{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-max-0-153-4-retriever-r05-20260924T221413Z", "taskChecksum": "8c56c97a3cd40259e1a07cb3b2228657d5bdfcfb9f89ae31b1ea4d2a4f77d228", "source": { "format": "codex-rollout", "sha256": "f31796db9c5379fb6afebfd07485e0382d30e0221112cd1a4dcf4f55354bfe5c", "records": 875, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 130, "item_completed:AgentMessage": 22, "token_count": 124 } }, "sessionId": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T01:18:38.095Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:38.095Z", "text": "You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.\n\n# Personality\n\nAs Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend.\n\nYou have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique.\n\nConversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them.\n\n## Writing style\n\nAvoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable.\n\nIf you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering.\n\n## Technical communication\n\nLead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice.\n\nYou prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nThe user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task.\n\nWhen you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events.\n\n## Intermediate commentary\n\nAs you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify.\n\nIf the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work.\n\nDo NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users.\n\nNever praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like \"I will do rather than \", \"I will do , not \".\n\n## Final answer\n\nIn your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary.\n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\n### Visualizations\n\nUse a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps.\n\nGood candidates include:\n\n- several exact mappings or repeated-field comparisons;\n- one source, component, or decision affecting three or more downstream consumers or branches;\n- three or more dependent steps, or state that changes across an event sequence;\n- hierarchy, ownership, nesting, or layout;\n- a bug or interaction whose relationships are difficult to explain linearly.\n\nPrefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout.\n\nUsually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations.\n\n# Rules for getting work done\n\n- When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss.\n- When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.\n- Do not chain shell commands with separators like `echo \"====\";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n\n## File editing constraints\n\nUse `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough.\n\nYou may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user.\n\nNever use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands.\n\n## Autonomy and persistence\n\nAdapt accordingly based on the user’s request type. When asked to:\n\n- Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant.\n- Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation.\n- Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains.\n- Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker.\n\nYou avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances:\na) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope.\nb) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications).\n\nA terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives.\n\nYou make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user.\n\nWhen presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront.\n\nIf completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission.\n\n# Destructive actions\n\nBe cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.\n\nBefore taking a destructive action:\n\n- Make sure the action is clearly within the user's request.\n- Resolve the exact targets with read-only checks when necessary.\n- Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command.\n- When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.\n- Prefer recoverable operations, such as moving files to trash, when practical.\n- If the target or scope is unclear, stop and ask the user.\n\nNever run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.\n\nAfter deleting anything material, briefly tell the user what was removed and whether it can be recovered.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.\n\n### How to use skills\n\n- Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n- Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback.\n- How to use a skill:\n 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{\"authority\":{\"kind\":\"orchestrator\"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF.\n 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths.\n 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it.\n 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path.\n 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist).\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skills you're using and why. If you skip an obvious skill, say why.\n- Context hygiene:\n - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked.\n - When variants exist, select only the relevant references and note the choice.\n- Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.\n\nWhen the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill.\n\nExplicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work.\n\nWhen using a skill the user did not explicitly name, follow this procedure:\n\n- First, tell the user in the commentary channel **why** you are using the skill.\n- Then, use the skill as long as it stays within the scope of the task.\n- Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response).\n\nIf a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-25T01:18:39.843Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-25T01:18:39.843Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.\n\nYou can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-history forks (`fork_turns` omitted or `\"all\"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `\"none\"` or a positive integer string." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-25T01:18:39.843Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-25T01:18:39.843Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n- GitHub (github@openai-curated-remote)\n- Gmail (gmail@openai-curated-remote)\n- Google Calendar (google-calendar@openai-curated-remote)\n- Google Drive (google-drive@openai-curated-remote)\n- Linear (linear@openai-curated-remote)\n- Notion (notion@openai-curated-remote)\n- OpenAI Developers (openai-developers@openai-curated-remote)\n- Outlook Calendar (outlook-calendar@openai-curated-remote)\n- Outlook Email (outlook-email@openai-curated-remote)\n- SharePoint (sharepoint@openai-curated-remote)\n- Slack (slack@openai-curated-remote)\n- Teams (teams@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-25T01:18:39.843Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T01:18:39.844Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:39.845Z", "data": { "type": "turn_context", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "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": "2026-09-25T01:18:39.862Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:18:39.862Z", "text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-25T01:18:42.056Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:42.056Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:18:43.406Z", "text": "I’ll first audit the data shapes, time windows, eligibility density, and label coverage. Then I’ll build a scalable sampled two-tower trainer, evaluate a few compact feature/objective variants on the public window, preserve the best valid ranking, and refit that configuration on train+validation for the final submission." }, { "kind": "tool", "timestamp": "2026-09-25T01:18:45.481Z", "callId": "call_WJ0Pot9H1xn9kBhQ2aEIoxLQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:45.553Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:45.518Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cbf0199c87d2a95c58d8901079bd", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 261, "reasoning_output_tokens": 70, "total_tokens": 14837 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 261, "reasoning_output_tokens": 70, "total_tokens": 14837 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 261, "reasoning_output_tokens": 70, "total_tokens": 14837 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:45.547Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4db355a5-ce41-4084-a5e8-f6ceb0a187a7", "process_id": "73975", "command": [ "/bin/sh", "-lc", "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8100 }, "formatted_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T01:18:50.070Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:50.070Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:18:56.734Z", "callId": "call_tEQldDsoEJ1Cm8aUXm6CYXTQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n pf=pq.ParquetFile(f)\\n md=pf.metadata\\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\\nprint('\\\\nTARGET')\\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape,t.head(),t.dtypes, 'uniq',t.uid.nunique())\\nprint('\\\\nCATALOG')\\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape,c.head(10),c.tail(),c.dtypes,c.item_id.nunique())\\nfor stage in ['validation','test']:\\n z=np.load(p/f'{stage}_eligible_candidates.npz')\\n print('\\\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n n=bits.sum(1)\\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:56.862Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:56.774Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cbf5d8b887d2b386083712000a80", "usage": { "input_tokens": 18843, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 570, "reasoning_output_tokens": 190, "total_tokens": 19413 }, "turn_token_usage": { "input_tokens": 33419, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 831, "reasoning_output_tokens": 260, "total_tokens": 34250 }, "thread_token_usage": { "input_tokens": 33419, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 831, "reasoning_output_tokens": 260, "total_tokens": 34250 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:56.858Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd6e89f7-1746-41f2-bf2d-25b71bbe7755", "process_id": "53884", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n pf=pq.ParquetFile(f)\n md=pf.metadata\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\nprint('\\nTARGET')\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape,t.head(),t.dtypes, 'uniq',t.uid.nunique())\nprint('\\nCATALOG')\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape,c.head(10),c.tail(),c.dtypes,c.item_id.nunique())\nfor stage in ['validation','test']:\n z=np.load(p/f'{stage}_eligible_candidates.npz')\n print('\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n n=bits.sum(1)\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n pf=pq.ParquetFile(f)\n md=pf.metadata\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\nprint('\\nTARGET')\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape,t.head(),t.dtypes, 'uniq',t.uid.nunique())\nprint('\\nCATALOG')\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape,c.head(10),c.tail(),c.dtypes,c.item_id.nunique())\nfor stage in ['validation','test']:\n z=np.load(p/f'{stage}_eligible_candidates.npz')\n print('\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n n=bits.sum(1)\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3549 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T01:18:59.073Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:59.073Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:18:59.767Z", "text": "The default `python` on PATH is not the task’s pinned runtime (it lacks the listed parquet engine). I’m locating the provided environment before touching any artifacts; this is an environment-path issue, not a data issue." }, { "kind": "tool", "timestamp": "2026-09-25T01:19:02.026Z", "callId": "call_T7QUmO9rHtxW00C9VcfYAd4o", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"which -a python python3 || true\\npython --version\\npython -m pip list 2>/dev/null | sed -n '1,80p'\\nfind /opt /usr/local /task -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -40\\nls -la /task | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:02.767Z", "content": "Script completed\nWall time 0.7 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\nPython 3.12.13\nPackage Version\n------- -------\npip 25.0.1\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\n-r--r--r-- 1 root root 0 Sep 25 01:00 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:01 data\n-r--r--r-- 1 root root 5774 Sep 25 01:00 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:00 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:00 requirements.txt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:02.068Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc010fb887d2ad9ee246b3ebfea7", "usage": { "input_tokens": 19469, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 218, "reasoning_output_tokens": 31, "total_tokens": 19687 }, "turn_token_usage": { "input_tokens": 52888, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 1049, "reasoning_output_tokens": 291, "total_tokens": 53937 }, "thread_token_usage": { "input_tokens": 52888, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 1049, "reasoning_output_tokens": 291, "total_tokens": 53937 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:02.763Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ff3429e-e624-444c-a248-5188b9ef2891", "process_id": "80954", "command": [ "/bin/sh", "-lc", "which -a python python3 || true\npython --version\npython -m pip list 2>/dev/null | sed -n '1,80p'\nfind /opt /usr/local /task -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -40\nls -la /task | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3 || true\npython --version\npython -m pip list 2>/dev/null | sed -n '1,80p'\nfind /opt /usr/local /task -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -40\nls -la /task | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\n-r--r--r-- 1 root root 0 Sep 25 01:00 