasoning_output_tokens": 10385, "total_tokens": 759092 } } }, { "kind": "context", "timestamp": "2026-09-25T00:35:27.611Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fbd06e6d-3d8e-44bc-a0cd-41712511aac2", "process_id": "1793", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 107041312 }, "formatted_output": "" } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T00:35:30.358Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:35:30.358Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:35:32.032Z", "text": "The first full model is now implemented: separate PyTorch history/preference and item towers, 48 learned personalized dimensions, plus one offline trend dimension whose user-side coefficient is tuned on validation. Training uses rolling cutoff-correct examples and in-batch sampled softmax, with no user-ID parameters or global interaction matrix. I’m running it now and will use the public score to decide the next refinement." }, { "kind": "tool", "timestamp": "2026-09-25T00:35:33.650Z", "callId": "call_zHyoFZg5RgFdA8IUJELxxK48", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:35:34.814Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=36951", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:33.726Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1cfda1487d2b4cc515e9829b7f8", "usage": { "input_tokens": 49652, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 287, "reasoning_output_tokens": 101, "total_tokens": 49939 }, "turn_token_usage": { "input_tokens": 784128, "cached_input_tokens": 740992, "cache_write_input_tokens": 0, "output_tokens": 24903, "reasoning_output_tokens": 10486, "total_tokens": 809031 }, "thread_token_usage": { "input_tokens": 784128, "cached_input_tokens": 740992, "cache_write_input_tokens": 0, "output_tokens": 24903, "reasoning_output_tokens": 10486, "total_tokens": 809031 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T00:35:36.081Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:35:36.081Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:35:36.761Z", "callId": "call_7VwnWmAILNnXISS1frPg0ksV", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:36:06.770Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nloading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:36.814Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1d6fc8087d2be2dffa57b9fa76f", "usage": { "input_tokens": 49967, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 50025 }, "turn_token_usage": { "input_tokens": 834095, "cached_input_tokens": 790528, "cache_write_input_tokens": 0, "output_tokens": 24961, "reasoning_output_tokens": 10497, "total_tokens": 859056 }, "thread_token_usage": { "input_tokens": 834095, "cached_input_tokens": 790528, "cache_write_input_tokens": 0, "output_tokens": 24961, "reasoning_output_tokens": 10497, "total_tokens": 859056 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T00:36:07.886Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:36:07.886Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:36:08.575Z", "callId": "call_fbVhW3YdYewdqB3tukS2MRBN", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:36:38.583Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:36:08.661Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1f6f68487d29eb5dd9d5c676444", "usage": { "input_tokens": 50111, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 13, "total_tokens": 50171 }, "turn_token_usage": { "input_tokens": 884206, "cached_input_tokens": 840320, "cache_write_input_tokens": 0, "output_tokens": 25021, "reasoning_output_tokens": 10510, "total_tokens": 909227 }, "thread_token_usage": { "input_tokens": 884206, "cached_input_tokens": 840320, "cache_write_input_tokens": 0, "output_tokens": 25021, "reasoning_output_tokens": 10510, "total_tokens": 909227 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-25T00:36:40.562Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:36:40.562Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:36:41.139Z", "callId": "call_ghPquwuCTGwCJUPGRdeS5eAe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:37:01.441Z", "content": "Script completed\nWall time 20.3 seconds\nOutput:\n\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:36:41.182Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c216c92887d2823f6162d218790b", "usage": { "input_tokens": 50245, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 43, "total_tokens": 50335 }, "turn_token_usage": { "input_tokens": 934451, "cached_input_tokens": 890240, "cache_write_input_tokens": 0, "output_tokens": 25111, "reasoning_output_tokens": 10553, "total_tokens": 959562 }, "thread_token_usage": { "input_tokens": 934451, "cached_input_tokens": 890240, "cache_write_input_tokens": 0, "output_tokens": 25111, "reasoning_output_tokens": 10553, "total_tokens": 959562 } } }, { "kind": "context", "timestamp": "2026-09-25T00:37:01.439Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eee8ee87-605e-463b-86b2-24dd794b3bed", "process_id": "36951", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n", "stderr": "", "aggregated_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n", "exit_code": 1, "duration": { "secs": 87, "nanos": 625947818 }, "formatted_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n" } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-25T00:37:06.779Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:06.779Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:37:08.061Z", "text": "Training completed cleanly, but ranking hit an edge case: a few target users have no catalog-mapped positive history even though they meet the overall activity threshold. I’m handling those users with the tower’s zero/padding representation and reusing the completed checkpoint, so no training time is lost." }, { "kind": "tool", "timestamp": "2026-09-25T00:37:13.592Z", "callId": "call_hbeOYlDAPmyDppd3koAwDD5g", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- positive = np.flatnonzero(ratio[:end] >= 50)\\n- positive = positive[-HISTORY_LEN:]\\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\\n+ positive = all_positive[-HISTORY_LEN:]\\n@@\\n- history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n- weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n+ if len(positive):\\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n@@\\n- lp = np.sort(lp)[-LIKED_LEN:]\\n- dp = np.sort(dp)[-DISLIKED_LEN:]\\n+ lp = np.sort(lp)[:LIKED_LEN]\\n+ dp = np.sort(dp)[:DISLIKED_LEN]\\n@@\\n- math.log1p(len(positive)) / 10.0,\\n+ math.log1p(len(all_positive)) / 10.0,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:37:13.661Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:13.601Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-004a27c1-6169-4d19-8baa-cc9b65b311b9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -443,4 +443,4 @@\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n- positive = np.flatnonzero(ratio[:end] >= 50)\n- positive = positive[-HISTORY_LEN:]\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\n+ positive = all_positive[-HISTORY_LEN:]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n@@ -449,4 +449,5 @@\n raw_w /= max(float(raw_w.sum()), 1e-8)\n- history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n- weights[row, -len(positive) :] = raw_w.astype(np.float16)\n+ if len(positive):\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\n lp = pref_group.get((int(uid), \"liked\"), np.empty(0, np.int32))\n@@ -454,4 +455,4 @@\n # Preference files lack activation times; popularity order is a stable overflow rule.\n- lp = np.sort(lp)[-LIKED_LEN:]\n- dp = np.sort(dp)[-DISLIKED_LEN:]\n+ lp = np.sort(lp)[:LIKED_LEN]\n+ dp = np.sort(dp)[:DISLIKED_LEN]\n liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\n@@ -459,3 +460,3 @@\n user_feat[row] = [\n- math.log1p(len(positive)) / 10.0,\n+ math.log1p(len(all_positive)) / 10.0,\n float(org[positive].mean()) if len(positive) else 0.0,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:37:13.660Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c22db5f087d2a3f402408c29d672", "usage": { "input_tokens": 50555, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 617, "reasoning_output_tokens": 236, "total_tokens": 51172 }, "turn_token_usage": { "input_tokens": 985006, "cached_input_tokens": 940288, "cache_write_input_tokens": 0, "output_tokens": 25728, "reasoning_output_tokens": 10789, "total_tokens": 1010734 }, "thread_token_usage": { "input_tokens": 985006, "cached_input_tokens": 940288, "cache_write_input_tokens": 0, "output_tokens": 25728, "reasoning_output_tokens": 10789, "total_tokens": 1010734 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-25T00:37:14.994Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:14.994Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:19.866Z", "callId": "call_ffXRh3tduNLMHJfpeXyJnlRD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch,pandas as pd\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False)\\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\\nmodel.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\\nprint('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\\nprint('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:37:21.031Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=73380", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:19.935Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c239f85087d28182b276a39924b3", "usage": { "input_tokens": 51196, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 290, "reasoning_output_tokens": 11, "total_tokens": 51486 }, "turn_token_usage": { "input_tokens": 1036202, "cached_input_tokens": 990720, "cache_write_input_tokens": 0, "output_tokens": 26018, "reasoning_output_tokens": 10800, "total_tokens": 1062220 }, "thread_token_usage": { "input_tokens": 1036202, "cached_input_tokens": 990720, "cache_write_input_tokens": 0, "output_tokens": 26018, "reasoning_output_tokens": 10800, "total_tokens": 1062220 } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-25T00:37:22.047Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:22.047Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:22.726Z", "callId": "call_iP7CbdEdBTvp32Tu0tBAeNMC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73380,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:37:26.234Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\nTraceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:22.816Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c241359887d28984c177320d3a21", "usage": { "input_tokens": 51514, "cached_input_tokens": 51072, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 51567 }, "turn_token_usage": { "input_tokens": 1087716, "cached_input_tokens": 1041792, "cache_write_input_tokens": 0, "output_tokens": 26071, "reasoning_output_tokens": 10806, "total_tokens": 1113787 }, "thread_token_usage": { "input_tokens": 1087716, "cached_input_tokens": 1041792, "cache_write_input_tokens": 0, "output_tokens": 26071, "reasoning_output_tokens": 10806, "total_tokens": 1113787 } } }, { "kind": "context", "timestamp": "2026-09-25T00:37:26.232Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1e06dc19-aadf-4910-aa62-c70418cfd527", "process_id": "73380", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,pandas as pd\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False)\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\nmodel.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\nprint('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\nprint('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,pandas as pd\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False)\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\nmodel.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\nprint('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\nprint('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n", "exit_code": 1, "duration": { "secs": 6, "nanos": 201716663 }, "formatted_output": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n" } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-25T00:37:27.710Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:27.710Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:31.735Z", "callId": "call_dKaopqtlGQloltUjmtizP5RK", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n user_feat = np.zeros((len(targets), 4), dtype=np.float32)\\n for row, uid in enumerate(targets):\\n- s, e = bounds[int(uid)]\\n- ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ if int(uid) in bounds:\\n+ s, e = bounds[int(uid)]\\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ else:\\n+ ts = np.empty(0, dtype=np.int64)\\n+ items = np.empty(0, dtype=np.int32)\\n+ org = np.empty(0, dtype=np.uint8)\\n+ ratio = np.empty(0, dtype=np.uint16)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:37:31.828Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:31.745Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-105fb2e6-4a50-412e-a830-b57e1a004575", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -440,4 +440,10 @@\n for row, uid in enumerate(targets):\n- s, e = bounds[int(uid)]\n- ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n+ if int(uid) in bounds:\n+ s, e = bounds[int(uid)]\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n+ else:\n+ ts = np.empty(0, dtype=np.int64)\n+ items = np.empty(0, dtype=np.int32)\n+ org = np.empty(0, dtype=np.uint8)\n+ ratio = np.empty(0, dtype=np.uint16)\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:37:31.827Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2466aa887d2bcbd3d6e13a151ea", "usage": { "input_tokens": 51655, "cached_input_tokens": 51328, "cache_write_input_tokens": 0, "output_tokens": 260, "reasoning_output_tokens": 28, "total_tokens": 51915 }, "turn_token_usage": { "input_tokens": 1139371, "cached_input_tokens": 1093120, "cache_write_input_tokens": 0, "output_tokens": 26331, "reasoning_output_tokens": 10834, "total_tokens": 1165702 }, "thread_token_usage": { "input_tokens": 1139371, "cached_input_tokens": 1093120, "cache_write_input_tokens": 0, "output_tokens": 26331, "reasoning_output_tokens": 10834, "total_tokens": 1165702 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T00:37:33.009Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:33.009Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:37.706Z", "callId": "call_HpC7h9VeKPsiMbVWgI29iH7z", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:37:38.872Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=7754", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:37.791Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c24c18e487d2af9d8afe85b7756c", "usage": { "input_tokens": 51939, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 276, "reasoning_output_tokens": 7, "total_tokens": 52215 }, "turn_token_usage": { "input_tokens": 1191310, "cached_input_tokens": 1144576, "cache_write_input_tokens": 0, "output_tokens": 26607, "reasoning_output_tokens": 10841, "total_tokens": 1217917 }, "thread_token_usage": { "input_tokens": 1191310, "cached_input_tokens": 1144576, "cache_write_input_tokens": 0, "output_tokens": 26607, "reasoning_output_tokens": 10841, "total_tokens": 1217917 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:37:41.973Z", "callId": "call_NJJhqAhhYAF9ChvkVHLc7RJO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:38:11.982Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nusers built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:42.049Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c25310a087d2813710a2b8c4f08f", "usage": { "input_tokens": 52243, "cached_input_tokens": 51712, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 52288 }, "turn_token_usage": { "input_tokens": 1243553, "cached_input_tokens": 1196288, "cache_write_input_tokens": 0, "output_tokens": 26652, "reasoning_output_tokens": 10841, "total_tokens": 1270205 }, "thread_token_usage": { "input_tokens": 1243553, "cached_input_tokens": 1196288, "cache_write_input_tokens": 0, "output_tokens": 26652, "reasoning_output_tokens": 10841, "total_tokens": 1270205 } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-25T00:38:13.419Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:38:13.419Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:38:14.102Z", "callId": "call_AxMcpj3aVrYIYEuDynqWLP9T", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:38:44.111Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:38:14.192Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c274260c87d2ab4b0aa8f97c8946", "usage": { "input_tokens": 52331, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 24, "total_tokens": 52402 }, "turn_token_usage": { "input_tokens": 1295884, "cached_input_tokens": 1248384, "cache_write_input_tokens": 0, "output_tokens": 26723, "reasoning_output_tokens": 10865, "total_tokens": 1322607 }, "thread_token_usage": { "input_tokens": 1295884, "cached_input_tokens": 1248384, "cache_write_input_tokens": 0, "output_tokens": 26723, "reasoning_output_tokens": 10865, "total_tokens": 1322607 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-25T00:38:45.565Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:38:45.565Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:38:46.259Z", "callId": "call_ZJkGkLN5wMc1WD4z5y59fz28", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:39:16.267Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:38:46.342Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2944d7887d281daec4794423284", "usage": { "input_tokens": 52425, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 77, "reasoning_output_tokens": 30, "total_tokens": 52502 }, "turn_token_usage": { "input_tokens": 1348309, "cached_input_tokens": 1300480, "cache_write_input_tokens": 0, "output_tokens": 26800, "reasoning_output_tokens": 10895, "total_tokens": 1375109 }, "thread_token_usage": { "input_tokens": 1348309, "cached_input_tokens": 1300480, "cache_write_input_tokens": 0, "output_tokens": 26800, "reasoning_output_tokens": 10895, "total_tokens": 1375109 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T00:39:20.705Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:39:20.705Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:39:21.880Z", "text": "The model embeddings are encoded successfully. Exact blend tuning is currently the slow part because it ranks all 30,000 eligible candidates for every user across ten coefficients; I’m tightening that implementation after this pass so later iterations only rescore a safe candidate union." }, { "kind": "tool", "timestamp": "2026-09-25T00:39:22.522Z", "callId": "call_WXH9xuzWJAMj6CxrxER5Fswn", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:39:52.531Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:39:22.596Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2b4722887d28fcf7d437fbe8101", "usage": { "input_tokens": 52525, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 29, "total_tokens": 52659 }, "turn_token_usage": { "input_tokens": 1400834, "cached_input_tokens": 1352704, "cache_write_input_tokens": 0, "output_tokens": 26934, "reasoning_output_tokens": 10924, "total_tokens": 1427768 }, "thread_token_usage": { "input_tokens": 1400834, "cached_input_tokens": 1352704, "cache_write_input_tokens": 0, "output_tokens": 26934, "reasoning_output_tokens": 10924, "total_tokens": 1427768 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-25T00:39:54.349Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:39:54.349Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:39:55.130Z", "callId": "call_ASaioqQlMIB4mlz1suYcy6lO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:39:55.454Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:39:55.209Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2d8b54887d29ea61c82ddf05099", "usage": { "input_tokens": 52682, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 38, "total_tokens": 52772 }, "turn_token_usage": { "input_tokens": 1453516, "cached_input_tokens": 1405056, "cache_write_input_tokens": 0, "output_tokens": 27024, "reasoning_output_tokens": 10962, "total_tokens": 1480540 }, "thread_token_usage": { "input_tokens": 1453516, "cached_input_tokens": 1405056, "cache_write_input_tokens": 0, "output_tokens": 27024, "reasoning_output_tokens": 10962, "total_tokens": 1480540 } } }, { "kind": "context", "timestamp": "2026-09-25T00:39:55.452Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0863d59b-a0ba-454b-8069-c4eedd545774", "process_id": "7754", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n", "stderr": "", "aggregated_output": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n", "exit_code": 1, "duration": { "secs": 137, "nanos": 581058123 }, "formatted_output": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n" } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-25T00:40:05.693Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:40:05.693Z", "text": "" } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-25T00:40:07.162Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:40:07.162Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:40:36.206Z", "callId": "call_mdGmZc87Af8VrwiqTsFXvSPl", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def relevant_truth() -> dict[int, dict[int, int]]:\\n- z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n- uid_row = {int(x): i for i, x in enumerate(z[\\\"uids\\\"])}\\n- item_col = {int(x): i for i, x in enumerate(z[\\\"item_ids\\\"])}\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].copy()\\n+ item_ids = z[\\\"item_ids\\\"].copy()\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ uid_row = {int(x): i for i, x in enumerate(uids)}\\n+ item_col = {int(x): i for i, x in enumerate(item_ids)}\\n@@\\n- if r is None or c is None or not ((int(z[\\\"packed_eligible\\\"][r, c // 8]) >> (c % 8)) & 1):\\n+ if r is None or c is None or not ((int(packed[r, c // 8]) >> (c % 8)) & 1):\\n@@\\n ) -> tuple[float, float | None]:\\n- z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n- assert np.array_equal(z[\\\"uids\\\"].astype(np.int64), targets)\\n- masks = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(z[\\\"item_ids\\\"]), bitorder=\\\"little\\\").astype(bool)\\n+ with np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ eligible_uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(eligible_uids, targets)\\n+ masks = np.unpackbits(packed, axis=1, count=len(eligible_items), bitorder=\\\"little\\\").astype(bool)\\n truth = relevant_truth() if stage == \\\"validation\\\" else None\\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n- best_score, best_weight, best_top = -1.0, None, None\\n- all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\\n+ best_score, best_weight = -1.0, None\\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\\n- for start in range(0, len(targets), 128):\\n- end = min(start + 128, len(targets))\\n- neural = user_embedding[start:end] @ item_embedding.T\\n- for w in weights:\\n- score = neural + float(w) * trend[None, :]\\n- score[~masks[start:end]] = -np.inf\\n- # Exact ordering is needed only within the selected top 100.\\n- part = np.argpartition(score, -100, axis=1)[:, -100:]\\n- part_score = np.take_along_axis(score, part, axis=1)\\n- order = np.argsort(-part_score, axis=1)\\n- top = np.take_along_axis(part, order, axis=1)\\n- all_top[float(w)][start:end] = top\\n- if truth is not None:\\n- for local, row in enumerate(range(start, end)):\\n- gold = truth.get(int(targets[row]))\\n- if gold:\\n- quality[float(w)].append(ndcg10(z[\\\"item_ids\\\"][top[local]], gold))\\n+ if truth is not None and len(weights) > 1:\\n+ # Tune on a safe union of each endpoint's top 700. This avoids ten full\\n+ # 8,456 x 30,000 partial sorts; the selected coefficient is ranked\\n+ # exactly over the full catalog below.\\n+ trend_order = np.argsort(-trend, kind=\\\"stable\\\")\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ neural = user_embedding[start:end] @ item_embedding.T\\n+ neural[~masks[start:end]] = -np.inf\\n+ neural_part = np.argpartition(neural, -700, axis=1)[:, -700:]\\n+ for local, row in enumerate(range(start, end)):\\n+ trend_part = trend_order[masks[row, trend_order]][:700]\\n+ candidates = np.union1d(neural_part[local], trend_part)\\n+ base = neural[local, candidates]\\n+ for w in weights:\\n+ s = base + float(w) * trend[candidates]\\n+ top = candidates[np.argsort(-s)[:10]]\\n+ gold = truth.get(int(targets[row]))\\n+ if gold:\\n+ quality[float(w)].append(ndcg10(eligible_items[top], gold))\\n if truth is not None:\\n for w in weights:\\n q = float(np.mean(quality[float(w)]))\\n@@\\n if q > best_score:\\n best_score, best_weight = q, float(w)\\n- best_top = all_top[best_weight]\\n else:\\n best_weight = float(weights[0])\\n- best_top = all_top[best_weight]\\n+\\n+ # One exact full-catalog ranking for the selected deployment coefficient.\\n+ best_top = np.empty((len(targets), 100), dtype=np.int32)\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ score = user_embedding[start:end] @ item_embedding.T\\n+ score += float(best_weight) * trend[None, :]\\n+ score[~masks[start:end]] = -np.inf\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ part_score = np.take_along_axis(score, part, axis=1)\\n+ order = np.argsort(-part_score, axis=1)\\n+ best_top[start:end] = np.take_along_axis(part, order, axis=1)\\n@@\\n- \\\"item_id\\\": z[\\\"item_ids\\\"][best_top.reshape(-1)].astype(np.int64),\\n+ \\\"item_id\\\": eligible_items[best_top.reshape(-1)].astype(np.int64),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:40:36.284Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:40:36.216Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-91af5fe8-beb2-4f7b-9e32-c6575b8bb9e3", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -524,5 +524,8 @@\n def relevant_truth() -> dict[int, dict[int, int]]:\n- z = np.load(DATA / \"validation_eligible_candidates.npz\")\n- uid_row = {int(x): i for i, x in enumerate(z[\"uids\"])}\n- item_col = {int(x): i for i, x in enumerate(z[\"item_ids\"])}\n+ with np.load(DATA / \"validation_eligible_candidates.npz\") as z:\n+ uids = z[\"uids\"].copy()\n+ item_ids = z[\"item_ids\"].copy()\n+ packed = z[\"packed_eligible\"].copy()\n+ uid_row = {int(x): i for i, x in enumerate(uids)}\n+ item_col = {int(x): i for i, x in enumerate(item_ids)}\n truth: dict[int, dict[int, int]] = {}\n@@ -535,3 +538,3 @@\n r, c = uid_row.get(int(uid)), item_col.get(int(item))\n- if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\n+ if r is None or c is None or not ((int(packed[r, c // 8]) >> (c % 8)) & 1):\n continue\n@@ -557,27 +560,32 @@\n ) -> tuple[float, float | None]:\n- z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n- assert np.array_equal(z[\"uids\"].astype(np.int64), targets)\n- masks = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(z[\"item_ids\"]), bitorder=\"little\").astype(bool)\n+ with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+ eligible_uids = z[\"uids\"].astype(np.int64, copy=True)\n+ eligible_items = z[\"item_ids\"].astype(np.int64, copy=True)\n+ packed = z[\"packed_eligible\"].copy()\n+ assert np.array_equal(eligible_uids, targets)\n+ masks = np.unpackbits(packed, axis=1, count=len(eligible_items), bitorder=\"little\").astype(bool)\n truth = relevant_truth() if stage == \"validation\" else None\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n- best_score, best_weight, best_top = -1.0, None, None\n- all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\n+ best_score, best_weight = -1.0, None\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\n- for start in range(0, len(targets), 128):\n- end = min(start + 128, len(targets))\n- neural = user_embedding[start:end] @ item_embedding.T\n- for w in weights:\n- score = neural + float(w) * trend[None, :]\n- score[~masks[start:end]] = -np.inf\n- # Exact ordering is needed only within the selected top 100.\n- part = np.argpartition(score, -100, axis=1)[:, -100:]\n- part_score = np.take_along_axis(score, part, axis=1)\n- order = np.argsort(-part_score, axis=1)\n- top = np.take_along_axis(part, order, axis=1)\n- all_top[float(w)][start:end] = top\n- if truth is not None:\n- for local, row in enumerate(range(start, end)):\n+ if truth is not None and len(weights) > 1:\n+ # Tune on a safe union of each endpoint's top 700. This avoids ten full\n+ # 8,456 x 30,000 partial sorts; the selected coefficient is ranked\n+ # exactly over the full catalog below.\n+ trend_order = np.argsort(-trend, kind=\"stable\")\n+ for start in range(0, len(targets), 128):\n+ end = min(start + 128, len(targets))\n+ neural = user_embedding[start:end] @ item_embedding.T\n+ neural[~masks[start:end]] = -np.inf\n+ neural_part = np.argpartition(neural, -700, axis=1)[:, -700:]\n+ for local, row in enumerate(range(start, end)):\n+ trend_part = trend_order[masks[row, trend_order]][:700]\n+ candidates = np.union1d(neural_part[local], trend_part)\n+ base = neural[local, candidates]\n+ for w in weights:\n+ s = base + float(w) * trend[candidates]\n+ top = candidates[np.argsort(-s)[:10]]\n gold = truth.get(int(targets[row]))\n if gold:\n- quality[float(w)].append(ndcg10(z[\"item_ids\"][top[local]], gold))\n+ quality[float(w)].append(ndcg10(eligible_items[top], gold))\n if truth is not None:\n@@ -588,7 +596,17 @@\n best_score, best_weight = q, float(w)\n- best_top = all_top[best_weight]\n else:\n best_weight = float(weights[0])\n- best_top = all_top[best_weight]\n \n+ # One exact full-catalog ranking for the selected deployment coefficient.\n+ best_top = np.empty((len(targets), 100), dtype=np.int32)\n+ for start in range(0, len(targets), 128):\n+ end = min(start + 128, len(targets))\n+ score = user_embedding[start:end] @ item_embedding.T\n+ score += float(best_weight) * trend[None, :]\n+ score[~masks[start:end]] = -np.inf\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\n+ part_score = np.take_along_axis(score, part, axis=1)\n+ order = np.argsort(-part_score, axis=1)\n+ best_top[start:end] = np.take_along_axis(part, order, axis=1)\n+\n frame = pd.DataFrame(\n@@ -596,3 +614,3 @@\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n- \"item_id\": z[\"item_ids\"][best_top.reshape(-1)].astype(np.int64),\n+ \"item_id\": eligible_items[best_top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:40:36.283Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2dba3e887d2b63bcce2f9b2c34b", "usage": { "input_tokens": 53172, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 2214, "reasoning_output_tokens": 592, "total_tokens": 55386 }, "turn_token_usage": { "input_tokens": 1506688, "cached_input_tokens": 1457536, "cache_write_input_tokens": 0, "output_tokens": 29238, "reasoning_output_tokens": 11554, "total_tokens": 1535926 }, "thread_token_usage": { "input_tokens": 1506688, "cached_input_tokens": 1457536, "cache_write_input_tokens": 0, "output_tokens": 29238, "reasoning_output_tokens": 11554, "total_tokens": 1535926 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-25T00:40:38.729Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:40:38.729Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:40:43.816Z", "callId": "call_MFD9uKbmAegH6VzfN1hOixme", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch,numpy as np\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:40:44.982Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=48313", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:40:43.901Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3049ac487d29e6b6cb9c776a811", "usage": { "input_tokens": 55410, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 357, "reasoning_output_tokens": 66, "total_tokens": 55767 }, "turn_token_usage": { "input_tokens": 1562098, "cached_input_tokens": 1510528, "cache_write_input_tokens": 0, "output_tokens": 29595, "reasoning_output_tokens": 11620, "total_tokens": 1591693 }, "thread_token_usage": { "input_tokens": 1562098, "cached_input_tokens": 1510528, "cache_write_input_tokens": 0, "output_tokens": 29595, "reasoning_output_tokens": 11620, "total_tokens": 1591693 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:40:46.825Z", "callId": "call_G4DKPTd9tiGuTKysfqamfKL1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":48313,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:41:02.479Z", "content": "Script completed\nWall time 15.6 seconds\nOutput:\n\nusers built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:40:46.919Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c30d2fe087d2a91bf684aa52e8c0", "usage": { "input_tokens": 55795, "cached_input_tokens": 55168, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 55840 }, "turn_token_usage": { "input_tokens": 1617893, "cached_input_tokens": 1565696, "cache_write_input_tokens": 0, "output_tokens": 29640, "reasoning_output_tokens": 11620, "total_tokens": 1647533 }, "thread_token_usage": { "input_tokens": 1617893, "cached_input_tokens": 1565696, "cache_write_input_tokens": 0, "output_tokens": 29640, "reasoning_output_tokens": 11620, "total_tokens": 1647533 } } }, { "kind": "context", "timestamp": "2026-09-25T00:41:02.477Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86ba4517-872f-4e47-8a17-e9232818d899", "process_id": "48313", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,numpy as np\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,numpy as np\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n", "stderr": "", "aggregated_output": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 496386594 }, "formatted_output": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-25T00:41:03.884Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:41:03.884Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:41:04.996Z", "callId": "call_GL3RJlRwtMhlicQMxgHkb0O2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/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": "2026-09-25T00:41:08.348Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:41:05.093Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c31eb11c87d28946f179a7516e34", "usage": { "input_tokens": 56059, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 23, "total_tokens": 56151 }, "turn_token_usage": { "input_tokens": 1673952, "cached_input_tokens": 1621376, "cache_write_input_tokens": 0, "output_tokens": 29732, "reasoning_output_tokens": 11643, "total_tokens": 1703684 }, "thread_token_usage": { "input_tokens": 1673952, "cached_input_tokens": 1621376, "cache_write_input_tokens": 0, "output_tokens": 29732, "reasoning_output_tokens": 11643, "total_tokens": 1703684 } } }, { "kind": "context", "timestamp": "2026-09-25T00:41:08.344Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73ad83b3-8c0a-405b-a9ef-507e310fdb66", "process_id": "71308", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 184181411 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-25T00:41:18.579Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:41:18.579Z", "text": "" } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-25T00:41:24.326Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:41:24.326Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:41:25.857Z", "text": "The first exact public result is 0.036140. The learned component alone is weak—as expected for unseen-item discovery—but it adds a meaningful personalized reranking lift over the roughly 0.031 trend baseline. I’m now optimizing the offline item signal’s decay and like/listen weighting, then I’ll retrain the personalized tower longer against the best variant." }, { "kind": "tool", "timestamp": "2026-09-25T00:41:38.666Z", "callId": "call_0fOXOhUImT7AuvUqNYKBmtwN", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,sys,itertools,time\\nfrom pathlib import Path\\nsys.path.insert(0,'.'); import train_ranker as tr\\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\\narr={}\\nfor fam in ['listens','likes']:\\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\\n base=(ci>=0)&(age>=0)&(age=80\\n a=np.zeros((nd,len(ids)),np.float32)\\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\\n# load truth/masks once\\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\\npop=cat.positive_listens.to_numpy(float)\\ndef ev(score):\\n order=np.argsort(-score,kind='stable'); probe=order[:300]\\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\\n out=[]\\n for r in rows:\\n top=probe[masks[r,probe]][:10]\\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\\n return np.mean(out)\\nresults=[]\\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\\n decay=2**(-np.arange(nd)/half)\\n L=decay@arr['listens']; A=decay@arr['likes']\\n for aw in [2,4,7,10,14,20,30]:\\n for prior in [0,.03,.1,.25,.5,1]:\\n s=L+aw*A+prior*pop**.75\\n results.append((ev(s),half,aw,prior))\\nresults.sort(reverse=True)\\nfor x in results[:30]: print(x,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:41:39.832Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=85093", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:41:38.762Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c324862087d293f1d1f09d9ce42d", "usage": { "input_tokens": 56219, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 1634, "reasoning_output_tokens": 834, "total_tokens": 57853 }, "turn_token_usage": { "input_tokens": 1730171, "cached_input_tokens": 1677312, "cache_write_input_tokens": 0, "output_tokens": 31366, "reasoning_output_tokens": 12477, "total_tokens": 1761537 }, "thread_token_usage": { "input_tokens": 1730171, "cached_input_tokens": 1677312, "cache_write_input_tokens": 0, "output_tokens": 31366, "reasoning_output_tokens": 12477, "total_tokens": 1761537 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:41:41.468Z", "callId": "call_yFKyCqio6umzaDvc8vPC82CU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:42:11.476Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nlistens 1170755.0\r\nlikes 41623.0\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:41:41.570Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c344047887d282b9eb6040013007", "usage": { "input_tokens": 57881, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 57926 }, "turn_token_usage": { "input_tokens": 1788052, "cached_input_tokens": 1733376, "cache_write_input_tokens": 0, "output_tokens": 31411, "reasoning_output_tokens": 12477, "total_tokens": 1819463 }, "thread_token_usage": { "input_tokens": 1788052, "cached_input_tokens": 1733376, "cache_write_input_tokens": 0, "output_tokens": 31411, "reasoning_output_tokens": 12477, "total_tokens": 1819463 } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-25T00:42:13.754Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:42:13.754Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:42:14.448Z", "callId": "call_ea6wvuEh6L21eVts0Muz1aMP", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:42:44.468Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:42:14.559Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c363ab2087d2aa51c66dde096acd", "usage": { "input_tokens": 57965, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 28, "total_tokens": 58040 }, "turn_token_usage": { "input_tokens": 1846017, "cached_input_tokens": 1791104, "cache_write_input_tokens": 0, "output_tokens": 31486, "reasoning_output_tokens": 12505, "total_tokens": 1877503 }, "thread_token_usage": { "input_tokens": 1846017, "cached_input_tokens": 1791104, "cache_write_input_tokens": 0, "output_tokens": 31486, "reasoning_output_tokens": 12505, "total_tokens": 1877503 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-25T00:42:45.686Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:42:45.686Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:42:46.404Z", "callId": "call_hwn9uuzZdOX2hshAdPViPQn7", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:42:50.140Z", "content": "Script completed\nWall time 3.7 seconds\nOutput:\n\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:42:46.487Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c384a81087d2acd2e0179b324664", "usage": { "input_tokens": 58063, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 58120 }, "turn_token_usage": { "input_tokens": 1904080, "cached_input_tokens": 1848832, "cache_write_input_tokens": 0, "output_tokens": 31543, "reasoning_output_tokens": 12515, "total_tokens": 1935623 }, "thread_token_usage": { "input_tokens": 1904080, "cached_input_tokens": 1848832, "cache_write_input_tokens": 0, "output_tokens": 31543, "reasoning_output_tokens": 12515, "total_tokens": 1935623 } } }, { "kind": "context", "timestamp": "2026-09-25T00:42:50.138Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-942e2108-c9ac-4b96-bf04-af19c8a1bef6", "process_id": "85093", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,sys,itertools,time\nfrom pathlib import Path\nsys.path.insert(0,'.'); import train_ranker as tr\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\narr={}\nfor fam in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\n base=(ci>=0)&(age>=0)&(age=80\n a=np.zeros((nd,len(ids)),np.float32)\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\n# load truth/masks once\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\npop=cat.positive_listens.to_numpy(float)\ndef ev(score):\n order=np.argsort(-score,kind='stable'); probe=order[:300]\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\n out=[]\n for r in rows:\n top=probe[masks[r,probe]][:10]\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\n return np.mean(out)\nresults=[]\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\n decay=2**(-np.arange(nd)/half)\n L=decay@arr['listens']; A=decay@arr['likes']\n for aw in [2,4,7,10,14,20,30]:\n for prior in [0,.03,.1,.25,.5,1]:\n s=L+aw*A+prior*pop**.75\n results.append((ev(s),half,aw,prior))\nresults.sort(reverse=True)\nfor x in results[:30]: print(x,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,sys,itertools,time\nfrom pathlib import Path\nsys.path.insert(0,'.'); import train_ranker as tr\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\narr={}\nfor fam in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\n base=(ci>=0)&(age>=0)&(age=80\n a=np.zeros((nd,len(ids)),np.float32)\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\n# load truth/masks once\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\npop=cat.positive_listens.to_numpy(float)\ndef ev(score):\n order=np.argsort(-score,kind='stable'); probe=order[:300]\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\n out=[]\n for r in rows:\n top=probe[masks[r,probe]][:10]\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\n return np.mean(out)\nresults=[]\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\n decay=2**(-np.arange(nd)/half)\n L=decay@arr['listens']; A=decay@arr['likes']\n for aw in [2,4,7,10,14,20,30]:\n for prior in [0,.03,.1,.25,.5,1]:\n s=L+aw*A+prior*pop**.75\n results.append((ev(s),half,aw,prior))\nresults.sort(reverse=True)\nfor x in results[:30]: print(x,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n", "stderr": "", "aggregated_output": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n", "exit_code": 0, "duration": { "secs": 71, "nanos": 307377716 }, "formatted_output": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T00:42:54.156Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:42:54.156Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:00.443Z", "callId": "call_zaaidx37Oo4TNDWjUbIXFbjP", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\\n@@\\n- for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ # Public tuning favored a short three-day half-life and strong explicit\\n+ # actions. Counts use only information available strictly before cutoff.\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 30.0)]:\\n@@\\n- mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n@@\\n- score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\\n+ weighted = np.exp2(-age_day[mask] / 3.0)\\n+ score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:43:00.565Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:00.452Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e03558f3-06f7-4b51-ae4d-63bfea081124", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -498,3 +498,3 @@\n \n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -502,3 +502,5 @@\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n- for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n+ # Public tuning favored a short three-day half-life and strong explicit\n+ # actions. Counts use only information available strictly before cutoff.\n+ for family, gain in [(\"listens\", 1.0), (\"likes\", 30.0)]:\n cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n@@ -508,3 +510,4 @@\n mapped = idx.get_indexer(x.item_id)\n- mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n mask &= x.is_organic.to_numpy() == 1\n@@ -512,3 +515,4 @@\n mask &= x.played_ratio_pct.to_numpy() >= 80\n- score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\n+ weighted = np.exp2(-age_day[mask] / 3.0)\n+ score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\n lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:43:00.563Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c38a5b5887d286bb35d9d1933128", "usage": { "input_tokens": 58859, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 517, "reasoning_output_tokens": 159, "total_tokens": 59376 }, "turn_token_usage": { "input_tokens": 1962939, "cached_input_tokens": 1906688, "cache_write_input_tokens": 0, "output_tokens": 32060, "reasoning_output_tokens": 12674, "total_tokens": 1994999 }, "thread_token_usage": { "input_tokens": 1962939, "cached_input_tokens": 1906688, "cache_write_input_tokens": 0, "output_tokens": 32060, "reasoning_output_tokens": 12674, "total_tokens": 1994999 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T00:43:01.894Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:01.894Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:05.226Z", "callId": "call_TgcPVDMq7e6fuvDXXdYFA727", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:43:06.391Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=39451", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:05.339Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c394d19c87d297b9e78d3fce5bdb", "usage": { "input_tokens": 59400, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 12, "total_tokens": 59604 }, "turn_token_usage": { "input_tokens": 2022339, "cached_input_tokens": 1965312, "cache_write_input_tokens": 0, "output_tokens": 32264, "reasoning_output_tokens": 12686, "total_tokens": 2054603 }, "thread_token_usage": { "input_tokens": 2022339, "cached_input_tokens": 1965312, "cache_write_input_tokens": 0, "output_tokens": 32264, "reasoning_output_tokens": 12686, "total_tokens": 2054603 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:43:09.827Z", "callId": "call_yctryFsY6FaZaHNMiRF966si", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":39451,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:43:21.497Z", "content": "Script completed\nWall time 11.7 seconds\nOutput:\n\ntrend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:09.922Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c39a919487d2bb9a84df9d48fb4c", "usage": { "input_tokens": 59632, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 59677 }, "turn_token_usage": { "input_tokens": 2081971, "cached_input_tokens": 2024576, "cache_write_input_tokens": 0, "output_tokens": 32309, "reasoning_output_tokens": 12686, "total_tokens": 2114280 }, "thread_token_usage": { "input_tokens": 2081971, "cached_input_tokens": 2024576, "cache_write_input_tokens": 0, "output_tokens": 32309, "reasoning_output_tokens": 12686, "total_tokens": 2114280 } } }, { "kind": "context", "timestamp": "2026-09-25T00:43:21.495Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae2aff46-0b96-4d04-af83-7a1b27a60ebe", "process_id": "39451", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n", "stderr": "", "aggregated_output": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 105807487 }, "formatted_output": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T00:43:23.035Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:23.035Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:24.128Z", "callId": "call_DBOVjITEgoq1MMjdooJVXi34", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/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": "2026-09-25T00:43:27.490Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:24.220Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3a9cb7087d286893dbeb93f23c3", "usage": { "input_tokens": 59892, "cached_input_tokens": 59392, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 20, "total_tokens": 59981 }, "turn_token_usage": { "input_tokens": 2141863, "cached_input_tokens": 2083968, "cache_write_input_tokens": 0, "output_tokens": 32398, "reasoning_output_tokens": 12706, "total_tokens": 2174261 }, "thread_token_usage": { "input_tokens": 2141863, "cached_input_tokens": 2083968, "cache_write_input_tokens": 0, "output_tokens": 32398, "reasoning_output_tokens": 12706, "total_tokens": 2174261 } } }, { "kind": "context", "timestamp": "2026-09-25T00:43:27.486Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a08030a-088a-4634-8fa0-81261dde0041", "process_id": "49767", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 190531389 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T00:43:31.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:31.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:37.577Z", "callId": "call_r8rvv4opkbb0tTgGLOU7dard", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\\nmodel=tr.train_model(snap,feat,48,15,20260926)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:43:38.744Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=87853", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:37.719Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3afa8bc87d2a058c42655083deb", "usage": { "input_tokens": 60049, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 167, "total_tokens": 60562 }, "turn_token_usage": { "input_tokens": 2201912, "cached_input_tokens": 2143744, "cache_write_input_tokens": 0, "output_tokens": 32911, "reasoning_output_tokens": 12873, "total_tokens": 2234823 }, "thread_token_usage": { "input_tokens": 2201912, "cached_input_tokens": 2143744, "cache_write_input_tokens": 0, "output_tokens": 32911, "reasoning_output_tokens": 12873, "total_tokens": 2234823 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T00:43:39.945Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:39.945Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:40.648Z", "callId": "call_Y1GtLBvaIf5tBFi2ctH89usN", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:44:10.657Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:40.743Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3bafec887d2b05227bb5259f5bc", "usage": { "input_tokens": 60590, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 60645 }, "turn_token_usage": { "input_tokens": 2262502, "cached_input_tokens": 2203648, "cache_write_input_tokens": 0, "output_tokens": 32966, "reasoning_output_tokens": 12881, "total_tokens": 2295468 }, "thread_token_usage": { "input_tokens": 2262502, "cached_input_tokens": 2203648, "cache_write_input_tokens": 0, "output_tokens": 32966, "reasoning_output_tokens": 12881, "total_tokens": 2295468 } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T00:44:12.066Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:12.066Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:12.742Z", "callId": "call_0VF28tP3YtD3xMKCRSWNGhMy", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:44:42.753Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:12.854Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3dae2d887d2b2d3baa28232b883", "usage": { "input_tokens": 60702, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 60766 }, "turn_token_usage": { "input_tokens": 2323204, "cached_input_tokens": 2264064, "cache_write_input_tokens": 0, "output_tokens": 33030, "reasoning_output_tokens": 12898, "total_tokens": 2356234 }, "thread_token_usage": { "input_tokens": 2323204, "cached_input_tokens": 2264064, "cache_write_input_tokens": 0, "output_tokens": 33030, "reasoning_output_tokens": 12898, "total_tokens": 2356234 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-25T00:44:46.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:46.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:46.512Z", "callId": "call_DN88CuPWGY8HGtTHO6fOTjKT", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:45:16.520Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:46.571Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3faf5c087d28edce423170e6d63", "usage": { "input_tokens": 60840, "cached_input_tokens": 60544, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 60893 }, "turn_token_usage": { "input_tokens": 2384044, "cached_input_tokens": 2324608, "cache_write_input_tokens": 0, "output_tokens": 33083, "reasoning_output_tokens": 12904, "total_tokens": 2417127 }, "thread_token_usage": { "input_tokens": 2384044, "cached_input_tokens": 2324608, "cache_write_input_tokens": 0, "output_tokens": 33083, "reasoning_output_tokens": 12904, "total_tokens": 2417127 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T00:45:17.713Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:17.713Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:18.439Z", "callId": "call_18rKyn1L4Qph5OixqwOwPrz3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:45:48.450Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:18.542Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c41cb3a087d289aed72dd7bc2e4f", "usage": { "input_tokens": 60984, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 61038 }, "turn_token_usage": { "input_tokens": 2445028, "cached_input_tokens": 2385280, "cache_write_input_tokens": 0, "output_tokens": 33137, "reasoning_output_tokens": 12911, "total_tokens": 2478165 }, "thread_token_usage": { "input_tokens": 2445028, "cached_input_tokens": 2385280, "cache_write_input_tokens": 0, "output_tokens": 33137, "reasoning_output_tokens": 12911, "total_tokens": 2478165 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T00:45:52.946Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:52.946Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:45:52.950Z", "text": "The stronger trend decay raises the public score to 0.037047. Longer neural training is still improving contrastive loss substantially (from 6.02 to 5.03 by epoch 12), so I’m letting this run complete before comparing exact rankings; the existing best validation file remains intact." }, { "kind": "tool", "timestamp": "2026-09-25T00:45:52.951Z", "callId": "call_vGtiLmouNDf1noqkGX0CCujG", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:46:22.968Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:53.011Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c43ca8dc87d2a4cb50e872951bca", "usage": { "input_tokens": 61112, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 6, "total_tokens": 61233 }, "turn_token_usage": { "input_tokens": 2506140, "cached_input_tokens": 2446080, "cache_write_input_tokens": 0, "output_tokens": 33258, "reasoning_output_tokens": 12917, "total_tokens": 2539398 }, "thread_token_usage": { "input_tokens": 2506140, "cached_input_tokens": 2446080, "cache_write_input_tokens": 0, "output_tokens": 33258, "reasoning_output_tokens": 12917, "total_tokens": 2539398 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-25T00:46:24.323Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:24.323Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T00:46:24.596Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a6ed944-6692-44af-aa9b-f6430e48e1a7", "process_id": "87853", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\nmodel=tr.train_model(snap,feat,48,15,20260926)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\nmodel=tr.train_model(snap,feat,48,15,20260926)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n", "stderr": "", "aggregated_output": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n", "exit_code": 0, "duration": { "secs": 166, "nanos": 854493071 }, "formatted_output": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T00:46:24.994Z", "callId": "call_RMhDyCnlqyqD2cnbITmYNVNs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:46:25.097Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nRESULT 0.039204681489302264 0.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:25.096Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c45f3f2087d29b50fadaf8c3d817", "usage": { "input_tokens": 61481, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 61537 }, "turn_token_usage": { "input_tokens": 2567621, "cached_input_tokens": 2507008, "cache_write_input_tokens": 0, "output_tokens": 33314, "reasoning_output_tokens": 12926, "total_tokens": 2600935 }, "thread_token_usage": { "input_tokens": 2567621, "cached_input_tokens": 2507008, "cache_write_input_tokens": 0, "output_tokens": 33314, "reasoning_output_tokens": 12926, "total_tokens": 2600935 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-25T00:46:26.396Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:26.396Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:46:27.520Z", "callId": "call_p8A0dxYQHqyJY85m1poghiT9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:46:30.880Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:27.611Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c461587c87d299cf6e60f71bd1a2", "usage": { "input_tokens": 61575, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 9, "total_tokens": 61656 }, "turn_token_usage": { "input_tokens": 2629196, "cached_input_tokens": 2568320, "cache_write_input_tokens": 0, "output_tokens": 33395, "reasoning_output_tokens": 12935, "total_tokens": 2662591 }, "thread_token_usage": { "input_tokens": 2629196, "cached_input_tokens": 2568320, "cache_write_input_tokens": 0, "output_tokens": 33395, "reasoning_output_tokens": 12935, "total_tokens": 2662591 } } }, { "kind": "context", "timestamp": "2026-09-25T00:46:30.875Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f2c000bf-2e93-4ff6-96b5-70495bface60", "process_id": "92536", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 192039919 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-25T00:46:35.076Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:35.076Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:46:38.850Z", "callId": "call_zi3zzdovBv723g21NZ6vL7Hs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def train_model(\\n snapshots: SnapshotArrays,\\n features: np.ndarray,\\n dim: int,\\n epochs: int,\\n seed: int,\\n+ initial_model: TwoTower | None = None,\\n+ learning_rate: float = 2.5e-3,\\n ) -> TwoTower:\\n@@\\n- model = TwoTower(len(features), features.shape[1], dim)\\n- opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ model = initial_model if initial_model is not None else TwoTower(len(features), features.shape[1], dim)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:46:38.949Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:38.858Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2aac2e6a-adae-4413-bac6-2950dbc4ea3c", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -364,2 +364,4 @@\n seed: int,\n+ initial_model: TwoTower | None = None,\n+ learning_rate: float = 2.5e-3,\n ) -> TwoTower:\n@@ -367,4 +369,4 @@\n torch.set_num_threads(min(8, torch.get_num_threads()))\n- model = TwoTower(len(features), features.shape[1], dim)\n- opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n+ model = initial_model if initial_model is not None else TwoTower(len(features), features.shape[1], dim)\n+ opt = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\n feat_t = torch.from_numpy(features)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:46:38.948Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c46727d887d29f836ff6c18b8e30", "usage": { "input_tokens": 61727, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 390, "reasoning_output_tokens": 171, "total_tokens": 62117 }, "turn_token_usage": { "input_tokens": 2690923, "cached_input_tokens": 2629760, "cache_write_input_tokens": 0, "output_tokens": 33785, "reasoning_output_tokens": 13106, "total_tokens": 2724708 }, "thread_token_usage": { "input_tokens": 2690923, "cached_input_tokens": 2629760, "cache_write_input_tokens": 0, "output_tokens": 33785, "reasoning_output_tokens": 13106, "total_tokens": 2724708 } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-25T00:46:40.457Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:40.457Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:46:47.121Z", "callId": "call_AiXyaL6u8seBvj6fQQYH5Sfz", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:46:48.287Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=66792", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:47.246Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c46f4d7087d29e7ad2fb87e0c69b", "usage": { "input_tokens": 62141, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 395, "reasoning_output_tokens": 18, "total_tokens": 62536 }, "turn_token_usage": { "input_tokens": 2753064, "cached_input_tokens": 2691328, "cache_write_input_tokens": 0, "output_tokens": 34180, "reasoning_output_tokens": 13124, "total_tokens": 2787244 }, "thread_token_usage": { "input_tokens": 2753064, "cached_input_tokens": 2691328, "cache_write_input_tokens": 0, "output_tokens": 34180, "reasoning_output_tokens": 13124, "total_tokens": 2787244 } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-25T00:46:49.444Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:49.444Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:46:50.156Z", "callId": "call_smCe19pG92q5PKogCfsJSGC1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:47:20.164Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:50.269Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c47877a487d28fdf65e715fa7b78", "usage": { "input_tokens": 62564, "cached_input_tokens": 61952, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62617 }, "turn_token_usage": { "input_tokens": 2815628, "cached_input_tokens": 2753280, "cache_write_input_tokens": 0, "output_tokens": 34233, "reasoning_output_tokens": 13130, "total_tokens": 2849861 }, "thread_token_usage": { "input_tokens": 2815628, "cached_input_tokens": 2753280, "cache_write_input_tokens": 0, "output_tokens": 34233, "reasoning_output_tokens": 13130, "total_tokens": 2849861 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-25T00:47:21.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:47:21.