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:01 data\n-r--r--r-- 1 root root 5774 Sep 25 01:00 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:00 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:00 requirements.txt\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\n-r--r--r-- 1 root root 0 Sep 25 01:00 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:01 data\n-r--r--r-- 1 root root 5774 Sep 25 01:00 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:00 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:00 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 572394770 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\n-r--r--r-- 1 root root 0 Sep 25 01:00 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:01 data\n-r--r--r-- 1 root root 5774 Sep 25 01:00 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:00 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:00 requirements.txt\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T01:19:04.381Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:04.381Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:06.721Z", "callId": "call_ApEd4nQwziVT4KKBT3L0h6a2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"env | sort | sed -n '1,160p'\\nfind / -maxdepth 3 -type d \\\\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\\\) 2>/dev/null | head -80\\nfind / -maxdepth 4 -type f -name 'libtorch_cpu.so' 2>/dev/null | head\\nls -la /usr/local/bin | sed -n '1,100p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":18000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:06.825Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\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=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCODEX_THREAD_ID=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=ed0cb8c187fe\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\ntotal 52\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 13 Sep 25 01:18 node -> /usr/bin/node\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:06.757Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc06f12487d28ec602f1c7c04535", "usage": { "input_tokens": 19915, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 178, "reasoning_output_tokens": 27, "total_tokens": 20093 }, "turn_token_usage": { "input_tokens": 72803, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 1227, "reasoning_output_tokens": 318, "total_tokens": 74030 }, "thread_token_usage": { "input_tokens": 72803, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 1227, "reasoning_output_tokens": 318, "total_tokens": 74030 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:06.820Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9d960df9-5f18-401d-9965-a34e84c7ef84", "process_id": "20105", "command": [ "/bin/sh", "-lc", "env | sort | sed -n '1,160p'\nfind / -maxdepth 3 -type d \\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\) 2>/dev/null | head -80\nfind / -maxdepth 4 -type f -name 'libtorch_cpu.so' 2>/dev/null | head\nls -la /usr/local/bin | sed -n '1,100p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "env | sort | sed -n '1,160p'\nfind / -maxdepth 3 -type d \\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\) 2>/dev/null | head -80\nfind / -maxdepth 4 -type f -name 'libtorch_cpu.so' 2>/dev/null | head\nls -la /usr/local/bin | sed -n '1,100p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "CODEX_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=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCODEX_THREAD_ID=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=ed0cb8c187fe\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\ntotal 52\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 13 Sep 25 01:18 node -> /usr/bin/node\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n", "stderr": "", "aggregated_output": "CODEX_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=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCODEX_THREAD_ID=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=ed0cb8c187fe\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\ntotal 52\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 13 Sep 25 01:18 node -> /usr/bin/node\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2100 }, "formatted_output": "CODEX_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=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCODEX_THREAD_ID=01a0d624-99f6-7c71-b1bc-47b2f5d2ed53\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=ed0cb8c187fe\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\ntotal 52\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 13 Sep 25 01:18 node -> /usr/bin/node\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T01:19:08.498Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:08.498Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:10.389Z", "callId": "call_AGsIJ2K99aEoFqopCySn1dkb", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type d \\\\( -name 'torch' -o -name 'pandas' -o -name 'pyarrow' \\\\) 2>/dev/null | head -80\\nfind / -type f -name 'python*' -perm -111 2>/dev/null | head -80\\ncommand -v uv || true\\nls -la /app\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:11.215Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python-config.py\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3-minimal.postinst\n/var/lib/dpkg/info/python3-six.postinst\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3-pkg-resources.prerm\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3-six.prerm\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\n/var/lib/dpkg/info/python3-pkg-resources.postinst\n/var/lib/dpkg/info/python3-minimal.prerm\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_edition_defaults.py\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_message.py\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:14 solution\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:10.432Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc0b1c7887d29fe8ebf3856a936a", "usage": { "input_tokens": 21135, "cached_input_tokens": 19712, "cache_write_input_tokens": 0, "output_tokens": 156, "reasoning_output_tokens": 32, "total_tokens": 21291 }, "turn_token_usage": { "input_tokens": 93938, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 1383, "reasoning_output_tokens": 350, "total_tokens": 95321 }, "thread_token_usage": { "input_tokens": 93938, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 1383, "reasoning_output_tokens": 350, "total_tokens": 95321 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:11.211Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a29589c4-0f63-417e-8ae6-1171af484327", "process_id": "34560", "command": [ "/bin/sh", "-lc", "find / -type d \\( -name 'torch' -o -name 'pandas' -o -name 'pyarrow' \\) 2>/dev/null | head -80\nfind / -type f -name 'python*' -perm -111 2>/dev/null | head -80\ncommand -v uv || true\nls -la /app" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d \\( -name 'torch' -o -name 'pandas' -o -name 'pyarrow' \\) 2>/dev/null | head -80\nfind / -type f -name 'python*' -perm -111 2>/dev/null | head -80\ncommand -v uv || true\nls -la /app" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python-config.py\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3-minimal.postinst\n/var/lib/dpkg/info/python3-six.postinst\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3-pkg-resources.prerm\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3-six.prerm\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\n/var/lib/dpkg/info/python3-pkg-resources.postinst\n/var/lib/dpkg/info/python3-minimal.prerm\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_edition_defaults.py\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_message.py\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:14 solution\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python-config.py\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3-minimal.postinst\n/var/lib/dpkg/info/python3-six.postinst\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3-pkg-resources.prerm\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3-six.prerm\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\n/var/lib/dpkg/info/python3-pkg-resources.postinst\n/var/lib/dpkg/info/python3-minimal.prerm\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_edition_defaults.py\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_message.py\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:14 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 657356754 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python-config.py\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3-minimal.postinst\n/var/lib/dpkg/info/python3-six.postinst\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3-pkg-resources.prerm\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3-six.prerm\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\n/var/lib/dpkg/info/python3-pkg-resources.postinst\n/var/lib/dpkg/info/python3-minimal.prerm\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_edition_defaults.py\n/opt/task-venv/lib/python3.12/site-packages/google/protobuf/internal/python_message.py\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:14 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:18 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:14 solution\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T01:19:12.460Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:12.460Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:15.032Z", "callId": "call_JXPQZcqQqYgCoWRrdqJt6bMm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find /opt/task-venv -maxdepth 2 -ls | head -80\\n/opt/task-venv/bin/python --version 2>&1 || true\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas,pyarrow,torch,numpy\\nprint(pandas.__version__,pyarrow.__version__,torch.__version__,numpy.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:19.002Z", "content": "Script completed\nWall time 4.0 seconds\nOutput:\n\n 1338705 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv\n 1336424 0 lrwxrwxrwx 1 root root 3 Sep 25 01:14 /opt/task-venv/lib64 -> lib\n 1363849 4 -r--r--r-- 1 root root 171 Sep 25 01:14 /opt/task-venv/pyvenv.cfg\n 1338743 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/lib\n 1338744 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/lib/python3.12\n 1338706 0 -r--r--r-- 1 root root 0 Sep 25 01:14 /opt/task-venv/.lock\n 1338741 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/include\n 1338742 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/include/python3.12\n 1363850 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/share\n 1363867 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/share/man\n 1363851 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/share/jupyter\n 1338735 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/etc\n 1338736 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/etc/jupyter\n 1338707 4 dr-xr-xr-x 1 root root 4096 Sep 25 01:14 /opt/task-venv/bin\n 1338722 4 -r-xr-xr-x 1 root root 311 Sep 25 01:14 /opt/task-venv/bin/jupytext\n 1311305 0 lrwxrwxrwx 1 root root 6 Sep 25 01:14 /opt/task-venv/bin/python3.12 -> python\n 1338715 4 -r-xr-xr-x 1 root root 299 Sep 25 01:14 /opt/task-venv/bin/idna\n 1338710 4 -r--r--r-- 1 root root 920 Sep 25 01:14 /opt/task-venv/bin/activate.csh\n 1338712 4 -r-xr-xr-x 1 root root 302 Sep 25 01:14 /opt/task-venv/bin/chardetect\n 1311303 0 lrwxrwxrwx 1 root root 21 Sep 25 01:14 /opt/task-venv/bin/python -> /usr/local/bin/python\n 1338713 4 -r-xr-xr-x 1 root root 304 Sep 25 01:14 /opt/task-venv/bin/dotenv\n 1338719 4 -r-xr-xr-x 1 root root 311 Sep 25 01:14 /opt/task-venv/bin/jupyter-migrate\n 1338733 4 -r-xr-xr-x 1 root root 312 Sep 25 01:14 /opt/task-venv/bin/torchrun\n 1338709 4 -r--r--r-- 1 root root 2158 Sep 25 01:14 /opt/task-venv/bin/activate\n 1338734 4 -r-xr-xr-x 1 root root 299 Sep 25 01:14 /opt/task-venv/bin/tqdm\n 1338723 4 -r-xr-xr-x 1 root root 315 Sep 25 01:14 /opt/task-venv/bin/jupytext-config\n 1338725 4 -r-xr-xr-x 1 root root 312 Sep 25 01:14 /opt/task-venv/bin/markdown-it\n 1338720 4 -r-xr-xr-x 1 root root 316 Sep 25 01:14 /opt/task-venv/bin/jupyter-troubleshoot\n 1338727 4 -r-xr-xr-x 1 root root 308 Sep 25 01:14 /opt/task-venv/bin/numpy-config\n 1338721 4 -r-xr-xr-x 1 root root 344 Sep 25 01:14 /opt/task-venv/bin/jupyter-trust\n 1338731 4 -r-xr-xr-x 1 root root 307 Sep 25 01:14 /opt/task-venv/bin/slugify\n 1338711 4 -r--r--r-- 1 root root 2195 Sep 25 01:14 /opt/task-venv/bin/activate.fish\n 1338726 4 -r-xr-xr-x 1 root root 325 Sep 25 01:14 /opt/task-venv/bin/normalizer\n 1338724 4 -r-xr-xr-x 1 root root 301 Sep 25 01:14 /opt/task-venv/bin/kaggle\n 1338718 4 -r-xr-xr-x 1 root root 311 Sep 25 01:14 /opt/task-venv/bin/jupyter\n 1338708 12 -r--r--r-- 1 root root 9033 Sep 25 01:14 /opt/task-venv/bin/Activate.ps1\n 1338728 4 -r-xr-xr-x 1 root root 313 Sep 25 01:14 /opt/task-venv/bin/pip\n 1338716 4 -r-xr-xr-x 1 root root 297 Sep 25 01:14 /opt/task-venv/bin/isympy\n 1338729 4 -r-xr-xr-x 1 root root 313 Sep 25 01:14 /opt/task-venv/bin/pip3\n 1338717 4 -r-xr-xr-x 1 root root 305 Sep 25 01:14 /opt/task-venv/bin/jsonschema\n 1338732 4 -r-xr-xr-x 1 root root 321 Sep 25 01:14 /opt/task-venv/bin/torchfrtrace\n 1311304 0 lrwxrwxrwx 1 root root 6 Sep 25 01:14 /opt/task-venv/bin/python3 -> python\n 1338730 4 -r-xr-xr-x 1 root root 313 Sep 25 01:14 /opt/task-venv/bin/pip3.12\n 1338714 4 -r-xr-xr-x 1 root root 308 Sep 25 01:14 /opt/task-venv/bin/f2py\nPython 3.12.13\n2.3.3 18.1.0 2.8.0+cpu 2.2.6\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:15.096Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc0f760087d2a6b0e78586da16c6", "usage": { "input_tokens": 21837, "cached_input_tokens": 20992, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 14, "total_tokens": 21980 }, "turn_token_usage": { "input_tokens": 115775, "cached_input_tokens": 104192, "cache_write_input_tokens": 0, "output_tokens": 1526, "reasoning_output_tokens": 364, "total_tokens": 117301 }, "thread_token_usage": { "input_tokens": 115775, "cached_input_tokens": 104192, "cache_write_input_tokens": 0, "output_tokens": 1526, "reasoning_output_tokens": 364, "total_tokens": 117301 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:18.997Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-80abdc85-143b-4bed-bc71-cbbb69602417", "process_id": "81116", "command": [ "/bin/sh", "-lc", "find /opt/task-venv -maxdepth 2 -ls | head -80\n/opt/task-venv/bin/python --version 2>&1 || true\n/opt/task-venv/bin/python - <<'PY'\nimport pandas,pyarrow,torch,numpy\nprint(pandas.__version__,pyarrow.__version__,torch.__version__,numpy.__version__)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find /opt/task-venv -maxdepth 2 -ls | head -80\n/opt/task-venv/bin/python --version 2>&1 || true\n/opt/task-venv/bin/python - <<'PY'\nimport pandas,pyarrow,torch,numpy\nprint(pandas.__version__,pyarrow.__version__,torch.__version__,numpy.__version__)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 18.1.0 2.8.0+cpu 2.2.6\n", "stderr": "", "aggregated_output": "2.3.3 18.1.0 2.8.0+cpu 2.2.6\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 800811942 }, "formatted_output": "2.3.3 18.1.0 2.8.0+cpu 2.2.6\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T01:19:20.997Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:20.997Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:27.907Z", "callId": "call_Fq7ZAlzjeJFxP2HdmVLagcoV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n pf=pq.ParquetFile(f); md=pf.metadata\\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\\nprint('\\\\nTARGET')\\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape); print(t.head()); print(t.dtypes, 'uniq',t.uid.nunique())\\nprint('\\\\nCATALOG')\\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape); print(c.head(10)); print(c.tail()); print(c.dtypes,c.item_id.nunique())\\nfor stage in ['validation','test']:\\n z=np.load(p/f'{stage}_eligible_candidates.npz')\\n print('\\\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n n=bits.sum(1)\\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:30.372Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\ncandidate_catalog.parquet rows 30000 rgs 1 schema ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rgs 1 schema ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rgs 1 schema ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rgs 42 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rgs 2 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rgs 1 schema ['uid', 'item_id', 'state']\n\nTARGET\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object uniq 8456\n\nCATALOG\n(30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object 30000\n\n validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24657 29067.306291390727 30000 percentiles [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ]\n\n test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24552 29041.3178807947 30000 percentiles [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:27.941Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc172bac87d2b71b41a3b0f202eb", "usage": { "input_tokens": 23876, "cached_input_tokens": 21632, "cache_write_input_tokens": 0, "output_tokens": 406, "reasoning_output_tokens": 14, "total_tokens": 24282 }, "turn_token_usage": { "input_tokens": 139651, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1932, "reasoning_output_tokens": 378, "total_tokens": 141583 }, "thread_token_usage": { "input_tokens": 139651, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1932, "reasoning_output_tokens": 378, "total_tokens": 141583 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:30.367Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-157891c3-a4f5-42c6-a301-40ea7411390c", "process_id": "28781", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n pf=pq.ParquetFile(f); md=pf.metadata\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\nprint('\\nTARGET')\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape); print(t.head()); print(t.dtypes, 'uniq',t.uid.nunique())\nprint('\\nCATALOG')\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape); print(c.head(10)); print(c.tail()); print(c.dtypes,c.item_id.nunique())\nfor stage in ['validation','test']:\n z=np.load(p/f'{stage}_eligible_candidates.npz')\n print('\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n n=bits.sum(1)\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n pf=pq.ParquetFile(f); md=pf.metadata\n print(f.name, 'rows',md.num_rows,'rgs',md.num_row_groups,'schema',pf.schema_arrow.names)\nprint('\\nTARGET')\nt=pd.read_parquet(p/'target_users.parquet'); print(t.shape); print(t.head()); print(t.dtypes, 'uniq',t.uid.nunique())\nprint('\\nCATALOG')\nc=pd.read_parquet(p/'candidate_catalog.parquet'); print(c.shape); print(c.head(10)); print(c.tail()); print(c.dtypes,c.item_id.nunique())\nfor stage in ['validation','test']:\n z=np.load(p/f'{stage}_eligible_candidates.npz')\n print('\\n',stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n n=bits.sum(1)\n print('uid first',z['uids'][:5], 'item eq cat',np.array_equal(z['item_ids'],c.item_id.to_numpy()),'eligible min/mean/max',n.min(),n.mean(),n.max(),'percentiles',np.percentile(n,[0,1,25,50,75,99,100]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet rows 30000 rgs 1 schema ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rgs 1 schema ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rgs 1 schema ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rgs 42 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rgs 2 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rgs 1 schema ['uid', 'item_id', 'state']\n\nTARGET\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object uniq 8456\n\nCATALOG\n(30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object 30000\n\n validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24657 29067.306291390727 30000 percentiles [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ]\n\n test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24552 29041.3178807947 30000 percentiles [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ]\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet rows 30000 rgs 1 schema ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rgs 1 schema ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rgs 1 schema ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rgs 42 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rgs 2 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rgs 1 schema ['uid', 'item_id', 'state']\n\nTARGET\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object uniq 8456\n\nCATALOG\n(30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object 30000\n\n validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24657 29067.306291390727 30000 percentiles [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ]\n\n test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24552 29041.3178807947 30000 percentiles [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ]\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 294827141 }, "formatted_output": "candidate_catalog.parquet rows 30000 rgs 1 schema ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rgs 1 schema ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rgs 1 schema ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rgs 42 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rgs 2 schema ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rgs 