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:47:22.189Z", "callId": "call_eS7E6xZTDgiBWaQ2Ob4GVcSm", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:47:52.199Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:47:22.323Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4986f1087d2a89d1f43d66130b7", "usage": { "input_tokens": 62674, "cached_input_tokens": 62336, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62727 }, "turn_token_usage": { "input_tokens": 2878302, "cached_input_tokens": 2815616, "cache_write_input_tokens": 0, "output_tokens": 34286, "reasoning_output_tokens": 13136, "total_tokens": 2912588 }, "thread_token_usage": { "input_tokens": 2878302, "cached_input_tokens": 2815616, "cache_write_input_tokens": 0, "output_tokens": 34286, "reasoning_output_tokens": 13136, "total_tokens": 2912588 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-25T00:47:53.463Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:47:53.463Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:47:54.188Z", "callId": "call_8wCBYw86gbmWyo6saCJ7Qduo", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:48:24.197Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:47:54.273Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4b86ef087d2835beb6b1f1f64e8", "usage": { "input_tokens": 62801, "cached_input_tokens": 62464, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62854 }, "turn_token_usage": { "input_tokens": 2941103, "cached_input_tokens": 2878080, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 13142, "total_tokens": 2975442 }, "thread_token_usage": { "input_tokens": 2941103, "cached_input_tokens": 2878080, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 13142, "total_tokens": 2975442 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:48:25.968Z", "callId": "call_uGTLFlX33QpWYOYGSMdKF0py", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:48:47.937Z", "content": "Script completed\nWall time 22.0 seconds\nOutput:\n\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:48:26.072Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4d8648887d292ee9ca54e2e75f8", "usage": { "input_tokens": 62945, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 62990 }, "turn_token_usage": { "input_tokens": 3004048, "cached_input_tokens": 2940672, "cache_write_input_tokens": 0, "output_tokens": 34384, "reasoning_output_tokens": 13142, "total_tokens": 3038432 }, "thread_token_usage": { "input_tokens": 3004048, "cached_input_tokens": 2940672, "cache_write_input_tokens": 0, "output_tokens": 34384, "reasoning_output_tokens": 13142, "total_tokens": 3038432 } } }, { "kind": "context", "timestamp": "2026-09-25T00:48:47.935Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e25973d-6e49-45d3-95d6-94e44196e46c", "process_id": "66792", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n", "stderr": "", "aggregated_output": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n", "exit_code": 0, "duration": { "secs": 120, "nanos": 650113608 }, "formatted_output": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-25T00:48:51.385Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:48:51.385Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:48:55.228Z", "callId": "call_oQ58wfhx3mk8iivUB8CPtxIH", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n+ weights = [forced_weight] if forced_weight is not None else [\\n+ 0.0, 0.035, 0.045, 0.055, 0.065, 0.075, 0.085, 0.095,\\n+ 0.11, 0.13, 0.16, 0.22, 0.35, 0.65, 1.5, 4.0,\\n+ ]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:48:55.322Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:48:55.238Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-854feb29-012f-45dc-8fd1-fbff77190a24", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -573,3 +573,6 @@\n truth = relevant_truth() if stage == \"validation\" else None\n- weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n+ weights = [forced_weight] if forced_weight is not None else [\n+ 0.0, 0.035, 0.045, 0.055, 0.065, 0.075, 0.085, 0.095,\n+ 0.11, 0.13, 0.16, 0.22, 0.35, 0.65, 1.5, 4.0,\n+ ]\n best_score, best_weight = -1.0, None\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:48:55.320Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4f01d6087d29852eb130d67c969", "usage": { "input_tokens": 63219, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 131, "total_tokens": 63572 }, "turn_token_usage": { "input_tokens": 3067267, "cached_input_tokens": 3003392, "cache_write_input_tokens": 0, "output_tokens": 34737, "reasoning_output_tokens": 13273, "total_tokens": 3102004 }, "thread_token_usage": { "input_tokens": 3067267, "cached_input_tokens": 3003392, "cache_write_input_tokens": 0, "output_tokens": 34737, "reasoning_output_tokens": 13273, "total_tokens": 3102004 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-25T00:48:56.810Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:48:56.810Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:49:00.152Z", "callId": "call_I8qCPBI5zmTzhpTisBwv06UJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:49:01.317Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=49342", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:00.259Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4f7907487d2aaf16fe787f63358", "usage": { "input_tokens": 63596, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 15, "total_tokens": 63804 }, "turn_token_usage": { "input_tokens": 3130863, "cached_input_tokens": 3066496, "cache_write_input_tokens": 0, "output_tokens": 34945, "reasoning_output_tokens": 13288, "total_tokens": 3165808 }, "thread_token_usage": { "input_tokens": 3130863, "cached_input_tokens": 3066496, "cache_write_input_tokens": 0, "output_tokens": 34945, "reasoning_output_tokens": 13288, "total_tokens": 3165808 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:49:03.027Z", "callId": "call_31x2Jy9tXODlEWXXaJCxMuC1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":49342,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:49:18.531Z", "content": "Script completed\nWall time 15.5 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:03.118Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4fd796487d2b1dde0d011cc728e", "usage": { "input_tokens": 63832, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 63877 }, "turn_token_usage": { "input_tokens": 3194695, "cached_input_tokens": 3129856, "cache_write_input_tokens": 0, "output_tokens": 34990, "reasoning_output_tokens": 13288, "total_tokens": 3229685 }, "thread_token_usage": { "input_tokens": 3194695, "cached_input_tokens": 3129856, "cache_write_input_tokens": 0, "output_tokens": 34990, "reasoning_output_tokens": 13288, "total_tokens": 3229685 } } }, { "kind": "context", "timestamp": "2026-09-25T00:49:18.529Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-05f03227-4ffc-47c1-b0ee-fe5616210fc3", "process_id": "49342", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 213163041 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-25T00:49:19.838Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:19.838Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:49:21.009Z", "callId": "call_Eyi7pZo9R518y3fJrlFA4EbC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:49:24.410Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:21.110Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c50ebbe887d298e6ab109934d284", "usage": { "input_tokens": 64199, "cached_input_tokens": 63616, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 12, "total_tokens": 64285 }, "turn_token_usage": { "input_tokens": 3258894, "cached_input_tokens": 3193472, "cache_write_input_tokens": 0, "output_tokens": 35076, "reasoning_output_tokens": 13300, "total_tokens": 3293970 }, "thread_token_usage": { "input_tokens": 3258894, "cached_input_tokens": 3193472, "cache_write_input_tokens": 0, "output_tokens": 35076, "reasoning_output_tokens": 13300, "total_tokens": 3293970 } } }, { "kind": "context", "timestamp": "2026-09-25T00:49:24.405Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-066ffc1b-5528-4267-b7df-463fb012b228", "process_id": "11540", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 231056195 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-25T00:49:35.002Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:35.002Z", "text": "" } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-25T00:49:44.327Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:44.327Z", "text": "" } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-25T00:49:49.232Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:49.232Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:49:50.981Z", "text": "The 25-epoch checkpoint reaches 0.039651 at a finely tuned coefficient of 0.065. The next issue is objective noise: another row’s sampled item can also be a known positive for the current history, yet ordinary in-batch softmax treats it as negative. I’m masking those known positives and fine-tuning the existing checkpoint, a targeted change that preserves the current best if it does not help." }, { "kind": "tool", "timestamp": "2026-09-25T00:49:55.179Z", "callId": "call_h9VJpyCGvpcnOTSmYmumasqz", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\\n- # A duplicated positive in the batch is another positive, not a negative.\\n- same = ti[:, None].eq(ti[None, :])\\n- same.fill_diagonal_(False)\\n- logits = logits.masked_fill(same, -1e4)\\n+ # Every stored future target is a positive. Mask any that another\\n+ # row happened to sample, rather than creating false negatives.\\n+ row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\\n+ known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\\n+ known_positive.fill_diagonal_(False)\\n+ logits = logits.masked_fill(known_positive, -1e4)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:49:55.305Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:55.188Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-86287142-3d97-44fa-8581-db6671286353", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -398,6 +398,8 @@\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n- # A duplicated positive in the batch is another positive, not a negative.\n- same = ti[:, None].eq(ti[None, :])\n- same.fill_diagonal_(False)\n- logits = logits.masked_fill(same, -1e4)\n+ # Every stored future target is a positive. Mask any that another\n+ # row happened to sample, rather than creating false negatives.\n+ row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\n+ known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\n+ known_positive.fill_diagonal_(False)\n+ logits = logits.masked_fill(known_positive, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:49:55.304Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c514a64887d2b8dba6c10a729642", "usage": { "input_tokens": 64358, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 1639, "reasoning_output_tokens": 1305, "total_tokens": 65997 }, "turn_token_usage": { "input_tokens": 3323252, "cached_input_tokens": 3257472, "cache_write_input_tokens": 0, "output_tokens": 36715, "reasoning_output_tokens": 14605, "total_tokens": 3359967 }, "thread_token_usage": { "input_tokens": 3323252, "cached_input_tokens": 3257472, "cache_write_input_tokens": 0, "output_tokens": 36715, "reasoning_output_tokens": 14605, "total_tokens": 3359967 } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-25T00:49:57.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:57.152Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:50:03.736Z", "callId": "call_j4u4fs1qsSoDCM1ZWwAN2lYP", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:50:04.902Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=88406", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:50:03.882Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5338f7487d286714eda52e93ded", "usage": { "input_tokens": 66021, "cached_input_tokens": 64128, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 19, "total_tokens": 66414 }, "turn_token_usage": { "input_tokens": 3389273, "cached_input_tokens": 3321600, "cache_write_input_tokens": 0, "output_tokens": 37108, "reasoning_output_tokens": 14624, "total_tokens": 3426381 }, "thread_token_usage": { "input_tokens": 3389273, "cached_input_tokens": 3321600, "cache_write_input_tokens": 0, "output_tokens": 37108, "reasoning_output_tokens": 14624, "total_tokens": 3426381 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-25T00:50:06.204Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:06.204Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:50:06.884Z", "callId": "call_Fw7oVkanfAzSqJLhgDMYmsM1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:50:36.893Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:50:06.993Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c53d17e087d29df5f8f87a607e49", "usage": { "input_tokens": 66442, "cached_input_tokens": 65792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66495 }, "turn_token_usage": { "input_tokens": 3455715, "cached_input_tokens": 3387392, "cache_write_input_tokens": 0, "output_tokens": 37161, "reasoning_output_tokens": 14630, "total_tokens": 3492876 }, "thread_token_usage": { "input_tokens": 3455715, "cached_input_tokens": 3387392, "cache_write_input_tokens": 0, "output_tokens": 37161, "reasoning_output_tokens": 14630, "total_tokens": 3492876 } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-25T00:50:38.192Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:38.192Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:50:38.883Z", "callId": "call_rBLyvIO1QjzuDIB60MRracJt", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:51:08.893Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:50:39.003Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c55d24ac87d290ad2f71e7f2dfdf", "usage": { "input_tokens": 66552, "cached_input_tokens": 66304, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 66607 }, "turn_token_usage": { "input_tokens": 3522267, "cached_input_tokens": 3453696, "cache_write_input_tokens": 0, "output_tokens": 37216, "reasoning_output_tokens": 14638, "total_tokens": 3559483 }, "thread_token_usage": { "input_tokens": 3522267, "cached_input_tokens": 3453696, "cache_write_input_tokens": 0, "output_tokens": 37216, "reasoning_output_tokens": 14638, "total_tokens": 3559483 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:51:10.663Z", "callId": "call_PrGV9jZk8OTpvL2Xg1gmJEXj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:51:40.672Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:51:10.759Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c57d114487d2a34db42e8af0c158", "usage": { "input_tokens": 66681, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 66726 }, "turn_token_usage": { "input_tokens": 3588948, "cached_input_tokens": 3520128, "cache_write_input_tokens": 0, "output_tokens": 37261, "reasoning_output_tokens": 14638, "total_tokens": 3626209 }, "thread_token_usage": { "input_tokens": 3588948, "cached_input_tokens": 3520128, "cache_write_input_tokens": 0, "output_tokens": 37261, "reasoning_output_tokens": 14638, "total_tokens": 3626209 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-25T00:51:41.860Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:51:41.860Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:51:42.562Z", "callId": "call_Y5H8GPDStKFMiHtZAIb4QTAm", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:51:56.691Z", "content": "Script completed\nWall time 14.1 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:51:42.669Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c59cece887d29ea4711b09062601", "usage": { "input_tokens": 66800, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 66856 }, "turn_token_usage": { "input_tokens": 3655748, "cached_input_tokens": 3586688, "cache_write_input_tokens": 0, "output_tokens": 37317, "reasoning_output_tokens": 14647, "total_tokens": 3693065 }, "thread_token_usage": { "input_tokens": 3655748, "cached_input_tokens": 3586688, "cache_write_input_tokens": 0, "output_tokens": 37317, "reasoning_output_tokens": 14647, "total_tokens": 3693065 } } }, { "kind": "context", "timestamp": "2026-09-25T00:51:56.689Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-81b48320-8ebb-4b3e-b619-5d7da791959c", "process_id": "88406", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n", "stderr": "", "aggregated_output": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n", "exit_code": 0, "duration": { "secs": 112, "nanos": 788365233 }, "formatted_output": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n" } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-25T00:52:03.561Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:52:03.561Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:52:13.963Z", "callId": "call_Ns2tKji9GBwMoa2Aw1a66bdq", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\\n@@\\n return out\\n+\\n+\\n+def _recent_unique_positions(items: np.ndarray, end: int, length: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Positions of the most recent distinct items, in chronological order.\\\"\\\"\\\"\\n+ start = max(0, end - 3000)\\n+ reverse = items[start:end][::-1]\\n+ if not len(reverse):\\n+ return np.empty(0, dtype=np.int64)\\n+ _, first_from_end = np.unique(reverse, return_index=True)\\n+ positions = start + (len(reverse) - 1 - first_from_end)\\n+ return np.sort(positions)[-length:]\\n@@\\n- hs = max(0, hp - HISTORY_LEN)\\n- hist_i = pitems[hs:hp]\\n- hist_t = pts[hs:hp]\\n+ selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ hist_i = pitems[selected]\\n+ hist_t = pts[selected]\\n@@\\n- raw_w *= 1.0 + 0.25 * porg[hs:hp]\\n- raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\\n+ raw_w *= 1.0 + 0.25 * porg[selected]\\n+ raw_w *= np.sqrt(np.minimum(pratio[selected], 120) / 50.0)\\n@@\\n- recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\\n+ recent_org = float(porg[selected].mean()) if len(selected) else 0.0\\n@@\\n- all_positive = np.flatnonzero(ratio[:end] >= 50)\\n- positive = all_positive[-HISTORY_LEN:]\\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\\n+ chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ positive = all_positive[chosen_within_positive]\\n@@\\n- cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w.npz\\\"\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_unique.npz\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:52:14.070Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:52:13.973Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d8e93e24-f785-4c7e-9282-e0a963013710", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -114,2 +114,13 @@\n \n+def _recent_unique_positions(items: np.ndarray, end: int, length: int) -> np.ndarray:\n+ \"\"\"Positions of the most recent distinct items, in chronological order.\"\"\"\n+ start = max(0, end - 3000)\n+ reverse = items[start:end][::-1]\n+ if not len(reverse):\n+ return np.empty(0, dtype=np.int64)\n+ _, first_from_end = np.unique(reverse, return_index=True)\n+ positions = start + (len(reverse) - 1 - first_from_end)\n+ return np.sort(positions)[-length:]\n+\n+\n def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n@@ -223,9 +234,9 @@\n continue\n- hs = max(0, hp - HISTORY_LEN)\n- hist_i = pitems[hs:hp]\n- hist_t = pts[hs:hp]\n+ selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ hist_i = pitems[selected]\n+ hist_t = pts[selected]\n # Smoothly favor recent, organic and deeply completed history.\n raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\n- raw_w *= 1.0 + 0.25 * porg[hs:hp]\n- raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\n+ raw_w *= 1.0 + 0.25 * porg[selected]\n+ raw_w *= np.sqrt(np.minimum(pratio[selected], 120) / 50.0)\n raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\n@@ -260,3 +271,3 @@\n lf = math.log1p(hp) / 10.0\n- recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\n+ recent_org = float(porg[selected].mean()) if len(selected) else 0.0\n features = np.asarray(\n@@ -454,3 +465,4 @@\n all_positive = np.flatnonzero(ratio[:end] >= 50)\n- positive = all_positive[-HISTORY_LEN:]\n+ chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ positive = all_positive[chosen_within_positive]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n@@ -648,3 +660,3 @@\n features = item_features(catalog)\n- cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w.npz\"\n+ cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n if cache.exists() and not args.rebuild_cache:\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:52:14.068Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5ace39887d28e12e6fc43a3bf77", "usage": { "input_tokens": 67178, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 873, "reasoning_output_tokens": 287, "total_tokens": 68051 }, "turn_token_usage": { "input_tokens": 3722926, "cached_input_tokens": 3653248, "cache_write_input_tokens": 0, "output_tokens": 38190, "reasoning_output_tokens": 14934, "total_tokens": 3761116 }, "thread_token_usage": { "input_tokens": 3722926, "cached_input_tokens": 3653248, "cache_write_input_tokens": 0, "output_tokens": 38190, "reasoning_output_tokens": 14934, "total_tokens": 3761116 } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-25T00:52:19.247Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:52:19.247Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:52:19.248Z", "callId": "call_ExWnD6ctMQI2xgdUH9jkqq1f", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"rg '\\\\bhs\\\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:52:19.418Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:52:19.321Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5be4cbc87d290da26f611c2ec04", "usage": { "input_tokens": 68075, "cached_input_tokens": 66944, "cache_write_input_tokens": 0, "output_tokens": 102, "reasoning_output_tokens": 12, "total_tokens": 68177 }, "turn_token_usage": { "input_tokens": 3791001, "cached_input_tokens": 3720192, "cache_write_input_tokens": 0, "output_tokens": 38292, "reasoning_output_tokens": 14946, "total_tokens": 3829293 }, "thread_token_usage": { "input_tokens": 3791001, "cached_input_tokens": 3720192, "cache_write_input_tokens": 0, "output_tokens": 38292, "reasoning_output_tokens": 14946, "total_tokens": 3829293 } } }, { "kind": "context", "timestamp": "2026-09-25T00:52:19.414Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9a67ba7d-c0dd-452b-8b80-c6b43a621688", "process_id": "3964", "command": [ "/bin/sh", "-lc", "rg '\\bhs\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "rg '\\bhs\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n", "stderr": "", "aggregated_output": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4530 }, "formatted_output": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n" } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-25T00:52:21.658Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:52:21.658Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:52:25.284Z", "callId": "call_OqNXtMBNUm4cW1MxOXwLq33n", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- # Every stored future target is a positive. Mask any that another\\n- # row happened to sample, rather than creating false negatives.\\n- row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\\n- known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\\n- known_positive.fill_diagonal_(False)\\n- logits = logits.masked_fill(known_positive, -1e4)\\n+ # A duplicated sampled positive is another positive, not a negative.\\n+ same = ti[:, None].eq(ti[None, :])\\n+ same.fill_diagonal_(False)\\n+ logits = logits.masked_fill(same, -1e4)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:52:26.781Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:52:25.293Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f87800a3-a1e0-4d26-bb33-f5977e666dc9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -409,8 +409,6 @@\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n- # Every stored future target is a positive. Mask any that another\n- # row happened to sample, rather than creating false negatives.\n- row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\n- known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\n- known_positive.fill_diagonal_(False)\n- logits = logits.masked_fill(known_positive, -1e4)\n+ # A duplicated sampled positive is another positive, not a negative.\n+ same = ti[:, None].eq(ti[None, :])\n+ same.fill_diagonal_(False)\n+ logits = logits.masked_fill(same, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:52:26.780Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5c39b3087d2b5dacc22a5fe0140", "usage": { "input_tokens": 68222, "cached_input_tokens": 67840, "cache_write_input_tokens": 0, "output_tokens": 269, "reasoning_output_tokens": 51, "total_tokens": 68491 }, "turn_token_usage": { "input_tokens": 3859223, "cached_input_tokens": 3788032, "cache_write_input_tokens": 0, "output_tokens": 38561, "reasoning_output_tokens": 14997, "total_tokens": 3897784 }, "thread_token_usage": { "input_tokens": 3859223, "cached_input_tokens": 3788032, "cache_write_input_tokens": 0, "output_tokens": 38561, "reasoning_output_tokens": 14997, "total_tokens": 3897784 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-25T00:52:28.682Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:52:28.682Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:52:35.850Z", "callId": "call_Q5chimePkOSgG0h1lTZJ5jf2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\\nelse:\\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:52:37.016Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=7881", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:52:35.934Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5cb17e087d28b66e1c4309cbfe5", "usage": { "input_tokens": 68515, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 436, "reasoning_output_tokens": 31, "total_tokens": 68951 }, "turn_token_usage": { "input_tokens": 3927738, "cached_input_tokens": 3856128, "cache_write_input_tokens": 0, "output_tokens": 38997, "reasoning_output_tokens": 15028, "total_tokens": 3966735 }, "thread_token_usage": { "input_tokens": 3927738, "cached_input_tokens": 3856128, "cache_write_input_tokens": 0, "output_tokens": 38997, "reasoning_output_tokens": 15028, "total_tokens": 3966735 } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-25T00:52:38.386Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:52:38.386Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:52:39.101Z", "callId": "call_1adzBAo0nZ3hUjYMAp9Uo5C8", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:53:09.110Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nloading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:52:39.206Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5d5329c87d287fcde01454e8b85", "usage": { "input_tokens": 68979, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 69032 }, "turn_token_usage": { "input_tokens": 3996717, "cached_input_tokens": 3924480, "cache_write_input_tokens": 0, "output_tokens": 39050, "reasoning_output_tokens": 15034, "total_tokens": 4035767 }, "thread_token_usage": { "input_tokens": 3996717, "cached_input_tokens": 3924480, "cache_write_input_tokens": 0, "output_tokens": 39050, "reasoning_output_tokens": 15034, "total_tokens": 4035767 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-25T00:53:10.240Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:53:10.240Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:53:10.930Z", "callId": "call_tgYOfr08LTe1A7tIV2JdSFiP", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:53:40.939Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:53:11.047Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5f54f3c87d2a2c3519315ca8cb8", "usage": { "input_tokens": 69104, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 69159 }, "turn_token_usage": { "input_tokens": 4065821, "cached_input_tokens": 3993344, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 15042, "total_tokens": 4104926 }, "thread_token_usage": { "input_tokens": 4065821, "cached_input_tokens": 3993344, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 15042, "total_tokens": 4104926 } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-25T00:53:47.784Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:53:47.784Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:53:48.481Z", "callId": "call_T5ZwhFF2a5xb63I4In4ucPeO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:54:18.490Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:53:48.593Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c615201887d28b0235ac36240c09", "usage": { "input_tokens": 69230, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 69288 }, "turn_token_usage": { "input_tokens": 4135051, "cached_input_tokens": 4062208, "cache_write_input_tokens": 0, "output_tokens": 39163, "reasoning_output_tokens": 15053, "total_tokens": 4174214 }, "thread_token_usage": { "input_tokens": 4135051, "cached_input_tokens": 4062208, "cache_write_input_tokens": 0, "output_tokens": 39163, "reasoning_output_tokens": 15053, "total_tokens": 4174214 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:54:20.441Z", "callId": "call_2qeYa4AmU215BecYQRsTzGTH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:54:50.451Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:20.549Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c63ab09087d2beaaea6e5378ca14", "usage": { "input_tokens": 69362, "cached_input_tokens": 68992, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 69407 }, "turn_token_usage": { "input_tokens": 4204413, "cached_input_tokens": 4131200, "cache_write_input_tokens": 0, "output_tokens": 39208, "reasoning_output_tokens": 15053, "total_tokens": 4243621 }, "thread_token_usage": { "input_tokens": 4204413, "cached_input_tokens": 4131200, "cache_write_input_tokens": 0, "output_tokens": 39208, "reasoning_output_tokens": 15053, "total_tokens": 4243621 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:54:53.392Z", "callId": "call_VtGt9Vi0WdCJ1JpSzxvrGT3k", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:55:14.575Z", "content": "Script completed\nWall time 21.2 seconds\nOutput:\n\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:53.519Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c65aa4f487d2b251f84d466b024a", "usage": { "input_tokens": 69498, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 69543 }, "turn_token_usage": { "input_tokens": 4273911, "cached_input_tokens": 4200320, "cache_write_input_tokens": 0, "output_tokens": 39253, "reasoning_output_tokens": 15053, "total_tokens": 4313164 }, "thread_token_usage": { "input_tokens": 4273911, "cached_input_tokens": 4200320, "cache_write_input_tokens": 0, "output_tokens": 39253, "reasoning_output_tokens": 15053, "total_tokens": 4313164 } } }, { "kind": "context", "timestamp": "2026-09-25T00:55:14.573Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27f9cd67-f190-4137-aece-21cc6593060e", "process_id": "7881", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\nelse:\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\nelse:\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n", "stderr": "", "aggregated_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n", "exit_code": 0, "duration": { "secs": 158, "nanos": 558392347 }, "formatted_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n" } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-25T00:55:16.462Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:55:16.462Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:55:23.017Z", "callId": "call_Ib6ZKulcEzYWnQI6M8MDvorr", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:55:24.197Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=76349", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:55:23.141Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c672cbcc87d2bdd26c6262729cc4", "usage": { "input_tokens": 69882, "cached_input_tokens": 69376, "cache_write_input_tokens": 0, "output_tokens": 414, "reasoning_output_tokens": 41, "total_tokens": 70296 }, "turn_token_usage": { "input_tokens": 4343793, "cached_input_tokens": 4269696, "cache_write_input_tokens": 0, "output_tokens": 39667, "reasoning_output_tokens": 15094, "total_tokens": 4383460 }, "thread_token_usage": { "input_tokens": 4343793, "cached_input_tokens": 4269696, "cache_write_input_tokens": 0, "output_tokens": 39667, "reasoning_output_tokens": 15094, "total_tokens": 4383460 } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-25T00:55:25.397Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:55:25.397Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:55:26.088Z", "callId": "call_fWTBxmGsLBjsfF40M22LV5Oz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:55:56.096Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:55:26.218Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c67c6a3087d2abfa663b6e6276a1", "usage": { "input_tokens": 70324, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70377 }, "turn_token_usage": { "input_tokens": 4414117, "cached_input_tokens": 4339456, "cache_write_input_tokens": 0, "output_tokens": 39720, "reasoning_output_tokens": 15100, "total_tokens": 4453837 }, "thread_token_usage": { "input_tokens": 4414117, "cached_input_tokens": 4339456, "cache_write_input_tokens": 0, "output_tokens": 39720, "reasoning_output_tokens": 15100, "total_tokens": 4453837 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-25T00:55:57.361Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:55:57.361Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:55:58.070Z", "callId": "call_wRhKZeYKzvDiUS6RckW7Bh9T", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:56:28.082Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:55:58.186Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c69c4db087d2b197b9eb1f60d086", "usage": { "input_tokens": 70434, "cached_input_tokens": 70144, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70487 }, "turn_token_usage": { "input_tokens": 4484551, "cached_input_tokens": 4409600, "cache_write_input_tokens": 0, "output_tokens": 39773, "reasoning_output_tokens": 15106, "total_tokens": 4524324 }, "thread_token_usage": { "input_tokens": 4484551, "cached_input_tokens": 4409600, "cache_write_input_tokens": 0, "output_tokens": 39773, "reasoning_output_tokens": 15106, "total_tokens": 4524324 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:56:30.111Z", "callId": "call_aOWXXQVD28O8Yp5OoaVh7EJw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:57:00.120Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:56:30.216Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6bc521887d2b99e5e81bfd1a23b", "usage": { "input_tokens": 70578, "cached_input_tokens": 70272, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 70623 }, "turn_token_usage": { "input_tokens": 4555129, "cached_input_tokens": 4479872, "cache_write_input_tokens": 0, "output_tokens": 39818, "reasoning_output_tokens": 15106, "total_tokens": 4594947 }, "thread_token_usage": { "input_tokens": 4555129, "cached_input_tokens": 4479872, "cache_write_input_tokens": 0, "output_tokens": 39818, "reasoning_output_tokens": 15106, "total_tokens": 4594947 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-25T00:57:04.685Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:57:04.685Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:57:04.700Z", "callId": "call_ntcLoJc2Hq8Si5yUyQayE6o9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:57:05.892Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:57:04.767Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6dc48a087d2b503363770957c7f", "usage": { "input_tokens": 