1 schema ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rgs 1 schema ['uid', 'item_id', 'state']\n\nTARGET\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object uniq 8456\n\nCATALOG\n(30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object 30000\n\n validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24657 29067.306291390727 30000 percentiles [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ]\n\n test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid first [100 200 300 600 700] item eq cat True eligible min/mean/max 24552 29041.3178807947 30000 percentiles [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ]\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T01:19:35.710Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:35.710Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:39.782Z", "callId": "call_lTumitrdcLQNuaTlO1R1C3wg", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"free -h && df -h /app /tmp\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\np=Path('/task/data')\\nfor family in ['listens','likes','dislikes','unlikes','undislikes']:\\n for split in ['train','val']:\\n f=p/f'{split}_{family}.parquet'\\n cols=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if family=='listens' else [])\\n x=pd.read_parquet(f,columns=cols)\\n print(split,family,'rows',len(x),'users',x.uid.nunique(),'items',x.item_id.nunique(),'ts',x.timestamp.min(),x.timestamp.max(),'organic',x.is_organic.mean(),end='')\\n if family=='listens': print(' pos50',((x.played_ratio_pct>=50)).mean(),'strong80',((x.played_ratio_pct>=80)&(x.is_organic==1)).mean())\\n else: print()\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:42.548Z", "content": "Script completed\nWall time 2.8 seconds\nOutput:\n\n/bin/sh: 1: free: not found\ntrain listens rows 43484159 users 9238 items 845395 ts 0 24786795 organic 0.5169151828370419 pos50 0.6324315712303417 strong80 0.2800464187429726\nval listens rows 1501697 users 8196 items 191747 ts 24788600 25393395 organic 0.517072352145606 pos50 0.6425790289252759 strong80 0.28377562184648436\ntrain likes rows 814831 users 8144 items 172303 ts 80 24786790 organic 0.5667911505576003\nval likes rows 37795 users 4114 items 22902 ts 24788815 25393390 organic 0.6662521497552586\ntrain dislikes rows 101075 users 5732 items 50876 ts 75 24786580 organic 0.5172198862231017\nval dislikes rows 3563 users 955 items 3135 ts 24789920 25393280 organic 0.5150154364299747\ntrain unlikes rows 292665 users 6197 items 113072 ts 250 24786570 organic 0.9466967351750295\nval unlikes rows 9346 users 1313 items 8080 ts 24789005 25393385 organic 0.9528140380911619\ntrain undislikes rows 19960 users 2769 items 14725 ts 15950 24785395 organic 0.8893286573146293\nval undislikes rows 606 users 205 items 579 ts 24810715 25393035 organic 0.8696369636963697\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:39.826Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc2290c087d2875470dbe80f394d", "usage": { "input_tokens": 25439, "cached_input_tokens": 23680, "cache_write_input_tokens": 0, "output_tokens": 483, "reasoning_output_tokens": 196, "total_tokens": 25922 }, "turn_token_usage": { "input_tokens": 165090, "cached_input_tokens": 149504, "cache_write_input_tokens": 0, "output_tokens": 2415, "reasoning_output_tokens": 574, "total_tokens": 167505 }, "thread_token_usage": { "input_tokens": 165090, "cached_input_tokens": 149504, "cache_write_input_tokens": 0, "output_tokens": 2415, "reasoning_output_tokens": 574, "total_tokens": 167505 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:42.533Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-92d588d0-bfbc-4893-881b-c68993ed8b79", "process_id": "47020", "command": [ "/bin/sh", "-lc", "free -h && df -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor family in ['listens','likes','dislikes','unlikes','undislikes']:\n for split in ['train','val']:\n f=p/f'{split}_{family}.parquet'\n cols=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(f,columns=cols)\n print(split,family,'rows',len(x),'users',x.uid.nunique(),'items',x.item_id.nunique(),'ts',x.timestamp.min(),x.timestamp.max(),'organic',x.is_organic.mean(),end='')\n if family=='listens': print(' pos50',((x.played_ratio_pct>=50)).mean(),'strong80',((x.played_ratio_pct>=80)&(x.is_organic==1)).mean())\n else: print()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h && df -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor family in ['listens','likes','dislikes','unlikes','undislikes']:\n for split in ['train','val']:\n f=p/f'{split}_{family}.parquet'\n cols=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(f,columns=cols)\n print(split,family,'rows',len(x),'users',x.uid.nunique(),'items',x.item_id.nunique(),'ts',x.timestamp.min(),x.timestamp.max(),'organic',x.is_organic.mean(),end='')\n if family=='listens': print(' pos50',((x.played_ratio_pct>=50)).mean(),'strong80',((x.played_ratio_pct>=80)&(x.is_organic==1)).mean())\n else: print()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train listens rows 43484159 users 9238 items 845395 ts 0 24786795 organic 0.5169151828370419 pos50 0.6324315712303417 strong80 0.2800464187429726\nval listens rows 1501697 users 8196 items 191747 ts 24788600 25393395 organic 0.517072352145606 pos50 0.6425790289252759 strong80 0.28377562184648436\ntrain likes rows 814831 users 8144 items 172303 ts 80 24786790 organic 0.5667911505576003\nval likes rows 37795 users 4114 items 22902 ts 24788815 25393390 organic 0.6662521497552586\ntrain dislikes rows 101075 users 5732 items 50876 ts 75 24786580 organic 0.5172198862231017\nval dislikes rows 3563 users 955 items 3135 ts 24789920 25393280 organic 0.5150154364299747\ntrain unlikes rows 292665 users 6197 items 113072 ts 250 24786570 organic 0.9466967351750295\nval unlikes rows 9346 users 1313 items 8080 ts 24789005 25393385 organic 0.9528140380911619\ntrain undislikes rows 19960 users 2769 items 14725 ts 15950 24785395 organic 0.8893286573146293\nval undislikes rows 606 users 205 items 579 ts 24810715 25393035 organic 0.8696369636963697\n", "stderr": "", "aggregated_output": "train listens rows 43484159 users 9238 items 845395 ts 0 24786795 organic 0.5169151828370419 pos50 0.6324315712303417 strong80 0.2800464187429726\nval listens rows 1501697 users 8196 items 191747 ts 24788600 25393395 organic 0.517072352145606 pos50 0.6425790289252759 strong80 0.28377562184648436\ntrain likes rows 814831 users 8144 items 172303 ts 80 24786790 organic 0.5667911505576003\nval likes rows 37795 users 4114 items 22902 ts 24788815 25393390 organic 0.6662521497552586\ntrain dislikes rows 101075 users 5732 items 50876 ts 75 24786580 organic 0.5172198862231017\nval dislikes rows 3563 users 955 items 3135 ts 24789920 25393280 organic 0.5150154364299747\ntrain unlikes rows 292665 users 6197 items 113072 ts 250 24786570 organic 0.9466967351750295\nval unlikes rows 9346 users 1313 items 8080 ts 24789005 25393385 organic 0.9528140380911619\ntrain undislikes rows 19960 users 2769 items 14725 ts 15950 24785395 organic 0.8893286573146293\nval undislikes rows 606 users 205 items 579 ts 24810715 25393035 organic 0.8696369636963697\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 