70680, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 70734 }, "turn_token_usage": { "input_tokens": 4625809, "cached_input_tokens": 4550272, "cache_write_input_tokens": 0, "output_tokens": 39872, "reasoning_output_tokens": 15113, "total_tokens": 4665681 }, "thread_token_usage": { "input_tokens": 4625809, "cached_input_tokens": 4550272, "cache_write_input_tokens": 0, "output_tokens": 39872, "reasoning_output_tokens": 15113, "total_tokens": 4665681 } } }, { "kind": "context", "timestamp": "2026-09-25T00:57:05.890Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be01cdce-65e9-4f40-9cd3-3f9611715adf", "process_id": "76349", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n", "stderr": "", "aggregated_output": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n", "exit_code": 0, "duration": { "secs": 102, "nanos": 694268040 }, "formatted_output": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n" } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-25T00:57:10.883Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:57:10.883Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:57:12.096Z", "text": "Using distinct recent tracks improves the best score again to 0.039965, while further fitting starts to flatten. I’m testing a single-dot-product ensemble by concatenating the raw-history and distinct-history embedding spaces; this often stabilizes collaborative retrieval and remains exactly MIPS-compatible." }, { "kind": "tool", "timestamp": "2026-09-25T00:57:24.355Z", "callId": "call_7SVYBy8Yu7ZJnXglakoWL3Pb", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\\n for l,r in enumerate(range(st,en)):\\n if int(uids[r]) not in truth: continue\\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\\n for g in gs:\\n b=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\\nbest=[]\\nfor k,v in vals.items(): best.append((np.mean(v),*k))\\nbest.sort(reverse=True)\\nfor x in best[:20]: print(x)\\n# exact save for winner\\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:57:25.521Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=53453", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:57:24.467Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6e21fac87d28313646e41c726ec", "usage": { "input_tokens": 71056, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 964, "reasoning_output_tokens": 214, "total_tokens": 72020 }, "turn_token_usage": { "input_tokens": 4696865, "cached_input_tokens": 4620800, "cache_write_input_tokens": 0, "output_tokens": 40836, "reasoning_output_tokens": 15327, "total_tokens": 4737701 }, "thread_token_usage": { "input_tokens": 4696865, "cached_input_tokens": 4620800, "cache_write_input_tokens": 0, "output_tokens": 40836, "reasoning_output_tokens": 15327, "total_tokens": 4737701 } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-25T00:57:26.743Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:57:26.743Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:57:27.449Z", "callId": "call_vsfLoKoFs6JtjkiK4fOnRxTm", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":53453,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:57:54.489Z", "content": "Script completed\nWall time 27.0 seconds\nOutput:\n\n(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:57:27.552Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6f5be6087d283920c05e9f539c6", "usage": { "input_tokens": 72048, "cached_input_tokens": 70912, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 72101 }, "turn_token_usage": { "input_tokens": 4768913, "cached_input_tokens": 4691712, "cache_write_input_tokens": 0, "output_tokens": 40889, "reasoning_output_tokens": 15333, "total_tokens": 4809802 }, "thread_token_usage": { "input_tokens": 4768913, "cached_input_tokens": 4691712, "cache_write_input_tokens": 0, "output_tokens": 40889, "reasoning_output_tokens": 15333, "total_tokens": 4809802 } } }, { "kind": "context", "timestamp": "2026-09-25T00:57:54.487Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a146f467-8958-4e58-8677-511d02705846", "process_id": "53453", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\n for l,r in enumerate(range(st,en)):\n if int(uids[r]) not in truth: continue\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\nbest=[]\nfor k,v in vals.items(): best.append((np.mean(v),*k))\nbest.sort(reverse=True)\nfor x in best[:20]: print(x)\n# exact save for winner\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\n for l,r in enumerate(range(st,en)):\n if int(uids[r]) not in truth: continue\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\nbest=[]\nfor k,v in vals.items(): best.append((np.mean(v),*k))\nbest.sort(reverse=True)\nfor x in best[:20]: print(x)\n# exact save for winner\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n", "stderr": "", "aggregated_output": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n", "exit_code": 1, "duration": { "secs": 29, "nanos": 967162764 }, "formatted_output": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n" } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-25T00:58:00.411Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:58:00.411Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:58:04.838Z", "callId": "call_culpXk7RjQdQepGAU5KCgy00", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if truth is not None:\\n+ if truth is not None and len(weights) > 1:\\n for w in weights:\\n@@\\n best_score, best_weight = q, float(w)\\n+ elif truth is not None:\\n+ best_weight = float(weights[0])\\n else:\\n best_weight = float(weights[0])\\n@@\\n order = np.argsort(-part_score, axis=1)\\n best_top[start:end] = np.take_along_axis(part, order, axis=1)\\n+\\n+ if truth is not None and len(weights) == 1:\\n+ exact_quality = [\\n+ ndcg10(eligible_items[best_top[row]], truth[int(uid)])\\n+ for row, uid in enumerate(targets)\\n+ if int(uid) in truth\\n+ ]\\n+ best_score = float(np.mean(exact_quality))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:58:04.966Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:58:04.846Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-94176782-2740-4824-8f43-529139693a0a", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -611,3 +611,3 @@\n quality[float(w)].append(ndcg10(eligible_items[top], gold))\n- if truth is not None:\n+ if truth is not None and len(weights) > 1:\n for w in weights:\n@@ -617,2 +617,4 @@\n best_score, best_weight = q, float(w)\n+ elif truth is not None:\n+ best_weight = float(weights[0])\n else:\n@@ -632,2 +634,10 @@\n \n+ if truth is not None and len(weights) == 1:\n+ exact_quality = [\n+ ndcg10(eligible_items[best_top[row]], truth[int(uid)])\n+ for row, uid in enumerate(targets)\n+ if int(uid) in truth\n+ ]\n+ best_score = float(np.mean(exact_quality))\n+\n frame = pd.DataFrame(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:58:04.964Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c712b55487d283a616850acb1e4e", "usage": { "input_tokens": 72765, "cached_input_tokens": 71808, "cache_write_input_tokens": 0, "output_tokens": 414, "reasoning_output_tokens": 159, "total_tokens": 73179 }, "turn_token_usage": { "input_tokens": 4841678, "cached_input_tokens": 4763520, "cache_write_input_tokens": 0, "output_tokens": 41303, "reasoning_output_tokens": 15492, "total_tokens": 4882981 }, "thread_token_usage": { "input_tokens": 4841678, "cached_input_tokens": 4763520, "cache_write_input_tokens": 0, "output_tokens": 41303, "reasoning_output_tokens": 15492, "total_tokens": 4882981 } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-25T00:58:06.392Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:58:06.392Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:58:11.244Z", "callId": "call_6PIlg3jWdCCwClYVAHHLv5tQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:58:12.410Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=96585", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:58:11.374Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c71d467887d297bac10836146ecb", "usage": { "input_tokens": 73203, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 293, "reasoning_output_tokens": 16, "total_tokens": 73496 }, "turn_token_usage": { "input_tokens": 4914881, "cached_input_tokens": 4836096, "cache_write_input_tokens": 0, "output_tokens": 41596, "reasoning_output_tokens": 15508, "total_tokens": 4956477 }, "thread_token_usage": { "input_tokens": 4914881, "cached_input_tokens": 4836096, "cache_write_input_tokens": 0, "output_tokens": 41596, "reasoning_output_tokens": 15508, "total_tokens": 4956477 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:58:14.950Z", "callId": "call_vsEj970ADJILjjxMEg7gg4Za", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":96585,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:58:22.842Z", "content": "Script completed\nWall time 7.9 seconds\nOutput:\n\nRESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:58:15.071Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c724939c87d28f55087e916a4a7a", "usage": { "input_tokens": 73524, "cached_input_tokens": 73088, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 73569 }, "turn_token_usage": { "input_tokens": 4988405, "cached_input_tokens": 4909184, "cache_write_input_tokens": 0, "output_tokens": 41641, "reasoning_output_tokens": 15508, "total_tokens": 5030046 }, "thread_token_usage": { "input_tokens": 4988405, "cached_input_tokens": 4909184, "cache_write_input_tokens": 0, "output_tokens": 41641, "reasoning_output_tokens": 15508, "total_tokens": 5030046 } } }, { "kind": "context", "timestamp": "2026-09-25T00:58:22.841Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bace8d21-dff3-46c0-a8d3-f48ac693cec7", "process_id": "96585", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 431791721 }, "formatted_output": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-25T00:58:34.604Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:58:34.604Z", "text": "" } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-25T00:58:39.710Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:58:39.710Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:58:52.533Z", "callId": "call_Ob7W4o2bzQ7MskjAuASdmlPq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,r in enumerate(range(st,en)):\\n gold=truth.get(int(uids[r]));\\n if not gold: continue\\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\\n for w in ws:\\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\\n rows.append((r,*vals))\\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\\nfor col in ['recent','history','ratio','breadth']:\\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\\n total=0\\n print('\\\\n',col)\\n for b,ind in feat.groupby('bin').groups.items():\\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\\n print('oracle-bin',total/len(feat))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:58:53.699Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=68069", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:58:52.667Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c72f03e087d289a3eff43e590ba2", "usage": { "input_tokens": 73654, "cached_input_tokens": 73344, "cache_write_input_tokens": 0, "output_tokens": 1551, "reasoning_output_tokens": 777, "total_tokens": 75205 }, "turn_token_usage": { "input_tokens": 5062059, "cached_input_tokens": 4982528, "cache_write_input_tokens": 0, "output_tokens": 43192, "reasoning_output_tokens": 16285, "total_tokens": 5105251 }, "thread_token_usage": { "input_tokens": 5062059, "cached_input_tokens": 4982528, "cache_write_input_tokens": 0, "output_tokens": 43192, "reasoning_output_tokens": 16285, "total_tokens": 5105251 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:58:55.441Z", "callId": "call_3292xXuJeXw9b3MeGMhKTpeq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":68069,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:59:06.528Z", "content": "Script completed\nWall time 11.1 seconds\nOutput:\n\nglobal [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:58:55.533Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c74de21487d2af0d75b137e64b87", "usage": { "input_tokens": 75233, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 75278 }, "turn_token_usage": { "input_tokens": 5137292, "cached_input_tokens": 5056000, "cache_write_input_tokens": 0, "output_tokens": 43237, "reasoning_output_tokens": 16285, "total_tokens": 5180529 }, "thread_token_usage": { "input_tokens": 5137292, "cached_input_tokens": 5056000, "cache_write_input_tokens": 0, "output_tokens": 43237, "reasoning_output_tokens": 16285, "total_tokens": 5180529 } } }, { "kind": "context", "timestamp": "2026-09-25T00:59:06.526Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50dcb3c1-061f-4a99-aca6-6769f9da20d4", "process_id": "68069", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\n for w in ws:\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\n rows.append((r,*vals))\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\nfor col in ['recent','history','ratio','breadth']:\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\n total=0\n print('\\n',col)\n for b,ind in feat.groupby('bin').groups.items():\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\n print('oracle-bin',total/len(feat))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\n for w in ws:\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\n rows.append((r,*vals))\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\nfor col in ['recent','history','ratio','breadth']:\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\n total=0\n print('\\n',col)\n for b,ind in feat.groupby('bin').groups.items():\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\n print('oracle-bin',total/len(feat))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n", "stderr": "", "aggregated_output": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 828627583 }, "formatted_output": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n" } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-25T00:59:16.869Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:16.869Z", "text": "" } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-25T00:59:26.192Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:26.192Z", "text": "" } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-25T00:59:29.929Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:29.929Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:59:39.707Z", "callId": "call_pqM6dZV4mgDMJg5VQ5t2Vidj", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\\n+def build_snapshots(\\n+ stage: str,\\n+ catalog_ids: np.ndarray,\\n+ weeks: int = 30,\\n+ unique_history: bool = True,\\n+) -> SnapshotArrays:\\n@@\\n- selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ selected = (\\n+ _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ if unique_history\\n+ else np.arange(max(0, hp - HISTORY_LEN), hp, dtype=np.int64)\\n+ )\\n@@\\n-def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\\n+def build_inference_users(\\n+ stage: str,\\n+ catalog_ids: np.ndarray,\\n+ unique_history: bool = True,\\n+) -> tuple[np.ndarray, ...]:\\n@@\\n- chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ chosen_within_positive = (\\n+ _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ if unique_history\\n+ else np.arange(max(0, len(all_positive) - HISTORY_LEN), len(all_positive), dtype=np.int64)\\n+ )\\n@@\\n parser.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ parser.add_argument(\\\"--history-mode\\\", choices=[\\\"unique\\\", \\\"raw\\\"], default=\\\"unique\\\")\\n@@\\n- cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_unique.npz\\\"\\n+ unique_history = args.history_mode == \\\"unique\\\"\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_{args.history_mode}.npz\\\"\\n@@\\n- snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks, unique_history)\\n@@\\n- users = build_inference_users(args.stage, catalog_ids)\\n+ users = build_inference_users(args.stage, catalog_ids, unique_history)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:59:39.829Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:59:39.715Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c59e482c-9021-4b77-9ef4-7384e3017421", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -125,3 +125,8 @@\n \n-def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n+def build_snapshots(\n+ stage: str,\n+ catalog_ids: np.ndarray,\n+ weeks: int = 30,\n+ unique_history: bool = True,\n+) -> SnapshotArrays:\n \"\"\"Create cutoff-correct rolling history -> organic-discovery examples.\"\"\"\n@@ -234,3 +239,7 @@\n continue\n- selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ selected = (\n+ _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ if unique_history\n+ else np.arange(max(0, hp - HISTORY_LEN), hp, dtype=np.int64)\n+ )\n hist_i = pitems[selected]\n@@ -427,3 +436,7 @@\n \n-def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\n+def build_inference_users(\n+ stage: str,\n+ catalog_ids: np.ndarray,\n+ unique_history: bool = True,\n+) -> tuple[np.ndarray, ...]:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -463,3 +476,7 @@\n all_positive = np.flatnonzero(ratio[:end] >= 50)\n- chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ chosen_within_positive = (\n+ _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ if unique_history\n+ else np.arange(max(0, len(all_positive) - HISTORY_LEN), len(all_positive), dtype=np.int64)\n+ )\n positive = all_positive[chosen_within_positive]\n@@ -660,2 +677,3 @@\n parser.add_argument(\"--seed\", type=int, default=20260925)\n+ parser.add_argument(\"--history-mode\", choices=[\"unique\", \"raw\"], default=\"unique\")\n parser.add_argument(\"--trend-weight\", type=float)\n@@ -668,3 +686,4 @@\n features = item_features(catalog)\n- cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n+ unique_history = args.history_mode == \"unique\"\n+ cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_{args.history_mode}.npz\"\n if cache.exists() and not args.rebuild_cache:\n@@ -673,3 +692,3 @@\n else:\n- snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks, unique_history)\n snapshots.save(cache)\n@@ -687,3 +706,3 @@\n )\n- users = build_inference_users(args.stage, catalog_ids)\n+ users = build_inference_users(args.stage, catalog_ids, unique_history)\n ue, ie = encode(model, users, features)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:59:39.827Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c75ac7d487d288556eba72b31102", "usage": { "input_tokens": 76207, "cached_input_tokens": 75008, "cache_write_input_tokens": 0, "output_tokens": 1787, "reasoning_output_tokens": 1235, "total_tokens": 77994 }, "turn_token_usage": { "input_tokens": 5213499, "cached_input_tokens": 5131008, "cache_write_input_tokens": 0, "output_tokens": 45024, "reasoning_output_tokens": 17520, "total_tokens": 5258523 }, "thread_token_usage": { "input_tokens": 5213499, "cached_input_tokens": 5131008, "cache_write_input_tokens": 0, "output_tokens": 45024, "reasoning_output_tokens": 17520, "total_tokens": 5258523 } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-25T00:59:43.343Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:43.343Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:59:53.477Z", "callId": "call_b7eAQtbWbiQbrMdQsORxsy3t", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\\n for l,r in enumerate(range(st,en)):\\n gold=truth.get(int(uids[r]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\\n for g in gs:\\n b=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\\nfor x in best[:25]: print(x)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T00:59:54.658Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=98881", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:59:53.635Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c77c23a487d28b2d257299f0e817", "usage": { "input_tokens": 78018, "cached_input_tokens": 76032, "cache_write_input_tokens": 0, "output_tokens": 702, "reasoning_output_tokens": 133, "total_tokens": 78720 }, "turn_token_usage": { "input_tokens": 5291517, "cached_input_tokens": 5207040, "cache_write_input_tokens": 0, "output_tokens": 45726, "reasoning_output_tokens": 17653, "total_tokens": 5337243 }, "thread_token_usage": { "input_tokens": 5291517, "cached_input_tokens": 5207040, "cache_write_input_tokens": 0, "output_tokens": 45726, "reasoning_output_tokens": 17653, "total_tokens": 5337243 } } }, { "kind": "tool", "timestamp": "2026-09-25T00:59:56.704Z", "callId": "call_NafvGE6JbSR8Drr8yVTyf5fi", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":98881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:00:26.712Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:59:56.802Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c78ada7487d2a62e976d3acebac1", "usage": { "input_tokens": 78748, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 78793 }, "turn_token_usage": { "input_tokens": 5370265, "cached_input_tokens": 5284864, "cache_write_input_tokens": 0, "output_tokens": 45771, "reasoning_output_tokens": 17653, "total_tokens": 5416036 }, "thread_token_usage": { "input_tokens": 5370265, "cached_input_tokens": 5284864, "cache_write_input_tokens": 0, "output_tokens": 45771, "reasoning_output_tokens": 17653, "total_tokens": 5416036 } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-25T01:00:28.433Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:28.433Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:00:29.122Z", "callId": "call_oJ0k5LiyiGP4xk5Zp9FjjcPD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":98881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:00:37.336Z", "content": "Script completed\nWall time 8.2 seconds\nOutput:\n\n(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:00:29.233Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7aafa1887d284e5b45e7c6af4a7", "usage": { "input_tokens": 78816, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 78880 }, "turn_token_usage": { "input_tokens": 5449081, "cached_input_tokens": 5363456, "cache_write_input_tokens": 0, "output_tokens": 45835, "reasoning_output_tokens": 17670, "total_tokens": 5494916 }, "thread_token_usage": { "input_tokens": 5449081, "cached_input_tokens": 5363456, "cache_write_input_tokens": 0, "output_tokens": 45835, "reasoning_output_tokens": 17670, "total_tokens": 5494916 } } }, { "kind": "context", "timestamp": "2026-09-25T01:00:37.334Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ae306ba-b025-403f-a3a3-22671112a0b2", "process_id": "98881", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nfor x in best[:25]: print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nfor x in best[:25]: print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n", "stderr": "", "aggregated_output": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n", "exit_code": 0, "duration": { "secs": 43, "nanos": 677732900 }, "formatted_output": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n" } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-25T01:00:39.375Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:39.375Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:00:43.971Z", "callId": "call_CAiwwguRGA9iPwf20JHYrpNq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:00:45.138Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=33305", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:00:44.101Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7b582bc87d2a4971331961b824e", "usage": { "input_tokens": 79644, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 17, "total_tokens": 79925 }, "turn_token_usage": { "input_tokens": 5528725, "cached_input_tokens": 5442048, "cache_write_input_tokens": 0, "output_tokens": 46116, "reasoning_output_tokens": 17687, "total_tokens": 5574841 }, "thread_token_usage": { "input_tokens": 5528725, "cached_input_tokens": 5442048, "cache_write_input_tokens": 0, "output_tokens": 46116, "reasoning_output_tokens": 17687, "total_tokens": 5574841 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:00:46.922Z", "callId": "call_flEdMGM9NOhM6orh4HCZgRgo", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33305,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:00:55.550Z", "content": "Script completed\nWall time 8.6 seconds\nOutput:\n\n(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:00:47.027Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7bd636c87d2b5e15bce42766fd1", "usage": { "input_tokens": 79953, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 79998 }, "turn_token_usage": { "input_tokens": 5608678, "cached_input_tokens": 5521536, "cache_write_input_tokens": 0, "output_tokens": 46161, "reasoning_output_tokens": 17687, "total_tokens": 5654839 }, "thread_token_usage": { "input_tokens": 5608678, "cached_input_tokens": 5521536, "cache_write_input_tokens": 0, "output_tokens": 46161, "reasoning_output_tokens": 17687, "total_tokens": 5654839 } } }, { "kind": "context", "timestamp": "2026-09-25T01:00:55.548Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07b2b6ad-a6b2-43d3-bf65-b001a21e0cb0", "process_id": "33305", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 411142752 }, "formatted_output": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-25T01:01:05.395Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:01:05.395Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:01:17.753Z", "callId": "call_VfJ3MetImsvuCht1GZiB0eOO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\n# Recompute only scored-user approximate qualities, but bin from all target users.\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\\n rr.append(row); qs.append(q)\\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\\nfor b in range(5):\\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\\n # select nearest tested where necessary\\n vals=[]\\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\\n print('sched',schedule,np.mean(vals))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:01:18.917Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=98090", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:01:17.882Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7c80a9487d2b983f2ddb8d7e1d9", "usage": { "input_tokens": 80081, "cached_input_tokens": 79744, "cache_write_input_tokens": 0, "output_tokens": 1109, "reasoning_output_tokens": 376, "total_tokens": 81190 }, "turn_token_usage": { "input_tokens": 5688759, "cached_input_tokens": 5601280, "cache_write_input_tokens": 0, "output_tokens": 47270, "reasoning_output_tokens": 18063, "total_tokens": 5736029 }, "thread_token_usage": { "input_tokens": 5688759, "cached_input_tokens": 5601280, "cache_write_input_tokens": 0, "output_tokens": 47270, "reasoning_output_tokens": 18063, "total_tokens": 5736029 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:01:22.827Z", "callId": "call_EcEKsaGKMGFVbehUzaNMIsEv", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":98090,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:01:29.703Z", "content": "Script completed\nWall time 6.9 seconds\nOutput:\n\nedges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:01:22.939Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7df247c87d289059b49a9d0dff8", "usage": { "input_tokens": 81218, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 81263 }, "turn_token_usage": { "input_tokens": 5769977, "cached_input_tokens": 5681152, "cache_write_input_tokens": 0, "output_tokens": 47315, "reasoning_output_tokens": 18063, "total_tokens": 5817292 }, "thread_token_usage": { "input_tokens": 5769977, "cached_input_tokens": 5681152, "cache_write_input_tokens": 0, "output_tokens": 47315, "reasoning_output_tokens": 18063, "total_tokens": 5817292 } } }, { "kind": "context", "timestamp": "2026-09-25T01:01:29.701Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a81da5e-4671-45ca-b8cf-b86d5b91a61c", "process_id": "98090", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\n# Recompute only scored-user approximate qualities, but bin from all target users.\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\n rr.append(row); qs.append(q)\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\nfor b in range(5):\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\n # select nearest tested where necessary\n vals=[]\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\n print('sched',schedule,np.mean(vals))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\n# Recompute only scored-user approximate qualities, but bin from all target users.\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\n rr.append(row); qs.append(q)\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\nfor b in range(5):\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\n # select nearest tested where necessary\n vals=[]\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\n print('sched',schedule,np.mean(vals))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n", "stderr": "", "aggregated_output": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 785411004 }, "formatted_output": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n" } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-25T01:01:39.620Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:01:39.620Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:01:49.534Z", "callId": "call_PVjT9vMbjW5oU7DuWOzI95Uk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\\ntop=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:01:50.699Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=7247", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:01:49.704Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7e9f6a487d282dd63ac599387a4", "usage": { "input_tokens": 82416, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 917, "reasoning_output_tokens": 344, "total_tokens": 83333 }, "turn_token_usage": { "input_tokens": 5852393, "cached_input_tokens": 5762176, "cache_write_input_tokens": 0, "output_tokens": 48232, "reasoning_output_tokens": 18407, "total_tokens": 5900625 }, "thread_token_usage": { "input_tokens": 5852393, "cached_input_tokens": 5762176, "cache_write_input_tokens": 0, "output_tokens": 48232, "reasoning_output_tokens": 18407, "total_tokens": 5900625 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:01:54.391Z", "callId": "call_Fx7nyjllogtszin0OkrWU98K", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7247,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:02:00.832Z", "content": "Script completed\nWall time 6.4 seconds\nOutput:\n\n[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:01:54.492Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7fee90087d29bbff7d376e5af99", "usage": { "input_tokens": 83361, "cached_input_tokens": 82176, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 83406 }, "turn_token_usage": { "input_tokens": 5935754, "cached_input_tokens": 5844352, "cache_write_input_tokens": 0, "output_tokens": 48277, "reasoning_output_tokens": 18407, "total_tokens": 5984031 }, "thread_token_usage": { "input_tokens": 5935754, "cached_input_tokens": 5844352, "cache_write_input_tokens": 0, "output_tokens": 48277, "reasoning_output_tokens": 18407, "total_tokens": 5984031 } } }, { "kind": "context", "timestamp": "2026-09-25T01:02:00.831Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-11a3663b-639f-4e96-8fbb-201e18cb8cea", "process_id": "7247", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 133154643 }, "formatted_output": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-25T01:02:04.918Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:02:04.918Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:02:16.979Z", "callId": "call_dOW7iXveiBvvOuAF0ZFstYfY", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\\n for g in gs:\\n base=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\\nfor b in range(5):\\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:02:18.144Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=19333", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:02:17.101Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c809101c87d2ab6f5e575957b2dd", "usage": { "input_tokens": 83507, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 787, "reasoning_output_tokens": 108, "total_tokens": 84294 }, "turn_token_usage": { "input_tokens": 6019261, "cached_input_tokens": 5927552, "cache_write_input_tokens": 0, "output_tokens": 49064, "reasoning_output_tokens": 18515, "total_tokens": 6068325 }, "thread_token_usage": { "input_tokens": 6019261, "cached_input_tokens": 5927552, "cache_write_input_tokens": 0, "output_tokens": 49064, "reasoning_output_tokens": 18515, "total_tokens": 6068325 } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-25T01:02:19.637Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:02:19.637Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:02:20.330Z", "callId": "call_v2NZaQxeweD0QIBURxMjsZz2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":19333,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:02:50.338Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n\r\nbin 0 n 641\r\n(np.float64(0.0444453111606116), 0.35, 0.09)\r\n(np.float64(0.04434832223350444), 0.35, 0.11)\r\n(np.float64(0.04415682212784072), 0.5, 0.11)\r\n(np.float64(0.0439980504795929), 0.6, 0.11)\r\n(np.float64(0.04396205335762866), 0.5, 0.09)\r\n(np.float64(0.04388975094954861), 0.8, 0.075)\r\n(np.float64(0.043780587004762214), 0.9, 0.075)\r\n(np.float64(0.0437111869011351), 0.65, 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0.65, 0.075)\r\n(np.float64(0.047097037152278726), 0.8, 0.065)\r\n\r\nbin 4 n 1027\r\n(np.float64(0.0403960730046121), 0.9, 0.055)\r\n(np.float64(0.0400932173313189), 1.0, 0.055)\r\n(np.float64(0.04006575471857305), 1.0, 0.05)\r\n(np.float64(0.03997070938409836), 0.7, 0.055)\r\n(np.float64(0.03984419465051729), 0.65, 0.055)\r\n(np.float64(0.03982782124957421), 0.9, 0.05)\r\n(np.float64(0.03976517714843728), 0.6, 0.055)\r\n(np.float64(0.0397208324276101), 0.8, 0.055)\r\n(np.float64(0.03970631820210737), 0.9, 0.06)\r\n(np.float64(0.0397033649654579), 1.0, 0.06)\r\n(np.float64(0.039599371030816496), 0.7, 0.06)\r\n(np.float64(0.03946120222129177), 0.8, 0.06)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:02:20.448Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c81a622c87d28b6716d7c51777e1", "usage": { "input_tokens": 84322, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 84375 }, "turn_token_usage": { "input_tokens": 6103583, "cached_input_tokens": 6010880, "cache_write_input_tokens": 0, "output_tokens": 49117, "reasoning_output_tokens": 18521, "total_tokens": 6152700 }, "thread_token_usage": { "input_tokens": 6103583, "cached_input_tokens": 6010880, "cache_write_input_tokens": 0, "output_tokens": 49117, "reasoning_output_tokens": 18521, "total_tokens": 6152700 } } }, { "kind": "context", "timestamp": "2026-09-25T01:02:50.460Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a7163567-121f-40fd-8deb-c245d693d3ce", "process_id": "19333", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\n for g in gs:\n base=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\nfor b in range(5):\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\n for g in gs:\n base=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\nfor b in range(5):\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\r\nbin 0 n 641\r\n(np.float64(0.0444453111606116), 0.35, 0.09)\r\n(np.float64(0.04434832223350444), 0.35, 0.11)\r\n(np.float64(0.04415682212784072), 0.5, 0.11)\r\n(np.float64(0.0439980504795929), 0.6, 0.11)\r\n(np.float64(0.04396205335762866), 0.5, 0.09)\r\n(np.float64(0.04388975094954861), 0.8, 0.075)\r\n(np.float64(0.043780587004762214), 0.9, 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0.06)\r\n" } } } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-25T01:03:00.274Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:00.274Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:03:10.310Z", "callId": "call_wRMxRxjIVoNnlLrZa7DCQS1v", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\\ntop=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\\nprint(edges)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:03:11.477Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=3931", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:10.442Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c83a886487d28e3992933a196f5a", "usage": { "input_tokens": 85815, "cached_input_tokens": 84096, "cache_write_input_tokens": 0, "output_tokens": 1051, "reasoning_output_tokens": 483, "total_tokens": 86866 }, "turn_token_usage": { "input_tokens": 6189398, "cached_input_tokens": 6094976, "cache_write_input_tokens": 0, "output_tokens": 50168, "reasoning_output_tokens": 19004, "total_tokens": 6239566 }, "thread_token_usage": { "input_tokens": 6189398, "cached_input_tokens": 6094976, "cache_write_input_tokens": 0, "output_tokens": 50168, "reasoning_output_tokens": 19004, "total_tokens": 6239566 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:03:13.382Z", "callId": "call_DqSwPCYTJtlmDtUGnY92eaLs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":3931,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:27.922Z", "content": "Script completed\nWall time 14.5 seconds\nOutput:\n\n[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:13.514Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c84fae0087d2a7c2088f9d0faae5", "usage": { "input_tokens": 86894, "cached_input_tokens": 85632, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 86939 }, "turn_token_usage": { "input_tokens": 6276292, "cached_input_tokens": 6180608, "cache_write_input_tokens": 0, "output_tokens": 50213, "reasoning_output_tokens": 19004, "total_tokens": 6326505 }, "thread_token_usage": { "input_tokens": 6276292, "cached_input_tokens": 6180608, "cache_write_input_tokens": 0, "output_tokens": 50213, "reasoning_output_tokens": 19004, "total_tokens": 6326505 } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:27.920Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b193774-83fa-4387-b1d2-09f34936c49c", "process_id": "3931", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\nprint(edges)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\nprint(edges)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 444769644 }, "formatted_output": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-25T01:03:35.651Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:35.651Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:03:37.300Z", "text": "The stable activity calibration lifts the exact score to 0.040776: low-activity histories lean more on current catalog trends, while high-activity histories lean slightly more on personalized embeddings. I’m proceeding to the required final refit on train plus validation, reproducing both history views and using the observed validation week only as history/training data." }, { "kind": "tool", "timestamp": "2026-09-25T01:03:42.145Z", "callId": "call_7LfPEc87UcbThP6nWwJfInJU", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\\nfor factor in [1]:\\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\\n print((c/t.recent_positive_listens).describe())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:03:43.310Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=67128", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:42.271Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8601cc087d28039b9260b2ca17e", "usage": { "input_tokens": 87027, "cached_input_tokens": 86656, "cache_write_input_tokens": 0, "output_tokens": 728, "reasoning_output_tokens": 366, "total_tokens": 87755 }, "turn_token_usage": { "input_tokens": 6363319, "cached_input_tokens": 6267264, "cache_write_input_tokens": 0, "output_tokens": 50941, "reasoning_output_tokens": 19370, "total_tokens": 6414260 }, "thread_token_usage": { "input_tokens": 6363319, "cached_input_tokens": 6267264, "cache_write_input_tokens": 0, "output_tokens": 50941, "reasoning_output_tokens": 19370, "total_tokens": 6414260 } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:44.617Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae3639dd-aa5b-4538-99d5-f8463b4f2057", "process_id": "67128", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\nfor factor in [1]:\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\n print((c/t.recent_positive_listens).describe())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\nfor factor in [1]:\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\n print((c/t.recent_positive_listens).describe())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n", "stderr": "", "aggregated_output": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 309544433 }, "formatted_output": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:03:45.836Z", "callId": "call_i9MwJk8J6HgnxrzHm2lieqW7", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":67128,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:45.941Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nequal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:45.939Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c86f7f6487d29c76463ae9cdb3b5", "usage": { "input_tokens": 87783, "cached_input_tokens": 86912, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87828 }, "turn_token_usage": { "input_tokens": 6451102, "cached_input_tokens": 6354176, "cache_write_input_tokens": 0, "output_tokens": 50986, "reasoning_output_tokens": 19370, "total_tokens": 6502088 }, "thread_token_usage": { "input_tokens": 6451102, "cached_input_tokens": 6354176, "cache_write_input_tokens": 0, "output_tokens": 50986, "reasoning_output_tokens": 19370, "total_tokens": 6502088 } } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-25T01:03:56.599Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:56.599Z", "text": "" } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-25T01:04:04.698Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:04.698Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:10.222Z", "callId": "call_Df0uzdnHjzDJf2Dy9nSbW1VL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntop=np.empty((len(uids),100),np.int32)\\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\\nprint('baseline submission saved',len(uids)*100)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:04:11.390Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=59692", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:10.373Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c872549087d29620391d7cde653a", "usage": { "input_tokens": 88164, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 1275, "reasoning_output_tokens": 960, "total_tokens": 89439 }, "turn_token_usage": { "input_tokens": 6539266, "cached_input_tokens": 6441728, "cache_write_input_tokens": 0, "output_tokens": 52261, "reasoning_output_tokens": 20330, "total_tokens": 6591527 }, "thread_token_usage": { "input_tokens": 6539266, "cached_input_tokens": 6441728, "cache_write_input_tokens": 0, "output_tokens": 52261, "reasoning_output_tokens": 20330, "total_tokens": 6591527 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:04:13.401Z", "callId": "call_sJ9EVt3NGg28rmHHes9fOTql", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":59692,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:14.909Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\nTraceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:13.511Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c88b97d087d287ab94281a1c609d", "usage": { "input_tokens": 89467, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 89512 }, "turn_token_usage": { "input_tokens": 6628733, "cached_input_tokens": 6529664, "cache_write_input_tokens": 0, "output_tokens": 52306, "reasoning_output_tokens": 20330, "total_tokens": 6681039 }, "thread_token_usage": { "input_tokens": 6628733, "cached_input_tokens": 6529664, "cache_write_input_tokens": 0, "output_tokens": 52306, "reasoning_output_tokens": 20330, "total_tokens": 6681039 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:14.907Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-125729d5-8fa9-437c-96ab-8b37b47a07af", "process_id": "59692", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n", "exit_code": 1, "duration": { "secs": 4, "nanos": 518335932 }, "formatted_output": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n" } } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-25T01:04:17.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:17.621Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:19.356Z", "callId": "call_m8NPW9QGMt88AF6a3bTfk1dU", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if stage == \\\"final\\\":\\n+ if stage == \\\"final\\\" and \\\"uid\\\" in frame.columns:\\n frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n frame.reset_index(drop=True, inplace=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:04:19.513Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:19.365Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d6cecd2f-56e4-4795-999b-507d8805a9a9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -54,3 +54,3 @@\n # Each source is uid/time sorted, but concatenating the two windows is not.\n- if stage == \"final\":\n+ if stage == \"final\" and \"uid\" in frame.columns:\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:19.512Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c88f1ac487d2ada589877eb805b3", "usage": { "input_tokens": 89742, "cached_input_tokens": 89344, "cache_write_input_tokens": 0, "output_tokens": 175, "reasoning_output_tokens": 70, "total_tokens": 89917 }, "turn_token_usage": { "input_tokens": 6718475, "cached_input_tokens": 6619008, "cache_write_input_tokens": 0, "output_tokens": 52481, "reasoning_output_tokens": 20400, "total_tokens": 6770956 }, "thread_token_usage": { "input_tokens": 6718475, "cached_input_tokens": 6619008, "cache_write_input_tokens": 0, "output_tokens": 52481, "reasoning_output_tokens": 20400, "total_tokens": 6770956 } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-25T01:04:21.332Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:21.332Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:26.895Z", "callId": "call_WBqQneNXBsE2wbSAVumZ2fIk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntop=np.empty((len(uids),100),np.int32)\\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\\nprint('baseline submission saved',len(uids)*100)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:04:28.059Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=60400", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:27.035Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c894186487d28c5b35fcb68cf716", "usage": { "input_tokens": 89941, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 322, "reasoning_output_tokens": 7, "total_tokens": 90263 }, "turn_token_usage": { "input_tokens": 6808416, "cached_input_tokens": 6708608, "cache_write_input_tokens": 0, "output_tokens": 52803, "reasoning_output_tokens": 20407, "total_tokens": 6861219 }, "thread_token_usage": { "input_tokens": 6808416, "cached_input_tokens": 6708608, "cache_write_input_tokens": 0, "output_tokens": 52803, "reasoning_output_tokens": 20407, "total_tokens": 6861219 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:04:29.904Z", "callId": "call_e9ErpP4mnIMxa3gktYVwpfzl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":60400,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:33.246Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\nbaseline submission saved 845600\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:30.050Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c89c3f6c87d2be229b2f09bd01b7", "usage": { "input_tokens": 90291, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 90336 }, "turn_token_usage": { "input_tokens": 6898707, "cached_input_tokens": 6798336, "cache_write_input_tokens": 0, "output_tokens": 52848, "reasoning_output_tokens": 20407, "total_tokens": 6951555 }, "thread_token_usage": { "input_tokens": 6898707, "cached_input_tokens": 6798336, "cache_write_input_tokens": 0, "output_tokens": 52848, "reasoning_output_tokens": 20407, "total_tokens": 6951555 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:33.244Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4509267e-0cf8-4cc7-94ae-4a8721ce5336", "process_id": "60400", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "baseline submission saved 845600\r\n", "stderr": "", "aggregated_output": "baseline submission saved 845600\r\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 186188691 }, "formatted_output": "baseline submission saved 845600\r\n" } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-25T01:04:44.131Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:44.131Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:22.423Z", "callId": "call_owTRr7nPZcXGG9XPLNlB4Ucb", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected two-tower ensemble and write the hidden-test ranking.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import copy\\n+import json\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+import train_ranker as tr\\n+\\n+\\n+DIM = 48\\n+WEEKS = 30\\n+RAW_MIX = 0.35\\n+UNIQUE_MIX = 0.65\\n+# Coefficients selected on all-target activity quintiles in the public backtest.\\n+TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\\n+\\n+\\n+def save_model(model: tr.TwoTower, path, features: np.ndarray, catalog_ids: np.ndarray, mode: str) -> None:\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"dim\\\": DIM,\\n+ \\\"item_features\\\": features,\\n+ \\\"catalog_ids\\\": catalog_ids,\\n+ \\\"history_mode\\\": mode,\\n+ \\\"architecture\\\": \\\"history/preference MLP user tower + ID/feature MLP item tower\\\",\\n+ },\\n+ path,\\n+ )\\n+\\n+\\n+def final_activity(targets: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Cutoff-correct 90-day conventional-positive counts for calibration.\\\"\\\"\\\"\\n+ columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"played_ratio_pct\\\"]\\n+ x = pd.concat(\\n+ [\\n+ pd.read_parquet(tr.DATA / \\\"train_listens.parquet\\\", columns=columns),\\n+ pd.read_parquet(tr.DATA / \\\"val_listens.parquet\\\", columns=columns),\\n+ ],\\n+ ignore_index=True,\\n+ )\\n+ keep = (\\n+ (x.timestamp.to_numpy() >= tr.VALIDATION_CUTOFF - 90 * 86400)\\n+ & (x.timestamp.to_numpy() < tr.VALIDATION_CUTOFF)\\n+ & (x.played_ratio_pct.to_numpy() >= 50)\\n+ )\\n+ counts = x.loc[keep].groupby(\\\"uid\\\").size()\\n+ return counts.reindex(targets, fill_value=0).to_numpy(dtype=np.float32)\\n+\\n+\\n+def main() -> None:\\n+ started = time.time()\\n+ catalog = pd.read_parquet(tr.DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ features = tr.item_features(catalog)\\n+\\n+ raw_cache = tr.OUT / f\\\"snapshots_final_{WEEKS}w_raw.npz\\\"\\n+ if raw_cache.exists():\\n+ raw_snapshots = tr.SnapshotArrays.load(raw_cache)\\n+ else:\\n+ raw_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=False)\\n+ raw_snapshots.save(raw_cache)\\n+ raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\\n+ raw_model = tr.train_model(\\n+ raw_snapshots,\\n+ features,\\n+ DIM,\\n+ 10,\\n+ 20261010,\\n+ initial_model=raw_model,\\n+ learning_rate=1e-3,\\n+ )\\n+ save_model(raw_model, tr.OUT / \\\"two_tower_final_raw.pt\\\", features, catalog_ids, \\\"raw\\\")\\n+ raw_users = tr.build_inference_users(\\\"final\\\", catalog_ids, unique_history=False)\\n+ raw_user_embedding, raw_item_embedding = tr.encode(raw_model, raw_users, features)\\n+ del raw_snapshots\\n+\\n+ unique_cache = tr.OUT / f\\\"snapshots_final_{WEEKS}w_unique.npz\\\"\\n+ if unique_cache.exists():\\n+ unique_snapshots = tr.SnapshotArrays.load(unique_cache)\\n+ else:\\n+ unique_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=True)\\n+ unique_snapshots.save(unique_cache)\\n+ unique_model = copy.deepcopy(raw_model)\\n+ unique_model = tr.train_model(\\n+ unique_snapshots,\\n+ features,\\n+ DIM,\\n+ 10,\\n+ 20261030,\\n+ initial_model=unique_model,\\n+ learning_rate=1e-3,\\n+ )\\n+ save_model(unique_model, tr.OUT / \\\"two_tower_final_unique.pt\\\", features, catalog_ids, \\\"unique\\\")\\n+ unique_users = tr.build_inference_users(\\\"final\\\", catalog_ids, unique_history=True)\\n+ unique_user_embedding, unique_item_embedding = tr.encode(unique_model, unique_users, features)\\n+\\n+ targets = raw_users[0]\\n+ assert np.array_equal(targets, unique_users[0])\\n+ activity = final_activity(targets)\\n+ activity_edges = np.quantile(activity, [0.2, 0.4, 0.6, 0.8])\\n+ activity_bin = np.searchsorted(activity_edges, activity, side=\\\"right\\\")\\n+ trend_weight = TREND_BY_ACTIVITY[activity_bin]\\n+ trend = tr.recent_trend(\\\"final\\\", catalog_ids)\\n+\\n+ # These are the actual request-time/offline MIPS vectors. Their dot product\\n+ # is exactly the deployed relevance score—there is no cross feature or\\n+ # post-retrieval model.\\n+ user_vector = np.concatenate(\\n+ [\\n+ RAW_MIX * raw_user_embedding,\\n+ UNIQUE_MIX * unique_user_embedding,\\n+ trend_weight[:, None],\\n+ ],\\n+ axis=1,\\n+ ).astype(np.float32)\\n+ item_vector = np.concatenate(\\n+ [raw_item_embedding, unique_item_embedding, trend[:, None]], axis=1\\n+ ).astype(np.float32)\\n+ np.savez_compressed(\\n+ tr.OUT / \\\"deployment_vectors.npz\\\",\\n+ uids=targets,\\n+ item_ids=catalog_ids,\\n+ user_vectors=user_vector,\\n+ item_vectors=item_vector,\\n+ )\\n+\\n+ with np.load(tr.DATA / \\\"test_eligible_candidates.npz\\\") as z:\\n+ eligible_uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(targets, eligible_uids)\\n+ assert np.array_equal(catalog_ids, eligible_items)\\n+ masks = np.unpackbits(\\n+ packed, axis=1, count=len(eligible_items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ top = np.empty((len(targets), 100), dtype=np.int32)\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ score = user_vector[start:end] @ item_vector.T\\n+ score[~masks[start:end]] = -np.inf\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ selected_score = np.take_along_axis(score, part, axis=1)\\n+ top[start:end] = np.take_along_axis(\\n+ part, np.argsort(-selected_score, axis=1), axis=1\\n+ )\\n+\\n+ submission = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(targets, 100).astype(np.int64),\\n+ \\\"item_id\\\": eligible_items[top.reshape(-1)].astype(np.int64),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\\n+ }\\n+ )\\n+ # Atomic replacement keeps the previously saved valid baseline intact\\n+ # until the complete refit ranking has been written successfully.\\n+ temporary = tr.OUT / \\\"submission.refit.parquet\\\"\\n+ submission.to_parquet(temporary, index=False)\\n+ temporary.replace(tr.OUT / \\\"submission.parquet\\\")\\n+\\n+ report = {\\n+ \\\"model\\\": \\\"97d concatenated two-tower MIPS ensemble\\\",\\n+ \\\"raw_examples\\\": int(len(raw_snapshots)) if False else None,\\n+ \\\"unique_examples\\\": int(len(unique_snapshots.history)),\\n+ \\\"activity_edges\\\": activity_edges.tolist(),\\n+ \\\"trend_weights\\\": TREND_BY_ACTIVITY.tolist(),\\n+ \\\"rows\\\": int(len(submission)),\\n+ \\\"seconds\\\": round(time.time() - started, 1),\\n+ }\\n+ (tr.OUT / \\\"final_report.json\\\").write_text(json.dumps(report, indent=2) + \\\"\\\\n\\\")\\n+ print(json.dumps(report, indent=2), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:05:22.522Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:22.432Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4709cbee-594c-4b7c-852f-d277de454bd6", "changes": { "/app/solution/build_submission.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Refit the selected two-tower ensemble and write the hidden-test ranking.\"\"\"\nfrom __future__ import annotations\n\nimport copy\nimport json\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nimport train_ranker as tr\n\n\nDIM = 48\nWEEKS = 30\nRAW_MIX = 0.35\nUNIQUE_MIX = 0.65\n# Coefficients selected on all-target activity quintiles in the public backtest.\nTREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\n\n\ndef save_model(model: tr.TwoTower, path, features: np.ndarray, catalog_ids: np.ndarray, mode: str) -> None:\n torch.save(\n {\n \"state_dict\": model.state_dict(),\n \"dim\": DIM,\n \"item_features\": features,\n \"catalog_ids\": catalog_ids,\n \"history_mode\": mode,\n \"architecture\": \"history/preference MLP user tower + ID/feature MLP item tower\",\n },\n path,\n )\n\n\ndef final_activity(targets: np.ndarray) -> np.ndarray:\n \"\"\"Cutoff-correct 90-day conventional-positive counts for calibration.\"\"\"\n columns = [\"uid\", \"timestamp\", \"played_ratio_pct\"]\n x = pd.concat(\n [\n pd.read_parquet(tr.DATA / \"train_listens.parquet\", columns=columns),\n pd.read_parquet(tr.DATA / \"val_listens.parquet\", columns=columns),\n ],\n ignore_index=True,\n )\n keep = (\n (x.timestamp.to_numpy() >= tr.VALIDATION_CUTOFF - 90 * 86400)\n & (x.timestamp.to_numpy() < tr.VALIDATION_CUTOFF)\n & (x.played_ratio_pct.to_numpy() >= 50)\n )\n counts = x.loc[keep].groupby(\"uid\").size()\n return counts.reindex(targets, fill_value=0).to_numpy(dtype=np.float32)\n\n\ndef main() -> None:\n started = time.time()\n catalog = pd.read_parquet(tr.DATA / \"candidate_catalog.parquet\")\n catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n features = tr.item_features(catalog)\n\n raw_cache = tr.OUT / f\"snapshots_final_{WEEKS}w_raw.npz\"\n if raw_cache.exists():\n raw_snapshots = tr.SnapshotArrays.load(raw_cache)\n else:\n raw_snapshots = tr.build_snapshots(\"final\", catalog_ids, WEEKS, unique_history=False)\n raw_snapshots.save(raw_cache)\n raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\n raw_model = tr.train_model(\n raw_snapshots,\n features,\n DIM,\n 10,\n 20261010,\n initial_model=raw_model,\n learning_rate=1e-3,\n )\n save_model(raw_model, tr.OUT / \"two_tower_final_raw.pt\", features, catalog_ids, \"raw\")\n raw_users = tr.build_inference_users(\"final\", catalog_ids, unique_history=False)\n raw_user_embedding, raw_item_embedding = tr.encode(raw_model, raw_users, features)\n del raw_snapshots\n\n unique_cache = tr.OUT / f\"snapshots_final_{WEEKS}w_unique.npz\"\n if unique_cache.exists():\n unique_snapshots = tr.SnapshotArrays.load(unique_cache)\n else:\n unique_snapshots = tr.build_snapshots(\"final\", catalog_ids, WEEKS, unique_history=True)\n unique_snapshots.save(unique_cache)\n unique_model = copy.deepcopy(raw_model)\n unique_model = tr.train_model(\n unique_snapshots,\n features,\n DIM,\n 10,\n 20261030,\n initial_model=unique_model,\n learning_rate=1e-3,\n )\n save_model(unique_model, tr.OUT / \"two_tower_final_unique.pt\", features, catalog_ids, \"unique\")\n unique_users = tr.build_inference_users(\"final\", catalog_ids, unique_history=True)\n unique_user_embedding, unique_item_embedding = tr.encode(unique_model, unique_users, features)\n\n targets = raw_users[0]\n assert np.array_equal(targets, unique_users[0])\n activity = final_activity(targets)\n activity_edges = np.quantile(activity, [0.2, 0.4, 0.6, 0.8])\n activity_bin = np.searchsorted(activity_edges, activity, side=\"right\")\n trend_weight = TREND_BY_ACTIVITY[activity_bin]\n trend = tr.recent_trend(\"final\", catalog_ids)\n\n # These are the actual request-time/offline MIPS vectors. Their dot product\n # is exactly the deployed relevance score—there is no cross feature or\n # post-retrieval model.\n user_vector = np.concatenate(\n [\n RAW_MIX * raw_user_embedding,\n UNIQUE_MIX * unique_user_embedding,\n trend_weight[:, None],\n ],\n axis=1,\n ).astype(np.float32)\n item_vector = np.concatenate(\n [raw_item_embedding, unique_item_embedding, trend[:, None]], axis=1\n ).astype(np.float32)\n np.savez_compressed(\n tr.OUT / \"deployment_vectors.npz\",\n uids=targets,\n item_ids=catalog_ids,\n user_vectors=user_vector,\n item_vectors=item_vector,\n )\n\n with np.load(tr.DATA / \"test_eligible_candidates.npz\") as z:\n eligible_uids = z[\"uids\"].astype(np.int64, copy=True)\n eligible_items = z[\"item_ids\"].astype(np.int64, copy=True)\n packed = z[\"packed_eligible\"].copy()\n assert np.array_equal(targets, eligible_uids)\n assert np.array_equal(catalog_ids, eligible_items)\n masks = np.unpackbits(\n packed, axis=1, count=len(eligible_items), bitorder=\"little\"\n ).astype(bool)\n top = np.empty((len(targets), 100), dtype=np.int32)\n for start in range(0, len(targets), 128):\n end = min(start + 128, len(targets))\n score = user_vector[start:end] @ item_vector.T\n score[~masks[start:end]] = -np.inf\n part = np.argpartition(score, -100, axis=1)[:, -100:]\n selected_score = np.take_along_axis(score, part, axis=1)\n top[start:end] = np.take_along_axis(\n part, np.argsort(-selected_score, axis=1), axis=1\n )\n\n submission = pd.DataFrame(\n {\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n \"item_id\": eligible_items[top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\n }\n )\n # Atomic replacement keeps the previously saved valid baseline intact\n # until the complete refit ranking has been written successfully.\n temporary = tr.OUT / \"submission.refit.parquet\"\n submission.to_parquet(temporary, index=False)\n temporary.replace(tr.OUT / \"submission.parquet\")\n\n report = {\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\n \"raw_examples\": int(len(raw_snapshots)) if False else None,\n \"unique_examples\": int(len(unique_snapshots.history)),\n \"activity_edges\": activity_edges.tolist(),\n \"trend_weights\": TREND_BY_ACTIVITY.tolist(),\n \"rows\": int(len(submission)),\n \"seconds\": round(time.time() - started, 1),\n }\n (tr.OUT / \"final_report.json\").write_text(json.dumps(report, indent=2) + \"\\n\")\n print(json.dumps(report, indent=2), flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_submission.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:05:22.521Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8a1756887d2982badc4d9f25797", "usage": { "input_tokens": 90366, "cached_input_tokens": 90112, "cache_write_input_tokens": 0, "output_tokens": 2652, "reasoning_output_tokens": 516, "total_tokens": 93018 }, "turn_token_usage": { "input_tokens": 6989073, "cached_input_tokens": 6888448, "cache_write_input_tokens": 0, "output_tokens": 55500, "reasoning_output_tokens": 20923, "total_tokens": 7044573 }, "thread_token_usage": { "input_tokens": 6989073, "cached_input_tokens": 6888448, "cache_write_input_tokens": 0, "output_tokens": 55500, "reasoning_output_tokens": 20923, "total_tokens": 7044573 } } } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-25T01:05:25.203Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:25.203Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:27.439Z", "callId": "call_v9chaq0R2Rke3EHsCzxs75cg", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_submission.py\\n@@\\n else:\\n raw_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=False)\\n raw_snapshots.save(raw_cache)\\n+ raw_example_count = len(raw_snapshots.history)\\n@@\\n- \\\"raw_examples\\\": int(len(raw_snapshots)) if False else None,\\n+ \\\"raw_examples\\\": int(raw_example_count),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:05:27.575Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:27.448Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ab39b1c0-3924-4685-b699-29bddd6ea8f2", "changes": { "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -68,2 +68,3 @@\n raw_snapshots.save(raw_cache)\n+ raw_example_count = len(raw_snapshots.history)\n raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\n@@ -168,3 +169,3 @@\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\n- \"raw_examples\": int(len(raw_snapshots)) if False else None,\n+ \"raw_examples\": int(raw_example_count),\n \"unique_examples\": int(len(unique_snapshots.history)),\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_submission.