586426459 }, "formatted_output": "train listens rows 43484159 users 9238 items 845395 ts 0 24786795 organic 0.5169151828370419 pos50 0.6324315712303417 strong80 0.2800464187429726\nval listens rows 1501697 users 8196 items 191747 ts 24788600 25393395 organic 0.517072352145606 pos50 0.6425790289252759 strong80 0.28377562184648436\ntrain likes rows 814831 users 8144 items 172303 ts 80 24786790 organic 0.5667911505576003\nval likes rows 37795 users 4114 items 22902 ts 24788815 25393390 organic 0.6662521497552586\ntrain dislikes rows 101075 users 5732 items 50876 ts 75 24786580 organic 0.5172198862231017\nval dislikes rows 3563 users 955 items 3135 ts 24789920 25393280 organic 0.5150154364299747\ntrain unlikes rows 292665 users 6197 items 113072 ts 250 24786570 organic 0.9466967351750295\nval unlikes rows 9346 users 1313 items 8080 ts 24789005 25393385 organic 0.9528140380911619\ntrain undislikes rows 19960 users 2769 items 14725 ts 15950 24785395 organic 0.8893286573146293\nval undislikes rows 606 users 205 items 579 ts 24810715 25393035 organic 0.8696369636963697\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T01:19:52.147Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:52.147Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:19:53.763Z", "text": "The dataset is unusually favorable for a history encoder: 8,456 target users, dense histories, and a 30k catalog, while only sampled event pairs are needed for training. I’m now measuring how much validation relevance is explained by repeat affinity, recency, explicit likes, and global popularity; those diagnostics will determine the tower inputs and sampling mix." }, { "kind": "tool", "timestamp": "2026-09-25T01:20:13.821Z", "callId": "call_HBCN5hdf9L8mWvEkfct9K7aq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,25p' /proc/meminfo\\ndf -h /app /tmp\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\\ntset=set(map(int,target))\\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\\ncatset=set(map(int,cat.item_id))\\n# truth before eligibility then check via vector indices\\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,i):\\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\\n# aggregate relevant pairs history stats\\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\\n# likes pairs\\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\\nvals=[hidx.get(k,(0,)*7) for k in truth]\\na=np.array(vals)\\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\\n# future truth popularity ranks\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nranks=np.array([rank[i] for u,i in truth])\\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:20:43.990Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nMemTotal: 15988972 kB\nMemFree: 1919524 kB\nMemAvailable: 14834272 kB\nBuffers: 265424 kB\nCached: 12332356 kB\nSwapCached: 0 kB\nActive: 601916 kB\nInactive: 12298912 kB\nActive(anon): 329452 kB\nInactive(anon): 48 kB\nActive(file): 272464 kB\nInactive(file): 12298864 kB\nUnevictable: 43840 kB\nMlocked: 27208 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\nZswap: 0 kB\nZswapped: 0 kB\nDirty: 1544 kB\nWriteback: 0 kB\nAnonPages: 346940 kB\nMapped: 473128 kB\nShmem: 3036 kB\nKReclaimable: 802536 kB\nSlab: 969444 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\noverlay 96G 15G 82G 16% /\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:13.884Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc2ebde487d2bd4b01703dedf391", "usage": { "input_tokens": 26332, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 1671, "reasoning_output_tokens": 437, "total_tokens": 28003 }, "turn_token_usage": { "input_tokens": 191422, "cached_input_tokens": 174720, "cache_write_input_tokens": 0, "output_tokens": 4086, "reasoning_output_tokens": 1011, "total_tokens": 195508 }, "thread_token_usage": { "input_tokens": 191422, "cached_input_tokens": 174720, "cache_write_input_tokens": 0, "output_tokens": 4086, "reasoning_output_tokens": 1011, "total_tokens": 195508 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T01:20:46.964Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:46.964Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:48.076Z", "callId": "call_ImyTXkh6UIVBOQDEGXNkLtu8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ps -eo pid,etime,rss,cmd | grep '[o]pt/task-venv/bin/python'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:20:48.137Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"5db8ba\",\"wall_time_seconds\":0.000003881,\"exit_code\":1,\"original_token_count\":7,\"output\":\"/bin/sh: 1: ps: not found\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:48.114Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-00b78ce3-be3e-4b12-a7c0-b0773a864826", "process_id": "87202", "command": [ "/bin/sh", "-lc", "ps -eo pid,etime,rss,cmd | grep '[o]pt/task-venv/bin/python'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -eo pid,etime,rss,cmd | grep '[o]pt/task-venv/bin/python'" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3881 }, "formatted_output": "/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:48.135Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc6c2ef087d2b7dee2b5834e2f16", "usage": { "input_tokens": 28322, "cached_input_tokens": 26112, "cache_write_input_tokens": 0, "output_tokens": 191, "reasoning_output_tokens": 120, "total_tokens": 28513 }, "turn_token_usage": { "input_tokens": 219744, "cached_input_tokens": 200832, "cache_write_input_tokens": 0, "output_tokens": 4277, "reasoning_output_tokens": 1131, "total_tokens": 224021 }, "thread_token_usage": { "input_tokens": 219744, "cached_input_tokens": 200832, "cache_write_input_tokens": 0, "output_tokens": 4277, "reasoning_output_tokens": 1131, "total_tokens": 224021 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-25T01:20:50.207Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:50.207Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:09.773Z", "callId": "call_7LCdDqoN6tYQKTc9mFiyWtl9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\np=Path('/task/data'); t0=time.time()\\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,i):\\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:21:39.945Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"0ba819\",\"wall_time_seconds\":30.001248575,\"session_id\":83586,\"original_token_count\":8,\"output\":\"loaded meta 0.06093788146972656\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:09.832Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cc70647887d280e3ab89dcd7c9a7", "usage": { "input_tokens": 28581, "cached_input_tokens": 28160, "cache_write_input_tokens": 0, "output_tokens": 1160, "reasoning_output_tokens": 65, "total_tokens": 29741 }, "turn_token_usage": { "input_tokens": 248325, "cached_input_tokens": 228992, "cache_write_input_tokens": 0, "output_tokens": 5437, "reasoning_output_tokens": 1196, "total_tokens": 253762 }, "thread_token_usage": { "input_tokens": 248325, "cached_input_tokens": 228992, "cache_write_input_tokens": 0, "output_tokens": 5437, "reasoning_output_tokens": 1196, "total_tokens": 253762 