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:05:27.574Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8d2d76487d299b583aef049e4e3", "usage": { "input_tokens": 93042, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 197, "reasoning_output_tokens": 67, "total_tokens": 93239 }, "turn_token_usage": { "input_tokens": 7082115, "cached_input_tokens": 6978688, "cache_write_input_tokens": 0, "output_tokens": 55697, "reasoning_output_tokens": 20990, "total_tokens": 7137812 }, "thread_token_usage": { "input_tokens": 7082115, "cached_input_tokens": 6978688, "cache_write_input_tokens": 0, "output_tokens": 55697, "reasoning_output_tokens": 20990, "total_tokens": 7137812 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:05:30.287Z", "callId": "call_yUBicaBqVKcoBQVsQT0NgYoO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:05:30.455Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:30.408Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8d7e9dc87d28a53dd92c49ada32", "usage": { "input_tokens": 93263, "cached_input_tokens": 92800, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 93347 }, "turn_token_usage": { "input_tokens": 7175378, "cached_input_tokens": 7071488, "cache_write_input_tokens": 0, "output_tokens": 55781, "reasoning_output_tokens": 20990, "total_tokens": 7231159 }, "thread_token_usage": { "input_tokens": 7175378, "cached_input_tokens": 7071488, "cache_write_input_tokens": 0, "output_tokens": 55781, "reasoning_output_tokens": 20990, "total_tokens": 7231159 } } }, { "kind": "context", "timestamp": "2026-09-25T01:05:30.451Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e62a0bb8-b343-4df8-95af-9a5d70d8079a", "process_id": "41275", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4031 }, "formatted_output": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n" } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-25T01:05:33.458Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:33.458Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:34.833Z", "callId": "call_6wI52ynjAFWC41LKl4VHgXw3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:05:35.999Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=54733", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:35.016Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8daaea887d2be15017e12a09c85", "usage": { "input_tokens": 93399, "cached_input_tokens": 93056, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 15, "total_tokens": 93499 }, "turn_token_usage": { "input_tokens": 7268777, "cached_input_tokens": 7164544, "cache_write_input_tokens": 0, "output_tokens": 55881, "reasoning_output_tokens": 21005, "total_tokens": 7324658 }, "thread_token_usage": { "input_tokens": 7268777, "cached_input_tokens": 7164544, "cache_write_input_tokens": 0, "output_tokens": 55881, "reasoning_output_tokens": 21005, "total_tokens": 7324658 } } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-25T01:05:37.488Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:37.488Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:38.164Z", "callId": "call_LnuigPZloftwRJbCgjvFpZqg", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:08.207Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:38.302Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8e03d1887d291ada84315ccda15", "usage": { "input_tokens": 93527, "cached_input_tokens": 93184, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93580 }, "turn_token_usage": { "input_tokens": 7362304, "cached_input_tokens": 7257728, "cache_write_input_tokens": 0, "output_tokens": 55934, "reasoning_output_tokens": 21011, "total_tokens": 7418238 }, "thread_token_usage": { "input_tokens": 7362304, "cached_input_tokens": 7257728, "cache_write_input_tokens": 0, "output_tokens": 55934, "reasoning_output_tokens": 21011, "total_tokens": 7418238 } } } ] }, { "id": 129, "role": "agent", "timestamp": "2026-09-25T01:06:09.545Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:09.545Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:10.211Z", "callId": "call_IhyWujwnARUIEXibe5OhiKsY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:40.235Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:10.338Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c900650487d2851f664b4fbf87c4", "usage": { "input_tokens": 93666, "cached_input_tokens": 93312, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 93721 }, "turn_token_usage": { "input_tokens": 7455970, "cached_input_tokens": 7351040, "cache_write_input_tokens": 0, "output_tokens": 55989, "reasoning_output_tokens": 21019, "total_tokens": 7511959 }, "thread_token_usage": { "input_tokens": 7455970, "cached_input_tokens": 7351040, "cache_write_input_tokens": 0, "output_tokens": 55989, "reasoning_output_tokens": 21019, "total_tokens": 7511959 } } } ] }, { "id": 130, "role": "agent", "timestamp": "2026-09-25T01:06:41.606Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:41.606Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:42.284Z", "callId": "call_9g7n4EZiOH3leBYuzPk8e679", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:07:12.294Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:42.411Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c92073c087d29486de49af705298", "usage": { "input_tokens": 93795, "cached_input_tokens": 93440, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 93849 }, "turn_token_usage": { "input_tokens": 7549765, "cached_input_tokens": 7444480, "cache_write_input_tokens": 0, "output_tokens": 56043, "reasoning_output_tokens": 21026, "total_tokens": 7605808 }, "thread_token_usage": { "input_tokens": 7549765, "cached_input_tokens": 7444480, "cache_write_input_tokens": 0, "output_tokens": 56043, "reasoning_output_tokens": 21026, "total_tokens": 7605808 } } } ] }, { "id": 131, "role": "agent", "timestamp": "2026-09-25T01:07:13.587Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:13.587Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:07:14.289Z", "callId": "call_PCczFFHkKemWqUNETPNgUyv4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:07:44.299Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:07:14.421Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c94080e087d281fd393bde56e60e", "usage": { "input_tokens": 93923, "cached_input_tokens": 93568, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93976 }, "turn_token_usage": { "input_tokens": 7643688, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 56096, "reasoning_output_tokens": 21032, "total_tokens": 7699784 }, "thread_token_usage": { "input_tokens": 7643688, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 56096, "reasoning_output_tokens": 21032, "total_tokens": 7699784 } } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-09-25T01:07:50.778Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:50.778Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:07:50.783Z", "text": "The final refit has 100,053 rolling history examples and 846,369 future-relevance pairs, including the now-observed validation week. Training is progressing normally; the original trend-only test submission remains safely in place until the 97-dimensional ensemble ranking is atomically complete." }, { "kind": "tool", "timestamp": "2026-09-25T01:07:50.804Z", "callId": "call_lSkoyw42NNAtOMyy8u4nh5MR", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:08:20.811Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:07:50.871Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c96082bc87d28cbce5d0f16348b0", "usage": { "input_tokens": 94050, "cached_input_tokens": 93696, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 6, "total_tokens": 94167 }, "turn_token_usage": { "input_tokens": 7737738, "cached_input_tokens": 7631744, "cache_write_input_tokens": 0, "output_tokens": 56213, "reasoning_output_tokens": 21038, "total_tokens": 7793951 }, "thread_token_usage": { "input_tokens": 7737738, "cached_input_tokens": 7631744, "cache_write_input_tokens": 0, "output_tokens": 56213, "reasoning_output_tokens": 21038, "total_tokens": 7793951 } } } ] }, { "id": 133, "role": "agent", "timestamp": "2026-09-25T01:08:22.204Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:22.204Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:08:22.889Z", "callId": "call_MdBE49FOjxH5AnBHbje86vGo", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:08:52.900Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:08:23.031Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c98510e087d2a99559f8b8923c12", "usage": { "input_tokens": 94258, "cached_input_tokens": 93824, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 94311 }, "turn_token_usage": { "input_tokens": 7831996, "cached_input_tokens": 7725568, "cache_write_input_tokens": 0, "output_tokens": 56266, "reasoning_output_tokens": 21044, "total_tokens": 7888262 }, "thread_token_usage": { "input_tokens": 7831996, "cached_input_tokens": 7725568, "cache_write_input_tokens": 0, "output_tokens": 56266, "reasoning_output_tokens": 21044, "total_tokens": 7888262 } } }, { "kind": "context", "timestamp": "2026-09-25T01:08:36.351Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76843ead-3fa0-48df-b001-08ca9f2dc248", "process_id": "96937", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\n# variants intrinsic catalog columns\norders={\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\n}\nfor name,order in orders.items():\n out=[]\n for r,u in enumerate(t):\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\n take=order[mask[order]][:100]\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\n print(p)\nPY\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\n# variants intrinsic catalog columns\norders={\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\n}\nfor name,order in orders.items():\n out=[]\n for r,u in enumerate(t):\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\n take=order[mask[order]][:100]\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\n print(p)\nPY\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2455, "nanos": 513734558 }, "formatted_output": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 134, "role": "agent", "timestamp": "2026-09-25T01:08:54.403Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:54.403Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:08:55.093Z", "callId": "call_xEUs3F1QBOBTEQjkntES0mSj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:09:25.106Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:08:55.237Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9a5236887d28854abf2e3e891fd", "usage": { "input_tokens": 94402, "cached_input_tokens": 94080, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 94458 }, "turn_token_usage": { "input_tokens": 7926398, "cached_input_tokens": 7819648, "cache_write_input_tokens": 0, "output_tokens": 56322, "reasoning_output_tokens": 21053, "total_tokens": 7982720 }, "thread_token_usage": { "input_tokens": 7926398, "cached_input_tokens": 7819648, "cache_write_input_tokens": 0, "output_tokens": 56322, "reasoning_output_tokens": 21053, "total_tokens": 7982720 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:09:29.077Z", "callId": "call_jaWfg43jsdqZROlxkE94uwq4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:09:59.088Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:29.210Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9c54e5887d2802a9f783052b08a", "usage": { "input_tokens": 94532, "cached_input_tokens": 94208, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 94577 }, "turn_token_usage": { "input_tokens": 8020930, "cached_input_tokens": 7913856, "cache_write_input_tokens": 0, "output_tokens": 56367, "reasoning_output_tokens": 21053, "total_tokens": 8077297 }, "thread_token_usage": { "input_tokens": 8020930, "cached_input_tokens": 7913856, "cache_write_input_tokens": 0, "output_tokens": 56367, "reasoning_output_tokens": 21053, "total_tokens": 8077297 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:10:01.071Z", "callId": "call_ZngKq7K0OODiKMiYA083ORhz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:10:31.083Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:01.213Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9e74bcc87d28bb6d18ece5b5112", "usage": { "input_tokens": 94668, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 94713 }, "turn_token_usage": { "input_tokens": 8115598, "cached_input_tokens": 8008192, "cache_write_input_tokens": 0, "output_tokens": 56412, "reasoning_output_tokens": 21053, "total_tokens": 8172010 }, "thread_token_usage": { "input_tokens": 8115598, "cached_input_tokens": 8008192, "cache_write_input_tokens": 0, "output_tokens": 56412, "reasoning_output_tokens": 21053, "total_tokens": 8172010 } } } ] }, { "id": 135, "role": "agent", "timestamp": "2026-09-25T01:10:33.475Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:33.475Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:34.165Z", "callId": "call_j0ioZJBlrroDr65t5TWCgUn9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:11:04.190Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:34.305Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca074cb487d2ad1226acb2f0ea2d", "usage": { "input_tokens": 94763, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 31, "total_tokens": 94841 }, "turn_token_usage": { "input_tokens": 8210361, "cached_input_tokens": 8102656, "cache_write_input_tokens": 0, "output_tokens": 56490, "reasoning_output_tokens": 21084, "total_tokens": 8266851 }, "thread_token_usage": { "input_tokens": 8210361, "cached_input_tokens": 8102656, "cache_write_input_tokens": 0, "output_tokens": 56490, "reasoning_output_tokens": 21084, "total_tokens": 8266851 } } } ] }, { "id": 136, "role": "agent", "timestamp": "2026-09-25T01:11:05.668Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:05.668Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:11:06.360Z", "callId": "call_0TpJ90ZQM70gmWHb3NZBASKJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:11:36.369Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:06.503Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca287a4c87d2b841d3e2436f08e8", "usage": { "input_tokens": 94917, "cached_input_tokens": 94592, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 94974 }, "turn_token_usage": { "input_tokens": 8305278, "cached_input_tokens": 8197248, "cache_write_input_tokens": 0, "output_tokens": 56547, "reasoning_output_tokens": 21094, "total_tokens": 8361825 }, "thread_token_usage": { "input_tokens": 8305278, "cached_input_tokens": 8197248, "cache_write_input_tokens": 0, "output_tokens": 56547, "reasoning_output_tokens": 21094, "total_tokens": 8361825 } } } ] }, { "id": 137, "role": "agent", "timestamp": "2026-09-25T01:11:37.943Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:37.943Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:11:38.642Z", "callId": "call_T7j8uh82z2EVfLm8TUbOORei", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:12:08.653Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:38.788Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca48a70887d28bd9680ade0215b6", "usage": { "input_tokens": 95065, "cached_input_tokens": 94720, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 95118 }, "turn_token_usage": { "input_tokens": 8400343, "cached_input_tokens": 8291968, "cache_write_input_tokens": 0, "output_tokens": 56600, "reasoning_output_tokens": 21100, "total_tokens": 8456943 }, "thread_token_usage": { "input_tokens": 8400343, "cached_input_tokens": 8291968, "cache_write_input_tokens": 0, "output_tokens": 56600, "reasoning_output_tokens": 21100, "total_tokens": 8456943 } } } ] }, { "id": 138, "role": "agent", "timestamp": "2026-09-25T01:12:10.072Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:10.072Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:10.753Z", "callId": "call_QpIFbBsSyjz60NDmNPG7ztUU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:12:40.491Z", "content": "Script completed\nWall time 29.7 seconds\nOutput:\n\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:10.870Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca68df5487d297e0dbc104624a38", "usage": { "input_tokens": 95192, "cached_input_tokens": 94848, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 95245 }, "turn_token_usage": { "input_tokens": 8495535, "cached_input_tokens": 8386816, "cache_write_input_tokens": 0, "output_tokens": 56653, "reasoning_output_tokens": 21106, "total_tokens": 8552188 }, "thread_token_usage": { "input_tokens": 8495535, "cached_input_tokens": 8386816, "cache_write_input_tokens": 0, "output_tokens": 56653, "reasoning_output_tokens": 21106, "total_tokens": 8552188 } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:40.489Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6bacbb60-086f-4416-abb6-70a74bccbe84", "process_id": "54733", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u build_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u build_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n", "stderr": "", "aggregated_output": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n", "exit_code": 0, "duration": { "secs": 425, "nanos": 492150204 }, "formatted_output": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n" } } } ] }, { "id": 139, "role": "agent", "timestamp": "2026-09-25T01:12:48.817Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:48.817Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:12:50.122Z", "text": "The train+validation refit is complete, and the final 845,600-row submission has replaced the fallback atomically. I’m doing the last acceptance checks now: schema/ranks, per-user uniqueness, packed eligibility, and exact reproduction of every submitted order from the saved user/item dot-product vectors." }, { "kind": "tool", "timestamp": "2026-09-25T01:13:02.365Z", "callId": "call_kVeM1MS8vEYxelIlsjmozASJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); S=Path('/app/solution')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\\nassert eligible.all()\\nwith np.load(S/'deployment_vectors.npz') as v:\\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\\nassert np.array_equal(submitted,reproduced)\\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\nls -lh /app/solution | sort -k5h | tail -25\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:03.537Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=45802", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:02.557Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca88af5c87d29644c14972a8fdab", "usage": { "input_tokens": 95469, "cached_input_tokens": 94976, "cache_write_input_tokens": 0, "output_tokens": 1151, "reasoning_output_tokens": 396, "total_tokens": 96620 }, "turn_token_usage": { "input_tokens": 8591004, "cached_input_tokens": 8481792, "cache_write_input_tokens": 0, "output_tokens": 57804, "reasoning_output_tokens": 21502, "total_tokens": 8648808 }, "thread_token_usage": { "input_tokens": 8591004, "cached_input_tokens": 8481792, "cache_write_input_tokens": 0, "output_tokens": 57804, "reasoning_output_tokens": 21502, "total_tokens": 8648808 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:13:06.068Z", "callId": "call_EkP7CULlVf0yK78OIXfBQ0K3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45802,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:13:09.584Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:06.217Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca9fb77c87d2babe39626660a22d", "usage": { "input_tokens": 96648, "cached_input_tokens": 95232, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 96693 }, "turn_token_usage": { "input_tokens": 8687652, "cached_input_tokens": 8577024, "cache_write_input_tokens": 0, "output_tokens": 57849, "reasoning_output_tokens": 21502, "total_tokens": 8745501 }, "thread_token_usage": { "input_tokens": 8687652, "cached_input_tokens": 8577024, "cache_write_input_tokens": 0, "output_tokens": 57849, "reasoning_output_tokens": 21502, "total_tokens": 8745501 } } }, { "kind": "context", "timestamp": "2026-09-25T01:13:09.583Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3d95be46-c8aa-4d97-9a6b-ed3eec77ccfe", "process_id": "45802", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); S=Path('/app/solution')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\nassert eligible.all()\nwith np.load(S/'deployment_vectors.npz') as v:\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -25" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); S=Path('/app/solution')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\nassert eligible.all()\nwith np.load(S/'deployment_vectors.npz') as v:\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -25" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 50051700 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n" } } } ] }, { "id": 140, "role": "agent", "timestamp": "2026-09-25T01:13:20.081Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:13:20.081Z", "text": "" } ] }, { "id": 141, "role": "agent", "timestamp": "2026-09-25T01:13:26.921Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:13:26.921Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:13:51.870Z", "callId": "call_3cA4ChdQW2EF1K6hfi93sqe0", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n self.item_tower = ItemTower(n_items, feature_dim, dim)\\n self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+\\n+class RetrievalUserTower(nn.Module):\\n+ \\\"\\\"\\\"The deployed user tower, combining two trained history resolutions.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: UserTower, unique: UserTower, raw_mix: float = 0.35):\\n+ super().__init__()\\n+ self.raw = raw\\n+ self.unique = unique\\n+ self.raw_mix = float(raw_mix)\\n+\\n+ def forward(\\n+ self,\\n+ raw_inputs: tuple[torch.Tensor, ...],\\n+ unique_inputs: tuple[torch.Tensor, ...],\\n+ trend_weight: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ raw_vector = self.raw(*raw_inputs)\\n+ unique_vector = self.unique(*unique_inputs)\\n+ return torch.cat(\\n+ [\\n+ self.raw_mix * raw_vector,\\n+ (1.0 - self.raw_mix) * unique_vector,\\n+ trend_weight.reshape(-1, 1),\\n+ ],\\n+ dim=1,\\n+ )\\n+\\n+\\n+class RetrievalItemTower(nn.Module):\\n+ \\\"\\\"\\\"The independently precomputable deployed catalog-item tower.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: ItemTower, unique: ItemTower):\\n+ super().__init__()\\n+ self.raw = raw\\n+ self.unique = unique\\n+\\n+ def forward(\\n+ self,\\n+ item: torch.Tensor,\\n+ features: torch.Tensor,\\n+ trend: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ return torch.cat(\\n+ [\\n+ self.raw(item, features),\\n+ self.unique(item, features),\\n+ trend.reshape(-1, 1),\\n+ ],\\n+ dim=1,\\n+ )\\n+\\n+\\n+class RetrievalTwoTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment contract: relevance is exactly ``user_vector @ item_vector``.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: TwoTower, unique: TwoTower, raw_mix: float = 0.35):\\n+ super().__init__()\\n+ self.user_tower = RetrievalUserTower(raw.user_tower, unique.user_tower, raw_mix)\\n+ self.item_tower = RetrievalItemTower(raw.item_tower, unique.item_tower)\\n+\\n+ @staticmethod\\n+ def score(user_vector: torch.Tensor, item_vector: torch.Tensor) -> torch.Tensor:\\n+ return user_vector @ item_vector.T\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\\n+each of 8,456 users), and was checked for schema, unique ranks/items, packed\\n+test eligibility, and exact reproduction from the saved MIPS vectors.\\n+\\n+## Model\\n+\\n+The deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\\n+user-ID lookup and never builds or factorizes a user-item matrix.\\n+\\n+- The user tower pools the last 160 conventional-positive catalog events with\\n+ recency, organic, and completion weights. A second view pools the last 160\\n+ *distinct* positive tracks. Active liked and disliked tracks are separate\\n+ embedding pools sourced from the cutoff-correct preference state. Each view\\n+ passes its pooled history and four history statistics through a two-layer\\n+ GELU MLP and produces a normalized 48-dimensional vector.\\n+- The item tower combines a trainable catalog-ID embedding with a residual MLP\\n+ over log-popularity and organic-share features, producing a normalized\\n+ 48-dimensional vector for each history view.\\n+- The two 48-dimensional spaces are concatenated with one offline item-trend\\n+ dimension. Trend uses organic 80%-complete listens and organic likes with a\\n+ three-day half-life. Its user-side coefficient depends only on 90-day\\n+ history activity. Thus the final 97-dimensional relevance score is exactly\\n+ one user-vector/item-vector dot product. Candidate vectors can be computed\\n+ offline; request time needs one history-derived user vector and MIPS.\\n+\\n+`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\\n+`train_ranker.py` define the deployment contract. The exact fitted vectors are\\n+saved in `deployment_vectors.npz`; the two fitted checkpoints are\\n+`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\\n+\\n+## Training\\n+\\n+Training examples use 30 rolling cutoff-correct backtests. A history predicts\\n+organic first-discovery tracks in the next seven days after a 30-minute gap.\\n+The loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\\n+3.5 times as often as grade-1 strong listens. This is event-sampled and scales\\n+linearly with interaction records and minibatches. The final models are refit\\n+on `train_* + val_*`; validation events are then history/training data, never\\n+hidden outcomes.\\n+\\n+Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\\n+with `/task/evaluate_public.py`. The corresponding ranking is\\n+`validation.parquet`.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app/solution` with the supplied environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python build_submission.py\\n+```\\n+\\n+This writes checkpoints and vectors under `/app/solution/`, then atomically\\n+replaces `/app/solution/submission.parquet` only after ranking succeeds.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:13:51.988Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:51.887Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0ce43e12-2a12-4b38-949b-3dc007fa3fe9", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\ntest eligibility, and exact reproduction from the saved MIPS vectors.\n\n## Model\n\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\nuser-ID lookup and never builds or factorizes a user-item matrix.\n\n- The user tower pools the last 160 conventional-positive catalog events with\n recency, organic, and completion weights. A second view pools the last 160\n *distinct* positive tracks. Active liked and disliked tracks are separate\n embedding pools sourced from the cutoff-correct preference state. Each view\n passes its pooled history and four history statistics through a two-layer\n GELU MLP and produces a normalized 48-dimensional vector.\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\n over log-popularity and organic-share features, producing a normalized\n 48-dimensional vector for each history view.\n- The two 48-dimensional spaces are concatenated with one offline item-trend\n dimension. Trend uses organic 80%-complete listens and organic likes with a\n three-day half-life. Its user-side coefficient depends only on 90-day\n history activity. Thus the final 97-dimensional relevance score is exactly\n one user-vector/item-vector dot product. Candidate vectors can be computed\n offline; request time needs one history-derived user vector and MIPS.\n\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\n\n## Training\n\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\norganic first-discovery tracks in the next seven days after a 30-minute gap.\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\nlinearly with interaction records and minibatches. The final models are refit\non `train_* + val_*`; validation events are then history/training data, never\nhidden outcomes.\n\nPublic validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\nwith `/task/evaluate_public.py`. The corresponding ranking is\n`validation.parquet`.\n\n## Reproduce the final submission\n\nFrom `/app/solution` with the supplied environment:\n\n```sh\n/opt/task-venv/bin/python build_submission.py\n```\n\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\n" }, "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -378,2 +378,66 @@\n \n+class RetrievalUserTower(nn.Module):\n+ \"\"\"The deployed user tower, combining two trained history resolutions.\"\"\"\n+\n+ def __init__(self, raw: UserTower, unique: UserTower, raw_mix: float = 0.35):\n+ super().__init__()\n+ self.raw = raw\n+ self.unique = unique\n+ self.raw_mix = float(raw_mix)\n+\n+ def forward(\n+ self,\n+ raw_inputs: tuple[torch.Tensor, ...],\n+ unique_inputs: tuple[torch.Tensor, ...],\n+ trend_weight: torch.Tensor,\n+ ) -> torch.Tensor:\n+ raw_vector = self.raw(*raw_inputs)\n+ unique_vector = self.unique(*unique_inputs)\n+ return torch.cat(\n+ [\n+ self.raw_mix * raw_vector,\n+ (1.0 - self.raw_mix) * unique_vector,\n+ trend_weight.reshape(-1, 1),\n+ ],\n+ dim=1,\n+ )\n+\n+\n+class RetrievalItemTower(nn.Module):\n+ \"\"\"The independently precomputable deployed catalog-item tower.\"\"\"\n+\n+ def __init__(self, raw: ItemTower, unique: ItemTower):\n+ super().__init__()\n+ self.raw = raw\n+ self.unique = unique\n+\n+ def forward(\n+ self,\n+ item: torch.Tensor,\n+ features: torch.Tensor,\n+ trend: torch.Tensor,\n+ ) -> torch.Tensor:\n+ return torch.cat(\n+ [\n+ self.raw(item, features),\n+ self.unique(item, features),\n+ trend.reshape(-1, 1),\n+ ],\n+ dim=1,\n+ )\n+\n+\n+class RetrievalTwoTower(nn.Module):\n+ \"\"\"Deployment contract: relevance is exactly ``user_vector @ item_vector``.\"\"\"\n+\n+ def __init__(self, raw: TwoTower, unique: TwoTower, raw_mix: float = 0.35):\n+ super().__init__()\n+ self.user_tower = RetrievalUserTower(raw.user_tower, unique.user_tower, raw_mix)\n+ self.item_tower = RetrievalItemTower(raw.item_tower, unique.item_tower)\n+\n+ @staticmethod\n+ def score(user_vector: torch.Tensor, item_vector: torch.Tensor) -> torch.Tensor:\n+ return user_vector @ item_vector.T\n+\n+\n def train_model(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:13:51.986Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5caa5c76487d280fc405cc0056a09", "usage": { "input_tokens": 97503, "cached_input_tokens": 96512, "cache_write_input_tokens": 0, "output_tokens": 2284, "reasoning_output_tokens": 889, "total_tokens": 99787 }, "turn_token_usage": { "input_tokens": 8785155, "cached_input_tokens": 8673536, "cache_write_input_tokens": 0, "output_tokens": 60133, "reasoning_output_tokens": 22391, "total_tokens": 8845288 }, "thread_token_usage": { "input_tokens": 8785155, "cached_input_tokens": 8673536, "cache_write_input_tokens": 0, "output_tokens": 60133, "reasoning_output_tokens": 22391, "total_tokens": 8845288 } } } ] }, { "id": 142, "role": "agent", "timestamp": "2026-09-25T01:14:02.719Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:02.719Z", "text": "" } ] }, { "id": 143, "role": "agent", "timestamp": "2026-09-25T01:14:12.044Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:12.044Z", "text": "" } ] }, { "id": 144, "role": "agent", "timestamp": "2026-09-25T01:14:17.024Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:17.024Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:14:18.455Z", "text": "All acceptance checks pass: 8,456 users, 100 unique eligible items each, and the saved 97-dimensional vectors reproduce the submission exactly by dot product. I’m doing one final low-risk check on whether distinct-listener trend counts outperform raw event counts; the validated submission will only change if this improves the public backtest." }, { "kind": "tool", "timestamp": "2026-09-25T01:14:30.793Z", "callId": "call_qT1kGFubdAQON9DzQpkd6USq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\\nfor fam in ['listens','likes']:\\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\\n a=np.zeros((nd,len(ids)),np.float32)\\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\\n# public global score helper\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\\ndef ev(s):\\n order=np.argsort(-s,kind='stable')[:400]; q=[]\\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\\n return np.mean(q)\\nres=[]\\nfor half in [1,1.5,2,3,4,5,7,10]:\\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\\n for aw in [7,15,25,35,50]:\\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\\nprint(*sorted(res,reverse=True)[:25],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:31.973Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=63104", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:30.936Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cad06ecc87d28211d7d6646a8896", "usage": { "input_tokens": 99811, "cached_input_tokens": 97280, "cache_write_input_tokens": 0, "output_tokens": 2078, "reasoning_output_tokens": 1310, "total_tokens": 101889 }, "turn_token_usage": { "input_tokens": 8884966, "cached_input_tokens": 8770816, "cache_write_input_tokens": 0, "output_tokens": 62211, "reasoning_output_tokens": 23701, "total_tokens": 8947177 }, "thread_token_usage": { "input_tokens": 8884966, "cached_input_tokens": 8770816, "cache_write_input_tokens": 0, "output_tokens": 62211, "reasoning_output_tokens": 23701, "total_tokens": 8947177 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:14:33.847Z", "callId": "call_cpzg6X0uY5A9xK1xbLx0peSs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":63104,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:14:57.922Z", "content": "Script completed\nWall time 24.1 seconds\nOutput:\n\nlistens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:34.017Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5caf8314487d28435af1ce9b44ab3", "usage": { "input_tokens": 101917, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 101962 }, "turn_token_usage": { "input_tokens": 8986883, "cached_input_tokens": 8870400, "cache_write_input_tokens": 0, "output_tokens": 62256, "reasoning_output_tokens": 23701, "total_tokens": 9049139 }, "thread_token_usage": { "input_tokens": 8986883, "cached_input_tokens": 8870400, "cache_write_input_tokens": 0, "output_tokens": 62256, "reasoning_output_tokens": 23701, "total_tokens": 9049139 } } }, { "kind": "context", "timestamp": "2026-09-25T01:14:57.920Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c9e5b42f-4425-472e-9b49-4be5e8fa9fb9", "process_id": "63104", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\n a=np.zeros((nd,len(ids)),np.float32)\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\n# public global score helper\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\ndef ev(s):\n order=np.argsort(-s,kind='stable')[:400]; q=[]\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\n return np.mean(q)\nres=[]\nfor half in [1,1.5,2,3,4,5,7,10]:\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\n for aw in [7,15,25,35,50]:\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\nprint(*sorted(res,reverse=True)[:25],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\n a=np.zeros((nd,len(ids)),np.float32)\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\n# public global score helper\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\ndef ev(s):\n order=np.argsort(-s,kind='stable')[:400]; q=[]\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\n return np.mean(q)\nres=[]\nfor half in [1,1.5,2,3,4,5,7,10]:\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\n for aw in [7,15,25,35,50]:\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\nprint(*sorted(res,reverse=True)[:25],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n", "stderr": "", "aggregated_output": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 948388009 }, "formatted_output": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n" } } } ] }, { "id": 145, "role": "agent", "timestamp": "2026-09-25T01:15:02.039Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:15:02.039Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:15:07.754Z", "callId": "call_LwDI8oliPHyfPlkoBECaLFXb", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\\nnp.save('validation_unique_trend.npy',trend)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:15:08.919Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=6330", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:15:07.936Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb1222f487d2b8dc890ab098acf0", "usage": { "input_tokens": 102614, "cached_input_tokens": 101760, "cache_write_input_tokens": 0, "output_tokens": 480, "reasoning_output_tokens": 154, "total_tokens": 103094 }, "turn_token_usage": { "input_tokens": 9089497, "cached_input_tokens": 8972160, "cache_write_input_tokens": 0, "output_tokens": 62736, "reasoning_output_tokens": 23855, "total_tokens": 9152233 }, "thread_token_usage": { "input_tokens": 9089497, "cached_input_tokens": 8972160, "cache_write_input_tokens": 0, "output_tokens": 62736, "reasoning_output_tokens": 23855, "total_tokens": 9152233 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:15:10.841Z", "callId": "call_hmoKLxfBdxr3c8h0fXKBp7W2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6330,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:15:24.469Z", "content": "Script completed\nWall time 13.6 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:15:10.976Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb1d1de087d28a4365a2a1484f0a", "usage": { "input_tokens": 103122, "cached_input_tokens": 102400, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 103167 }, "turn_token_usage": { "input_tokens": 9192619, "cached_input_tokens": 9074560, "cache_write_input_tokens": 0, "output_tokens": 62781, "reasoning_output_tokens": 23855, "total_tokens": 9255400 }, "thread_token_usage": { "input_tokens": 9192619, "cached_input_tokens": 9074560, "cache_write_input_tokens": 0, "output_tokens": 62781, "reasoning_output_tokens": 23855, "total_tokens": 9255400 } } }, { "kind": "context", "timestamp": "2026-09-25T01:15:24.467Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07b1c341-8751-4e6e-a1f0-ca449b56154c", "process_id": "6330", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\nnp.save('validation_unique_trend.npy',trend)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\nnp.save('validation_unique_trend.npy',trend)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 550098409 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n" } } } ] }, { "id": 146, "role": "agent", "timestamp": "2026-09-25T01:15:28.477Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:15:28.477Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:15:36.099Z", "callId": "call_ADeWev3T2AAm8bskpGEVpTN5", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\\nprint(*sorted(res,reverse=True)[:40],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:15:37.276Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=31638", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:15:36.281Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb2ca95c87d29ccf057b78a3d27b", "usage": { "input_tokens": 103489, "cached_input_tokens": 102912, "cache_write_input_tokens": 0, "output_tokens": 568, "reasoning_output_tokens": 136, "total_tokens": 104057 }, "turn_token_usage": { "input_tokens": 9296108, "cached_input_tokens": 9177472, "cache_write_input_tokens": 0, "output_tokens": 63349, "reasoning_output_tokens": 23991, "total_tokens": 9359457 }, "thread_token_usage": { "input_tokens": 9296108, "cached_input_tokens": 9177472, "cache_write_input_tokens": 0, "output_tokens": 63349, "reasoning_output_tokens": 23991, "total_tokens": 9359457 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:15:39.187Z", "callId": "call_Bt5VjoX1dsOFZwG8GSt4a1iF", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:09.195Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:15:39.321Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb39823887d2bb9cbe69b6f96adc", "usage": { "input_tokens": 104085, "cached_input_tokens": 103296, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 104130 }, "turn_token_usage": { "input_tokens": 9400193, "cached_input_tokens": 9280768, "cache_write_input_tokens": 0, "output_tokens": 63394, "reasoning_output_tokens": 23991, "total_tokens": 9463587 }, "thread_token_usage": { "input_tokens": 9400193, "cached_input_tokens": 9280768, "cache_write_input_tokens": 0, "output_tokens": 63394, "reasoning_output_tokens": 23991, "total_tokens": 9463587 } } } ] }, { "id": 147, "role": "agent", "timestamp": "2026-09-25T01:16:10.657Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:10.657Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:11.355Z", "callId": "call_mDCqrAEhDdWwmWYqdzPdKsCl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:41.363Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:11.479Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb59673c87d2a556d48867f9fe1f", "usage": { "input_tokens": 104153, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 104206 }, "turn_token_usage": { "input_tokens": 9504346, "cached_input_tokens": 9384704, "cache_write_input_tokens": 0, "output_tokens": 63447, "reasoning_output_tokens": 23997, "total_tokens": 9567793 }, "thread_token_usage": { "input_tokens": 9504346, "cached_input_tokens": 9384704, "cache_write_input_tokens": 0, "output_tokens": 63447, "reasoning_output_tokens": 23997, "total_tokens": 9567793 } } } ] }, { "id": 148, "role": "agent", "timestamp": "2026-09-25T01:16:43.529Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:43.529Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:44.231Z", "callId": "call_O7QJCb5Xq2ZPB1TYesnTBTmE", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:59.238Z", "content": "Script completed\nWall time 15.0 seconds\nOutput:\n\n(np.float64(0.03520480844257275), 0.4, 4, 0)\r\n(np.float64(0.03503369282950995), 0.55, 4, 0)\r\n(np.float64(0.03502223888855064), 0.55, 6, 0)\r\n(np.float64(0.03499294910132534), 0.4, 6, 0)\r\n(np.float64(0.03497055975199991), 0.4, 7, 0)\r\n(np.float64(0.03492307949567489), 0.55, 7, 0)\r\n(np.float64(0.034879412801696104), 0.85, 6, 0)\r\n(np.float64(0.03485539596909437), 0.7, 4, 0)\r\n(np.float64(0.03484229945616528), 0.25, 4, 0)\r\n(np.float64(0.034841446363221805), 0.7, 6, 0)\r\n(np.float64(0.03483888997440489), 1.2, 8, 0.01)\r\n(np.float64(0.0348087619021521), 0.85, 7, 0)\r\n(np.float64(0.034748880425747836), 0.7, 7, 0)\r\n(np.float64(0.034701748159511284), 0.85, 20, 0.03)\r\n(np.float64(0.03469134251635742), 1, 10, 0.01)\r\n(np.float64(0.03468546354129284), 0.7, 12, 0.01)\r\n(np.float64(0.03468378061787328), 0.85, 12, 0.01)\r\n(np.float64(0.034682499011860254), 0.7, 8, 0)\r\n(np.float64(0.03467125328354198), 1, 8, 0)\r\n(np.float64(0.03465612430180013), 0.85, 4, 0)\r\n(np.float64(0.03464078778693491), 1, 12, 0.01)\r\n(np.float64(0.034629704726101565), 0.85, 10, 0.01)\r\n(np.float64(0.03462828331698332), 0.55, 8, 0)\r\n(np.float64(0.03460415058618136), 1.5, 8, 0.01)\r\n(np.float64(0.03460374111315636), 0.4, 8, 0)\r\n(np.float64(0.03456096451621286), 1, 4, 0)\r\n(np.float64(0.03455144059407113), 1, 10, 0)\r\n(np.float64(0.03454478942555659), 0.85, 8, 0)\r\n(np.float64(0.03453365746593463), 1, 20, 0.03)\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03450592591175055), 1.2, 8, 0)\r\n(np.float64(0.03449985645761904), 0.85, 10, 0)\r\n(np.float64(0.03448149966852392), 1.2, 30, 0.03)\r\n(np.float64(0.034455745227545985), 1.2, 10, 0)\r\n(np.float64(0.034455059058202016), 1.2, 10, 0.01)\r\n(np.float64(0.03445079038658426), 1, 10, 0.03)\r\n(np.float64(0.034444913265577866), 0.55, 10, 0)\r\n(np.float64(0.034417653194543624), 0.7, 15, 0.01)\r\n(np.float64(0.03440648387823677), 1, 30, 0.03)\r\n(np.float64(0.03439880588324988), 0.25, 6, 0)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:44.366Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb79907087d285d70ab411050501", "usage": { "input_tokens": 104229, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 67, "reasoning_output_tokens": 20, "total_tokens": 104296 }, "turn_token_usage": { "input_tokens": 9608575, "cached_input_tokens": 9488640, "cache_write_input_tokens": 0, "output_tokens": 63514, "reasoning_output_tokens": 24017, "total_tokens": 9672089 }, "thread_token_usage": { "input_tokens": 9608575, "cached_input_tokens": 9488640, "cache_write_input_tokens": 0, "output_tokens": 63514, "reasoning_output_tokens": 24017, "total_tokens": 9672089 } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:59.236Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f918c2de-254e-4569-97b9-2467f2015d7c", "process_id": "31638", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\nprint(*sorted(res,reverse=True)[:40],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\nprint(*sorted(res,reverse=True)[:40],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.03520480844257275), 0.4, 4, 0)\r\n(np.float64(0.03503369282950995), 0.55, 4, 0)\r\n(np.float64(0.03502223888855064), 0.55, 6, 0)\r\n(np.float64(0.03499294910132534), 0.4, 6, 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"timestamp": "2026-09-25T01:17:01.877Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:01.877Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:03.089Z", "callId": "call_UKJyofQIjGiUdvorkstZQR2U", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nprint(24786800%86400,25393400%86400)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:17:03.236Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n76400 78200\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:03.138Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-099ac669-8603-4ea2-9964-960970a7a8c4", "process_id": "77731", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nprint(24786800%86400,25393400%86400)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nprint(24786800%86400,25393400%86400)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "76400 78200\n", "stderr": "", "aggregated_output": "76400 78200\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7870 }, "formatted_output": "76400 78200\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:03.234Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb8b700087d2a92fdb26078cbc7f", "usage": { "input_tokens": 105289, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 153, "reasoning_output_tokens": 81, "total_tokens": 105442 }, "turn_token_usage": { "input_tokens": 9713864, "cached_input_tokens": 9592704, "cache_write_input_tokens": 0, "output_tokens": 63667, "reasoning_output_tokens": 24098, "total_tokens": 9777531 }, "thread_token_usage": { "input_tokens": 9713864, "cached_input_tokens": 9592704, "cache_write_input_tokens": 0, "output_tokens": 63667, "reasoning_output_tokens": 24098, "total_tokens": 9777531 } } } ] }, { "id": 150, "role": "agent", "timestamp": "2026-09-25T01:17:05.110Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:05.110Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:10.772Z", "callId": "call_trUT5YbuQwC2mKES6G8wqDzz", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:17:11.937Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=2261", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:10.950Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb8f895c87d285f65f9bcbfe8a2e", "usage": { "input_tokens": 105471, "cached_input_tokens": 105088, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 34, "total_tokens": 105829 }, "turn_token_usage": { "input_tokens": 9819335, "cached_input_tokens": 9697792, "cache_write_input_tokens": 0, "output_tokens": 64025, "reasoning_output_tokens": 24132, "total_tokens": 9883360 }, "thread_token_usage": { "input_tokens": 9819335, "cached_input_tokens": 9697792, "cache_write_input_tokens": 0, "output_tokens": 64025, "reasoning_output_tokens": 24132, "total_tokens": 9883360 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:17:14.068Z", "callId": "call_mfqY28IXrSNFmIRvRHLtyJb1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2261,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:17:27.528Z", "content": "Script completed\nWall time 13.5 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:14.220Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb982d9087d28bb2bbf2997e6e81", "usage": { "input_tokens": 105857, "cached_input_tokens": 105344, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 105902 }, "turn_token_usage": { "input_tokens": 9925192, "cached_input_tokens": 9803136, "cache_write_input_tokens": 0, "output_tokens": 64070, "reasoning_output_tokens": 24132, "total_tokens": 9989262 }, "thread_token_usage": { "input_tokens": 9925192, "cached_input_tokens": 9803136, "cache_write_input_tokens": 0, "output_tokens": 64070, "reasoning_output_tokens": 24132, "total_tokens": 9989262 } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:27.527Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0766c744-25fc-462a-81ca-0f4da3ca179e", "process_id": "2261", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 591164042 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n" } } } ] }, { "id": 151, "role": "agent", "timestamp": "2026-09-25T01:17:32.901Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:32.901Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:43.274Z", "callId": "call_AcnUfKHkmE464Uh0dwspK57w", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\\n for a in aa:\\n s=base+a*t1[cand]\\n for b in bb:\\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\\nprint(*best[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:17:44.438Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=9914", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:43.459Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cba7bdac87d28d2e8a8c78504e43", "usage": { "input_tokens": 106224, "cached_input_tokens": 105728, "cache_write_input_tokens": 0, "output_tokens": 810, "reasoning_output_tokens": 225, "total_tokens": 107034 }, "turn_token_usage": { "input_tokens": 10031416, "cached_input_tokens": 9908864, "cache_write_input_tokens": 0, "output_tokens": 64880, "reasoning_output_tokens": 24357, "total_tokens": 10096296 }, "thread_token_usage": { "input_tokens": 10031416, "cached_input_tokens": 9908864, "cache_write_input_tokens": 0, "output_tokens": 64880, "reasoning_output_tokens": 24357, "total_tokens": 10096296 } } } ] }, { "id": 152, "role": "agent", "timestamp": "2026-09-25T01:17:45.982Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:45.982Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:46.705Z", "callId": "call_eaLZymhRuz9Wm9XibisxOTaR", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":9914,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:12.960Z", "content": "Script completed\nWall time 26.3 seconds\nOutput:\n\n(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:46.818Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbb8a97087d2985dcc74950f9112", "usage": { "input_tokens": 107062, "cached_input_tokens": 105984, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 107115 }, "turn_token_usage": { "input_tokens": 10138478, "cached_input_tokens": 10014848, "cache_write_input_tokens": 0, "output_tokens": 64933, "reasoning_output_tokens": 24363, "total_tokens": 10203411 }, "thread_token_usage": { "input_tokens": 10138478, "cached_input_tokens": 10014848, "cache_write_input_tokens": 0, "output_tokens": 64933, "reasoning_output_tokens": 24363, "total_tokens": 10203411 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:12.958Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b77abf8-3f54-4e08-b402-a9390e5ac4b7", "process_id": "9914", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t1[cand]\n for b in bb:\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nprint(*best[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t1[cand]\n for b in bb:\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nprint(*best[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n", "stderr": "", "aggregated_output": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n", "exit_code": 0, "duration": { "secs": 29, "nanos": 520916244 }, "formatted_output": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n" } } } ] }, { "id": 153, "role": "agent", "timestamp": "2026-09-25T01:18:16.591Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:16.591Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:18:20.977Z", "callId": "call_BloGEgVCc9IHS8bRiT5bV0g8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:18:22.156Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=43374", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:21.125Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbd5263087d2a1959b5230829beb", "usage": { "input_tokens": 107818, "cached_input_tokens": 106880, "cache_write_input_tokens": 0, "output_tokens": 389, "reasoning_output_tokens": 141, "total_tokens": 108207 }, "turn_token_usage": { "input_tokens": 10246296, "cached_input_tokens": 10121728, "cache_write_input_tokens": 0, "output_tokens": 65322, "reasoning_output_tokens": 24504, "total_tokens": 10311618 }, "thread_token_usage": { "input_tokens": 10246296, "cached_input_tokens": 10121728, "cache_write_input_tokens": 0, "output_tokens": 65322, "reasoning_output_tokens": 24504, "total_tokens": 10311618 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:18:24.252Z", "callId": "call_mIaieCUzdv0EMUj8isUSo90n", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":43374,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:30.641Z", "content": "Script completed\nWall time 6.4 seconds\nOutput:\n\n(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:24.413Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbde81fc87d2a2e252660ad3cdd7", "usage": { "input_tokens": 108235, "cached_input_tokens": 107648, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 108280 }, "turn_token_usage": { "input_tokens": 10354531, "cached_input_tokens": 10229376, "cache_write_input_tokens": 0, "output_tokens": 65367, "reasoning_output_tokens": 24504, "total_tokens": 10419898 }, "thread_token_usage": { "input_tokens": 10354531, "cached_input_tokens": 10229376, "cache_write_input_tokens": 0, "output_tokens": 65367, "reasoning_output_tokens": 24504, "total_tokens": 10419898 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:30.639Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31d48d97-5ad2-46d0-ae8a-cd68bc7c80c2", "process_id": "43374", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 484273264 }, "formatted_output": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 154, "role": "agent", "timestamp": "2026-09-25T01:18:33.047Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:33.047Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:18:44.256Z", "callId": "call_Rh0v9HnoDALdBaDGFK8NJW3F", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\\nfor b in range(5):\\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\\n# best schedule total\\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:18:45.422Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=64744", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:44.351Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbe6d8c087d2b8819062b9f2a31e", "usage": { "input_tokens": 108366, "cached_input_tokens": 108032, "cache_write_input_tokens": 0, "output_tokens": 684, "reasoning_output_tokens": 55, "total_tokens": 109050 }, "turn_token_usage": { "input_tokens": 10462897, "cached_input_tokens": 10337408, "cache_write_input_tokens": 0, "output_tokens": 66051, "reasoning_output_tokens": 24559, "total_tokens": 10528948 }, "thread_token_usage": { "input_tokens": 10462897, "cached_input_tokens": 10337408, "cache_write_input_tokens": 0, "output_tokens": 66051, "reasoning_output_tokens": 24559, "total_tokens": 10528948 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:18:47.488Z", "callId": "call_3UWaKZDZAAh6q71j5j9gRpY2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":64744,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:55.822Z", "content": "Script completed\nWall time 8.3 seconds\nOutput:\n\n0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:47.655Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbf5a1b887d29bff278032eb83f7", "usage": { "input_tokens": 109078, "cached_input_tokens": 108160, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 109123 }, "turn_token_usage": { "input_tokens": 10571975, "cached_input_tokens": 10445568, "cache_write_input_tokens": 0, "output_tokens": 66096, "reasoning_output_tokens": 24559, "total_tokens": 10638071 }, "thread_token_usage": { "input_tokens": 10571975, "cached_input_tokens": 10445568, "cache_write_input_tokens": 0, "output_tokens": 66096, "reasoning_output_tokens": 24559, "total_tokens": 10638071 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:55.820Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb56b67f-f99f-4f16-bab9-53fb67e2f2a2", "process_id": "64744", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\n# best schedule total\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\n# best schedule total\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n", "stderr": "", "aggregated_output": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 398789932 }, "formatted_output": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n" } } } ] }, { "id": 155, "role": "agent", "timestamp": "2026-09-25T01:19:00.966Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:00.966Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:10.342Z", "callId": "call_PdWposqdVUNMeg7jQhPlYQQO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:19:11.519Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=18917", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:10.526Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc000de487d2a67da2007bbc6aa4", "usage": { "input_tokens": 110574, "cached_input_tokens": 108928, "cache_write_input_tokens": 0, "output_tokens": 738, "reasoning_output_tokens": 209, "total_tokens": 111312 }, "turn_token_usage": { "input_tokens": 10682549, "cached_input_tokens": 10554496, "cache_write_input_tokens": 0, "output_tokens": 66834, "reasoning_output_tokens": 24768, "total_tokens": 10749383 }, "thread_token_usage": { "input_tokens": 10682549, "cached_input_tokens": 10554496, "cache_write_input_tokens": 0, "output_tokens": 66834, "reasoning_output_tokens": 24768, "total_tokens": 10749383 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:19:13.414Z", "callId": "call_GjeWAa76yrgf8gBd8mE2CpP3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18917,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:19.705Z", "content": "Script completed\nWall time 6.3 seconds\nOutput:\n\n{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:13.568Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc0fb84887d2ac2a8d27447a5e73", "usage": { "input_tokens": 111340, "cached_input_tokens": 110336, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 111385 }, "turn_token_usage": { "input_tokens": 10793889, "cached_input_tokens": 10664832, "cache_write_input_tokens": 0, "output_tokens": 66879, "reasoning_output_tokens": 24768, "total_tokens": 10860768 }, "thread_token_usage": { "input_tokens": 10793889, "cached_input_tokens": 10664832, "cache_write_input_tokens": 0, "output_tokens": 66879, "reasoning_output_tokens": 24768, "total_tokens": 10860768 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:19.703Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7716ef89-bf46-49ae-b6e2-e0c9f6a09c71", "process_id": "18917", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 187025728 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 156, "role": "agent", "timestamp": "2026-09-25T01:19:30.176Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:30.176Z", "text": "" } ] }, { "id": 157, "role": "agent", "timestamp": "2026-09-25T01:19:36.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:36.152Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:19:37.486Z", "text": "The distinct-listener signal is materially better: public NDCG rises from 0.040776 to 0.041837 without changing the neural checkpoints. I’m checking one closely related variant—counting each recent listener only once rather than once per day—before updating the saved final vectors and submission." }, { "kind": "tool", "timestamp": "2026-09-25T01:19:49.009Z", "callId": "call_WChFHanwwBkPMIPtj7X2mQhG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\\nfor fam in ['listens','likes']:\\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\\nprint(*sorted(res,reverse=True)[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:19:50.175Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=78403", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:49.188Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc17ec3c87d28d67cb7f34658aa0", "usage": { "input_tokens": 111456, "cached_input_tokens": 111104, "cache_write_input_tokens": 0, "output_tokens": 1564, "reasoning_output_tokens": 847, "total_tokens": 113020 }, "turn_token_usage": { "input_tokens": 10905345, "cached_input_tokens": 10775936, "cache_write_input_tokens": 0, "output_tokens": 68443, "reasoning_output_tokens": 25615, "total_tokens": 10973788 }, "thread_token_usage": { "input_tokens": 10905345, "cached_input_tokens": 10775936, "cache_write_input_tokens": 0, "output_tokens": 68443, "reasoning_output_tokens": 25615, "total_tokens": 10973788 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:19:52.153Z", "callId": "call_nLl1ibmpOws0O7z8sTqwLqB7", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":78403,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:20:08.230Z", "content": "Script completed\nWall time 16.1 seconds\nOutput:\n\nlistens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:52.287Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc36680087d2af1e0b2957bd7b0c", "usage": { "input_tokens": 113048, "cached_input_tokens": 111232, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 113093 }, "turn_token_usage": { "input_tokens": 11018393, "cached_input_tokens": 10887168, "cache_write_input_tokens": 0, "output_tokens": 68488, "reasoning_output_tokens": 25615, "total_tokens": 11086881 }, "thread_token_usage": { "input_tokens": 11018393, "cached_input_tokens": 10887168, "cache_write_input_tokens": 0, "output_tokens": 68488, "reasoning_output_tokens": 25615, "total_tokens": 11086881 } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:08.228Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-284da1aa-ae0f-4564-9612-4935bc702373", "process_id": "78403", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n", "stderr": "", "aggregated_output": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n", "exit_code": 0, "duration": { "secs": 19, "nanos": 54503753 }, "formatted_output": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n" } } } ] }, { "id": 158, "role": "agent", "timestamp": "2026-09-25T01:20:10.544Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:10.544Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:16.236Z", "callId": "call_FCcr5DvUMo8KIqaRRnacy5vV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:20:17.404Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=77373", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:16.417Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc486c3c87d2ad690c7c6f9c6a17", "usage": { "input_tokens": 113751, "cached_input_tokens": 112896, "cache_write_input_tokens": 0, "output_tokens": 383, "reasoning_output_tokens": 59, "total_tokens": 114134 }, "turn_token_usage": { "input_tokens": 11132144, "cached_input_tokens": 11000064, "cache_write_input_tokens": 0, "output_tokens": 68871, "reasoning_output_tokens": 25674, "total_tokens": 11201015 }, "thread_token_usage": { "input_tokens": 11132144, "cached_input_tokens": 11000064, "cache_write_input_tokens": 0, "output_tokens": 68871, "reasoning_output_tokens": 25674, "total_tokens": 11201015 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:20:19.274Z", "callId": "call_ldHKgSx93e3HlyoMfjLwdTAY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":77373,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:20:33.061Z", "content": "Script completed\nWall time 13.8 seconds\nOutput:\n\nblend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:19.413Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc51993487d296cf2049b104ac41", "usage": { "input_tokens": 114162, "cached_input_tokens": 113536, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 114207 }, "turn_token_usage": { "input_tokens": 11246306, "cached_input_tokens": 11113600, "cache_write_input_tokens": 0, "output_tokens": 68916, "reasoning_output_tokens": 25674, "total_tokens": 11315222 }, "thread_token_usage": { "input_tokens": 11246306, "cached_input_tokens": 11113600, "cache_write_input_tokens": 0, "output_tokens": 68916, "reasoning_output_tokens": 25674, "total_tokens": 11315222 } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:33.059Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34fe8bb0-322a-4a12-8a4d-32973d9a3236", "process_id": "77373", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 655924443 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n" } } } ] }, { "id": 159, "role": "agent", "timestamp": "2026-09-25T01:20:35.031Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:35.031Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:45.746Z", "callId": "call_p2IDndshCStL3llqC2zNeQ89", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\\nfor b in range(5):\\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:20:46.911Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=24499", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:45.939Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc614d5887d29a3ea5f47047cfde", "usage": { "input_tokens": 114529, "cached_input_tokens": 113920, "cache_write_input_tokens": 0, "output_tokens": 640, "reasoning_output_tokens": 36, "total_tokens": 115169 }, "turn_token_usage": { "input_tokens": 11360835, "cached_input_tokens": 11227520, "cache_write_input_tokens": 0, "output_tokens": 69556, "reasoning_output_tokens": 25710, "total_tokens": 11430391 }, "thread_token_usage": { "input_tokens": 11360835, "cached_input_tokens": 11227520, "cache_write_input_tokens": 0, "output_tokens": 69556, "reasoning_output_tokens": 25710, "total_tokens": 11430391 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:20:49.124Z", "callId": "call_HSqFZqgarNWkCf5wQssIe0uI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":24499,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":18000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:20:56.670Z", "content": "Script completed\nWall time 7.5 seconds\nOutput:\n\n0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:49.278Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc6f223487d2a83c0556f4e1ec01", "usage": { "input_tokens": 115197, "cached_input_tokens": 114304, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 115242 }, "turn_token_usage": { "input_tokens": 11476032, "cached_input_tokens": 11341824, "cache_write_input_tokens": 0, "output_tokens": 69601, "reasoning_output_tokens": 25710, "total_tokens": 11545633 }, "thread_token_usage": { "input_tokens": 11476032, "cached_input_tokens": 11341824, "cache_write_input_tokens": 0, "output_tokens": 69601, "reasoning_output_tokens": 25710, "total_tokens": 11545633 } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:56.668Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7bdfd620-f5d4-49ca-913f-47f14040049f", "process_id": "24499", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n", "stderr": "", "aggregated_output": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 758681937 }, "formatted_output": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n" } } } ] }, { "id": 160, "role": "agent", "timestamp": "2026-09-25T01:21:01.059Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:01.059Z", "text": "" } ] }, { "id": 161, "role": "agent", "timestamp": "2026-09-25T01:21:01.636Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:01.636Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:11.602Z", "callId": "call_DFzBAIXXmbfsFckQxWAXOfB2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\\n for a in aa:\\n s=base+a*t[cand]\\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:21:12.767Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=30530", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:11.765Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc78e7d887d29439c28ddb7be633", "usage": { "input_tokens": 116467, "cached_input_tokens": 115072, "cache_write_input_tokens": 0, "output_tokens": 752, "reasoning_output_tokens": 192, "total_tokens": 117219 }, "turn_token_usage": { "input_tokens": 11592499, "cached_input_tokens": 11456896, "cache_write_input_tokens": 0, "output_tokens": 70353, "reasoning_output_tokens": 25902, "total_tokens": 11662852 }, "thread_token_usage": { "input_tokens": 11592499, "cached_input_tokens": 11456896, "cache_write_input_tokens": 0, "output_tokens": 70353, "reasoning_output_tokens": 25902, "total_tokens": 11662852 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:21:14.706Z", "callId": "call_IJfjFnexQQONaNQa9bYzDdiF", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":30530,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:21:34.522Z", "content": "Script completed\nWall time 19.8 seconds\nOutput:\n\n(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:14.853Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc88f90c87d28bd71ceacef9f314", "usage": { "input_tokens": 117247, "cached_input_tokens": 116352, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 117292 }, "turn_token_usage": { "input_tokens": 11709746, "cached_input_tokens": 11573248, "cache_write_input_tokens": 0, "output_tokens": 70398, "reasoning_output_tokens": 25902, "total_tokens": 11780144 }, "thread_token_usage": { "input_tokens": 11709746, "cached_input_tokens": 11573248, "cache_write_input_tokens": 0, "output_tokens": 70398, "reasoning_output_tokens": 25902, "total_tokens": 11780144 } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:34.516Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ea2460a-1185-4d71-97b2-7744f003ce71", "process_id": "30530", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t[cand]\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t[cand]\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n", "stderr": "", "aggregated_output": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n", "exit_code": 0, "duration": { "secs": 22, "nanos": 750497445 }, "formatted_output": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n" } } } ] }, { "id": 162, "role": "agent", "timestamp": "2026-09-25T01:21:39.882Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:39.882Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:49.196Z", "callId": "call_LhVVAxf4TojhbATyH5FiepGN", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:21:50.361Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=44884", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:49.365Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc9ebd3887d2807e01ce7743bb61", "usage": { "input_tokens": 117882, "cached_input_tokens": 117120, "cache_write_input_tokens": 0, "output_tokens": 749, "reasoning_output_tokens": 223, "total_tokens": 118631 }, "turn_token_usage": { "input_tokens": 11827628, "cached_input_tokens": 11690368, "cache_write_input_tokens": 0, "output_tokens": 71147, "reasoning_output_tokens": 26125, "total_tokens": 11898775 }, "thread_token_usage": { "input_tokens": 11827628, "cached_input_tokens": 11690368, "cache_write_input_tokens": 0, "output_tokens": 71147, "reasoning_output_tokens": 26125, "total_tokens": 11898775 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:21:52.308Z", "callId": "call_qDpgWQFbAwsqaDCP9cAByCRp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":44884,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:21:58.628Z", "content": "Script completed\nWall time 6.3 seconds\nOutput:\n\n{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:54.822Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccae892c87d2bddbde465c875e32", "usage": { "input_tokens": 118659, "cached_input_tokens": 117760, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 118704 }, "turn_token_usage": { "input_tokens": 11946287, "cached_input_tokens": 11808128, "cache_write_input_tokens": 0, "output_tokens": 71192, "reasoning_output_tokens": 26125, "total_tokens": 12017479 }, "thread_token_usage": { "input_tokens": 11946287, "cached_input_tokens": 11808128, "cache_write_input_tokens": 0, "output_tokens": 71192, "reasoning_output_tokens": 26125, "total_tokens": 12017479 } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:58.626Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-59ed017d-716f-4ace-84db-5976e3ef34df", "process_id": "44884", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 267051259 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 163, "role": "agent", "timestamp": "2026-09-25T01:22:00.281Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:00.281Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:21.505Z", "callId": "call_CpEyBbmCKyzmw8zCo8oic8ie", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\\n+def recent_trend(stage: str, catalog_ids: np.ndarray) -> np.ndarray:\\n cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n idx = pd.Index(catalog_ids)\\n score = np.zeros(len(catalog_ids), dtype=np.float64)\\n- # Public tuning favored a short three-day half-life and strong explicit\\n- # actions. Counts use only information available strictly before cutoff.\\n- for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 30.0)]:\\n- cols = [\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ # Count each listener/item only once, at their most recent active day. This\\n+ # targets future user-level discovery rather than repeat-play volume.\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n if family == \\\"listens\\\":\\n cols.append(\\\"played_ratio_pct\\\")\\n- x = load_family(stage, family, cols)\\n- mapped = idx.get_indexer(x.item_id)\\n- age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\\n- mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n- mask &= x.is_organic.to_numpy() == 1\\n- if family == \\\"listens\\\":\\n- mask &= x.played_ratio_pct.to_numpy() >= 80\\n- weighted = np.exp2(-age_day[mask] / 3.0)\\n- score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\\n- lifetime = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").positive_listens.to_numpy(dtype=np.float64)\\n- raw = score + prior * np.power(lifetime, 0.75)\\n+ sources = [DATA / f\\\"train_{family}.parquet\\\"]\\n+ if stage == \\\"final\\\":\\n+ sources.append(DATA / f\\\"val_{family}.parquet\\\")\\n+ recent_parts: list[pd.DataFrame] = []\\n+ for source in sources:\\n+ x = pd.read_parquet(source, columns=cols)\\n+ mapped = idx.get_indexer(x.item_id)\\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int16)\\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n+ mask &= x.is_organic.to_numpy() == 1\\n+ if family == \\\"listens\\\":\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ recent_parts.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": x.uid.to_numpy()[mask],\\n+ \\\"ci\\\": mapped[mask],\\n+ \\\"age\\\": age_day[mask],\\n+ }\\n+ )\\n+ )\\n+ recent = pd.concat(recent_parts, ignore_index=True)\\n+ latest = recent.groupby([\\\"uid\\\", \\\"ci\\\"], as_index=False).age.min()\\n+ weighted = np.exp2(-latest.age.to_numpy(dtype=np.float64) / 1.2)\\n+ score += gain * np.bincount(\\n+ latest.ci.to_numpy(), weights=weighted, minlength=len(catalog_ids)\\n+ )\\n+ raw = score\\n*** Update File: /app/solution/build_submission.py\\n@@\\n-TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\\n+TREND_BY_ACTIVITY = np.asarray([0.08, 0.09, 0.06, 0.065, 0.06], dtype=np.float32)\\n*** Update File: /app/solution/README.md\\n@@\\n-- The two 48-dimensional spaces are concatenated with one offline item-trend\\n- dimension. Trend uses organic 80%-complete listens and organic likes with a\\n- three-day half-life. Its user-side coefficient depends only on 90-day\\n+- The two 48-dimensional spaces are concatenated with one offline item-trend\\n+ dimension. Trend counts each recent organic listener/item once (80%-complete\\n+ listens plus organic likes) with a 1.2-day half-life. Its user-side\\n+ coefficient depends only on 90-day\\n@@\\n-Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\\n+Public validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:22:21.664Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:21.515Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b1250806-081a-4f64-ad3a-02b6d3e1c308", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -593,3 +593,3 @@\n \n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\n+def recent_trend(stage: str, catalog_ids: np.ndarray) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -597,19 +597,36 @@\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n- # Public tuning favored a short three-day half-life and strong explicit\n- # actions. Counts use only information available strictly before cutoff.\n- for family, gain in [(\"listens\", 1.0), (\"likes\", 30.0)]:\n- cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n+ # Count each listener/item only once, at their most recent active day. This\n+ # targets future user-level discovery rather than repeat-play volume.\n+ for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n+ cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n if family == \"listens\":\n cols.append(\"played_ratio_pct\")\n- x = load_family(stage, family, cols)\n- mapped = idx.get_indexer(x.item_id)\n- age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\n- mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n- mask &= x.is_organic.to_numpy() == 1\n- if family == \"listens\":\n- mask &= x.played_ratio_pct.to_numpy() >= 80\n- weighted = np.exp2(-age_day[mask] / 3.0)\n- score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\n- lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\n- raw = score + prior * np.power(lifetime, 0.75)\n+ sources = [DATA / f\"train_{family}.parquet\"]\n+ if stage == \"final\":\n+ sources.append(DATA / f\"val_{family}.parquet\")\n+ recent_parts: list[pd.DataFrame] = []\n+ for source in sources:\n+ x = pd.read_parquet(source, columns=cols)\n+ mapped = idx.get_indexer(x.item_id)\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int16)\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n+ mask &= x.is_organic.to_numpy() == 1\n+ if family == \"listens\":\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\n+ recent_parts.append(\n+ pd.DataFrame(\n+ {\n+ \"uid\": x.uid.to_numpy()[mask],\n+ \"ci\": mapped[mask],\n+ \"age\": age_day[mask],\n+ }\n+ )\n+ )\n+ recent = pd.concat(recent_parts, ignore_index=True)\n+ latest = recent.groupby([\"uid\", \"ci\"], as_index=False).age.min()\n+ weighted = np.exp2(-latest.age.to_numpy(dtype=np.float64) / 1.2)\n+ score += gain * np.bincount(\n+ latest.ci.to_numpy(), weights=weighted, minlength=len(catalog_ids)\n+ )\n+ raw = score\n # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\n", "move_path": null }, "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -20,3 +20,3 @@\n # Coefficients selected on all-target activity quintiles in the public backtest.\n-TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\n+TREND_BY_ACTIVITY = np.asarray([0.08, 0.09, 0.06, 0.065, 0.06], dtype=np.float32)\n \n", "move_path": null }, "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -21,4 +21,5 @@\n - The two 48-dimensional spaces are concatenated with one offline item-trend\n- dimension. Trend uses organic 80%-complete listens and organic likes with a\n- three-day half-life. Its user-side coefficient depends only on 90-day\n+ dimension. Trend counts each recent organic listener/item once (80%-complete\n+ listens plus organic likes) with a 1.2-day half-life. Its user-side\n+ coefficient depends only on 90-day\n history activity. Thus the final 97-dimensional relevance score is exactly\n@@ -42,3 +43,3 @@\n \n-Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\n+Public validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\n with `/task/evaluate_public.py`. The corresponding ranking is\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\nM /app/solution/build_submission.py\nM /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:21.663Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccb6ce6087d2bc28c76c48bb6f3e", "usage": { "input_tokens": 118773, "cached_input_tokens": 118528, "cache_write_input_tokens": 0, "output_tokens": 1207, "reasoning_output_tokens": 20, "total_tokens": 119980 }, "turn_token_usage": { "input_tokens": 12065060, "cached_input_tokens": 11926656, "cache_write_input_tokens": 0, "output_tokens": 72399, "reasoning_output_tokens": 26145, "total_tokens": 12137459 }, "thread_token_usage": { "input_tokens": 12065060, "cached_input_tokens": 11926656, "cache_write_input_tokens": 0, "output_tokens": 72399, "reasoning_output_tokens": 26145, "total_tokens": 12137459 } } } ] }, { "id": 164, "role": "agent", "timestamp": "2026-09-25T01:22:26.111Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:26.111Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:22:27.544Z", "text": "The final trend variant wins decisively: it counts each listener-track once at the most recent day, uses a 1.2-day half-life, and reaches 0.042143 publicly. I’m updating only the final offline trend coordinate and reranking from the already-refit neural vectors—no retraining or eligibility logic changes." }, { "kind": "tool", "timestamp": "2026-09-25T01:22:40.019Z", "callId": "call_gaT3F8CTZ6hECn6qZ5myTzMA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_best.parquet /app/solution/validation.parquet\\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport json,numpy as np,pandas as pd\\nimport train_ranker as tr\\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\\nwith np.load('deployment_vectors.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\\niv[:,-1]=tr.recent_trend('final',items)\\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\\n# Replace both only after all computations and writes succeed.\\nfrom pathlib import Path\\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\\\n')\\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:22:41.186Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=89775", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:40.135Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccce2ee887d2805555ad570e9e39", "usage": { "input_tokens": 120004, "cached_input_tokens": 118656, "cache_write_input_tokens": 0, "output_tokens": 931, "reasoning_output_tokens": 158, "total_tokens": 120935 }, "turn_token_usage": { "input_tokens": 12185064, "cached_input_tokens": 12045312, "cache_write_input_tokens": 0, "output_tokens": 73330, "reasoning_output_tokens": 26303, "total_tokens": 12258394 }, "thread_token_usage": { "input_tokens": 12185064, "cached_input_tokens": 12045312, "cache_write_input_tokens": 0, "output_tokens": 73330, "reasoning_output_tokens": 26303, "total_tokens": 12258394 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:22:43.272Z", "callId": "call_ehFhpSPTYxY41nRq9tuhotFY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":89775,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:22:48.016Z", "content": "Script completed\nWall time 4.7 seconds\nOutput:\n\n{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:43.420Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cce1631087d29c8836ea25606fcd", "usage": { "input_tokens": 120963, "cached_input_tokens": 119808, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 121008 }, "turn_token_usage": { "input_tokens": 12306027, "cached_input_tokens": 12165120, "cache_write_input_tokens": 0, "output_tokens": 73375, "reasoning_output_tokens": 26303, "total_tokens": 12379402 }, "thread_token_usage": { "input_tokens": 12306027, "cached_input_tokens": 12165120, "cache_write_input_tokens": 0, "output_tokens": 73375, "reasoning_output_tokens": 26303, "total_tokens": 12379402 } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:48.014Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7931c987-161a-4d6d-adb3-58c990d1c1c9", "process_id": "89775", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_best.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport json,numpy as np,pandas as pd\nimport train_ranker as tr\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\nwith np.load('deployment_vectors.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\niv[:,-1]=tr.recent_trend('final',items)\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\n# Replace both only after all computations and writes succeed.\nfrom pathlib import Path\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\n')\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_best.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport json,numpy as np,pandas as pd\nimport train_ranker as tr\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\nwith np.load('deployment_vectors.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\niv[:,-1]=tr.recent_trend('final',items)\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\n# Replace both only after all computations and writes succeed.\nfrom pathlib import Path\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\n')\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n", "stderr": "", "aggregated_output": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 829461436 }, "formatted_output": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n" } } } ] }, { "id": 165, "role": "agent", "timestamp": "2026-09-25T01:22:50.465Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:50.465Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:00.647Z", "callId": "call_Rove4iz11LCmPn1lWoWkiJ3L", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\\nassert np.array_equal(submitted,reproduced)\\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\\nsed -n '1,240p' README.md\\ncat final_report.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:23:01.812Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=84114", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:00.843Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cce8410487d2a1598b7199b2a8a0", "usage": { "input_tokens": 121118, "cached_input_tokens": 120832, "cache_write_input_tokens": 0, "output_tokens": 638, "reasoning_output_tokens": 64, "total_tokens": 121756 }, "turn_token_usage": { "input_tokens": 12427145, "cached_input_tokens": 12285952, "cache_write_input_tokens": 0, "output_tokens": 74013, "reasoning_output_tokens": 26367, "total_tokens": 12501158 }, "thread_token_usage": { "input_tokens": 12427145, "cached_input_tokens": 12285952, "cache_write_input_tokens": 0, "output_tokens": 74013, "reasoning_output_tokens": 26367, "total_tokens": 12501158 } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:07.395Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6c8dd21d-cdc9-48b0-b4de-a12e1882f65c", "process_id": "84114", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\nsed -n '1,240p' README.md\ncat final_report.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\nsed -n '1,240p' README.md\ncat final_report.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 584441041 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:23:07.752Z", "callId": "call_mJyxP1xaYfavAAB69tcYmL6R", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":84114,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:23:07.907Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:07.905Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccf600bc87d28ea0fb05fcc2599f", "usage": { "input_tokens": 121784, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 121829 }, "turn_token_usage": { "input_tokens": 12548929, "cached_input_tokens": 12406912, "cache_write_input_tokens": 0, "output_tokens": 74058, "reasoning_output_tokens": 26367, "total_tokens": 12622987 }, "thread_token_usage": { "input_tokens": 12548929, "cached_input_tokens": 12406912, "cache_write_input_tokens": 0, "output_tokens": 74058, "reasoning_output_tokens": 26367, "total_tokens": 12622987 } } } ] }, { "id": 166, "role": "agent", "timestamp": "2026-09-25T01:23:12.013Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:12.013Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:14.263Z", "callId": "call_ngTEA0Qp0PIgFZcwIYVy23W1", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n dimension. Trend counts each recent organic listener/item once (80%-complete\\n listens plus organic likes) with a 1.2-day half-life. Its user-side\\n- coefficient depends only on 90-day\\n- history activity. Thus the final 97-dimensional relevance score is exactly\\n+ coefficient depends only on 90-day history activity. Thus the final\\n+ 97-dimensional relevance score is exactly\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:23:14.457Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:14.271Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-05920607-4d31-4957-bd47-ec0b840eaa93", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -23,4 +23,4 @@\n listens plus organic likes) with a 1.2-day half-life. Its user-side\n- coefficient depends only on 90-day\n- history activity. Thus the final 97-dimensional relevance score is exactly\n+ coefficient depends only on 90-day history activity. Thus the final\n+ 97-dimensional relevance score is exactly\n one user-vector/item-vector dot product. Candidate vectors can be computed\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:14.455Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccfc5aa087d2b4f31f00e50805f4", "usage": { "input_tokens": 122741, "cached_input_tokens": 121600, "cache_write_input_tokens": 0, "output_tokens": 240, "reasoning_output_tokens": 106, "total_tokens": 122981 }, "turn_token_usage": { "input_tokens": 12671670, "cached_input_tokens": 12528512, "cache_write_input_tokens": 0, "output_tokens": 74298, "reasoning_output_tokens": 26473, "total_tokens": 12745968 }, "thread_token_usage": { "input_tokens": 12671670, "cached_input_tokens": 12528512, "cache_write_input_tokens": 0, "output_tokens": 74298, "reasoning_output_tokens": 26473, "total_tokens": 12745968 } } } ] }, { "id": 167, "role": "agent", "timestamp": "2026-09-25T01:23:17.488Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:17.488Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:20.209Z", "callId": "call_gg1v0KwEh5tXaAsxmGTS15Dm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:23:25.475Z", "content": "Script completed\nWall time 5.3 seconds\nOutput:\n\ntrend_reproduction_max_abs_error 0.00018596649169921875 identical False\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:20.393Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd02e4fc87d2a91029c7734048fc", "usage": { "input_tokens": 123005, "cached_input_tokens": 122624, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 7, "total_tokens": 123172 }, "turn_token_usage": { "input_tokens": 12794675, "cached_input_tokens": 12651136, "cache_write_input_tokens": 0, "output_tokens": 74465, "reasoning_output_tokens": 26480, "total_tokens": 12869140 }, "thread_token_usage": { "input_tokens": 12794675, "cached_input_tokens": 12651136, "cache_write_input_tokens": 0, "output_tokens": 74465, "reasoning_output_tokens": 26480, "total_tokens": 12869140 } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:25.471Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd411c79-7af3-4cab-b470-8c6fbeb690fe", "process_id": "46204", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n", "stderr": "", "aggregated_output": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 87640561 }, "formatted_output": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n" } } } ] }, { "id": 168, "role": "agent", "timestamp": "2026-09-25T01:23:30.942Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:30.942Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:40.673Z", "callId": "call_ttrwyTsGlEw6dy5nFv1DeiNm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:23:41.839Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=26124", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:40.857Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd0dac5887d2accc2cc1eaccf710", "usage": { "input_tokens": 123214, "cached_input_tokens": 122880, "cache_write_input_tokens": 0, "output_tokens": 791, "reasoning_output_tokens": 243, "total_tokens": 124005 }, "turn_token_usage": { "input_tokens": 12917889, "cached_input_tokens": 12774016, "cache_write_input_tokens": 0, "output_tokens": 75256, "reasoning_output_tokens": 26723, "total_tokens": 12993145 }, "thread_token_usage": { "input_tokens": 12917889, "cached_input_tokens": 12774016, "cache_write_input_tokens": 0, "output_tokens": 75256, "reasoning_output_tokens": 26723, "total_tokens": 12993145 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:23:44.302Z", "callId": "call_RPUtdIHrSbjcPcrz7dF8TfvI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":26124,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:23:51.334Z", "content": "Script completed\nWall time 7.0 seconds\nOutput:\n\n{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:44.479Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd1e151c87d2b30b545e4873f554", "usage": { "input_tokens": 124033, "cached_input_tokens": 123008, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 124078 }, "turn_token_usage": { "input_tokens": 13041922, "cached_input_tokens": 12897024, "cache_write_input_tokens": 0, "output_tokens": 75301, "reasoning_output_tokens": 26723, "total_tokens": 13117223 }, "thread_token_usage": { "input_tokens": 13041922, "cached_input_tokens": 12897024, "cache_write_input_tokens": 0, "output_tokens": 75301, "reasoning_output_tokens": 26723, "total_tokens": 13117223 } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:51.333Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7d377d45-d214-430b-a57f-5020627f2c70", "process_id": "26124", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 495054172 }, "formatted_output": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } } ] }, { "id": 169, "role": "agent", "timestamp": "2026-09-25T01:23:53.040Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:53.040Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:54.514Z", "callId": "call_zFYWMXkzxzXdUujRRDpBGHlH", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:23:57.849Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:54.710Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd27870c87d299d98a366d5a7b03", "usage": { "input_tokens": 124145, "cached_input_tokens": 123904, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 17, "total_tokens": 124252 }, "turn_token_usage": { "input_tokens": 13166067, "cached_input_tokens": 13020928, "cache_write_input_tokens": 0, "output_tokens": 75408, "reasoning_output_tokens": 26740, "total_tokens": 13241475 }, "thread_token_usage": { "input_tokens": 13166067, "cached_input_tokens": 13020928, "cache_write_input_tokens": 0, "output_tokens": 75408, "reasoning_output_tokens": 26740, "total_tokens": 13241475 } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:57.845Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3650ecf1-3c76-47ae-b1c9-23c36c5c538c", "process_id": "36072", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 162555526 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 170, "role": "agent", "timestamp": "2026-09-25T01:24:08.271Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:08.271Z", "text": "" } ] }, { "id": 171, "role": "agent", "timestamp": "2026-09-25T01:24:17.599Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:17.599Z", "text": "" } ] }, { "id": 172, "role": "agent", "timestamp": "2026-09-25T01:24:19.979Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:19.979Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:24:21.432Z", "text": "The reproducible pipeline now matches the 0.042143 score and the final artifact remains fully valid. I’m evaluating one last discovery-specific refinement: trend based only on newly acquired listeners, excluding repeat plays that could never be eligible outcomes. This is still streaming pair aggregation—not a user-item matrix—and will be adopted only if it validates." }, { "kind": "tool", "timestamp": "2026-09-25T01:24:38.495Z", "callId": "call_ixRETr8ggfejaxMJYTRD6zC3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,time,resource\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\\ndel x,rel\\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\\nprint(*sorted(res,reverse=True)[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:24:39.666Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=14237", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:24:38.679Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd2e10f887d2ba54b1941940c90f", "usage": { "input_tokens": 124320, "cached_input_tokens": 123904, "cache_write_input_tokens": 0, "output_tokens": 2197, "reasoning_output_tokens": 1165, "total_tokens": 126517 }, "turn_token_usage": { "input_tokens": 13290387, "cached_input_tokens": 13144832, "cache_write_input_tokens": 0, "output_tokens": 77605, "reasoning_output_tokens": 27905, "total_tokens": 13367992 }, "thread_token_usage": { "input_tokens": 13290387, "cached_input_tokens": 13144832, "cache_write_input_tokens": 0, "output_tokens": 77605, "reasoning_output_tokens": 27905, "total_tokens": 13367992 } } } ] }, { "id": 173, "role": "agent", "timestamp": "2026-09-25T01:24:41.320Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:41.320Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:24:42.067Z", "callId": "call_zxQC5m9N2pFZIMiFIpvGqVH4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":14237,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:25:06.940Z", "content": "Script completed\nWall time 24.9 seconds\nOutput:\n\nload listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:24:42.239Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd57ea7087d2bc50db6be4e35784", "usage": { "input_tokens": 126545, "cached_input_tokens": 124160, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 126598 }, "turn_token_usage": { "input_tokens": 13416932, "cached_input_tokens": 13268992, "cache_write_input_tokens": 0, "output_tokens": 77658, "reasoning_output_tokens": 27911, "total_tokens": 13494590 }, "thread_token_usage": { "input_tokens": 13416932, "cached_input_tokens": 13268992, "cache_write_input_tokens": 0, "output_tokens": 77658, "reasoning_output_tokens": 27911, "total_tokens": 13494590 } } }, { "kind": "context", "timestamp": "2026-09-25T01:25:06.938Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e9cd1a3d-046f-4472-b9eb-7c43b437c9ff", "process_id": "14237", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,time,resource\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\ndel x,rel\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,time,resource\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\ndel x,rel\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n", "stderr": "", "aggregated_output": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 274041447 }, "formatted_output": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n" } } } ] }, { "id": 174, "role": "agent", "timestamp": "2026-09-25T01:25:13.198Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:25:13.198Z", "text": "" } ] } ] }