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-25T01:21:40.914Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:40.914Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:41.607Z", "callId": "call_38H0nzq4R3IUtUHUPO3YhfPu", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83586,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:22:11.616Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"21ca12\",\"wall_time_seconds\":30.001213372,\"session_id\":83586,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:41.662Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cca4220c87d2bf194ddefe1c4d03", "usage": { "input_tokens": 29809, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 15, "total_tokens": 29872 }, "turn_token_usage": { "input_tokens": 278134, "cached_input_tokens": 257408, "cache_write_input_tokens": 0, "output_tokens": 5500, "reasoning_output_tokens": 1211, "total_tokens": 283634 }, "thread_token_usage": { "input_tokens": 278134, "cached_input_tokens": 257408, "cache_write_input_tokens": 0, "output_tokens": 5500, "reasoning_output_tokens": 1211, "total_tokens": 283634 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-25T01:22:13.860Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:13.860Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:14.566Z", "callId": "call_YWzUEUEwMqTs0usBAW0Tdj0z", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83586,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:22:44.573Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"5d57ab\",\"wall_time_seconds\":30.000284157,\"session_id\":83586,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:14.616Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ccc3cadc87d2938425b3286f2da4", "usage": { "input_tokens": 29929, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 85, "total_tokens": 30062 }, "turn_token_usage": { "input_tokens": 308063, "cached_input_tokens": 286976, "cache_write_input_tokens": 0, "output_tokens": 5633, "reasoning_output_tokens": 1296, "total_tokens": 313696 }, "thread_token_usage": { "input_tokens": 308063, "cached_input_tokens": 286976, "cache_write_input_tokens": 0, "output_tokens": 5633, "reasoning_output_tokens": 1296, "total_tokens": 313696 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-25T01:22:47.871Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:47.871Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:49.567Z", "callId": "call_F2CezzN1NDRXase6NmW1777m", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /proc/[0-9]*/cmdline; do x=$(tr '\\\\000' ' ' < \\\"$f\\\" 2>/dev/null); case \\\"$x\\\" in *task-venv*python*) echo \\\"$f $x\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:22:49.643Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/9497/cmdline /bin/sh -c sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY \n/proc/9500/cmdline /opt/task-venv/bin/python - \n/proc/9533/cmdline /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY \n/proc/9534/cmdline /opt/task-venv/bin/python - \n/proc/9568/cmdline /bin/sh -c for f in /proc/[0-9]*/cmdline; do x=$(tr '\u0000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:49.615Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-720b77b9-8c1d-4bcb-9301-2f5662721def", "process_id": "43230", "command": [ "/bin/sh", "-lc", "for f in /proc/[0-9]*/cmdline; do x=$(tr '\\000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /proc/[0-9]*/cmdline; do x=$(tr '\\000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/9497/cmdline /bin/sh -c sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY \n/proc/9500/cmdline /opt/task-venv/bin/python - \n/proc/9533/cmdline /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY \n/proc/9534/cmdline /opt/task-venv/bin/python - \n/proc/9568/cmdline /bin/sh -c for f in /proc/[0-9]*/cmdline; do x=$(tr '\u0000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done \n", "stderr": "", "aggregated_output": "/proc/9497/cmdline /bin/sh -c sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY \n/proc/9500/cmdline /opt/task-venv/bin/python - \n/proc/9533/cmdline /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY \n/proc/9534/cmdline /opt/task-venv/bin/python - \n/proc/9568/cmdline /bin/sh -c for f in /proc/[0-9]*/cmdline; do x=$(tr '\u0000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6210 }, "formatted_output": "/proc/9497/cmdline /bin/sh -c sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY \n/proc/9500/cmdline /opt/task-venv/bin/python - \n/proc/9533/cmdline /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY \n/proc/9534/cmdline /opt/task-venv/bin/python - \n/proc/9568/cmdline /bin/sh -c for f in /proc/[0-9]*/cmdline; do x=$(tr '\u0000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *task-venv*python*) echo \"$f $x\";; esac; done \n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:49.641Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cce4c0a887d280ccdc177b83baeb", "usage": { "input_tokens": 30120, "cached_input_tokens": 29696, "cache_write_input_tokens": 0, "output_tokens": 248, "reasoning_output_tokens": 144, "total_tokens": 30368 }, "turn_token_usage": { "input_tokens": 338183, "cached_input_tokens": 316672, "cache_write_input_tokens": 0, "output_tokens": 5881, "reasoning_output_tokens": 1440, "total_tokens": 344064 }, "thread_token_usage": { "input_tokens": 338183, "cached_input_tokens": 316672, "cache_write_input_tokens": 0, "output_tokens": 5881, "reasoning_output_tokens": 1440, "total_tokens": 344064 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T01:22:51.103Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:51.103Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:52.046Z", "callId": "call_TbIflm0Pb25vZwJoR8fLlDFg", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"kill 9500 9497 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:22:52.104Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:52.082Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b727432-52a4-46ef-bda1-5a57d6622eea", "process_id": "95247", "command": [ "/bin/sh", "-lc", "sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,25p' /proc/meminfo\ndf -h /app /tmp\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy()\ntset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet')\ncatset=set(map(int,cat.item_id))\n# truth before eligibility then check via vector indices\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}\ntruth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict())\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique())\n# aggregate relevant pairs history stats\nh=tr.assign(pos50=tr.played_ratio_pct.ge(50),strong=tr.played_ratio_pct.ge(80),orgstrong=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1))).groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max'),last_org=('timestamp',lambda x: 0)).reset_index()\n# likes pairs\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]\nla=lk.groupby(['uid','item_id'],sort=False).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index()\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(u),int(i)):(int(n),int(n50),int(n80),int(nos),int(nlike),int(last),int(lastlike)) for u,i,n,n50,n80,nos,last,nlike,lastlike in h[['uid','item_id','n','n50','n80','nos','last','nlike','lastlike']].itertuples(index=False,name=None)}\nvals=[hidx.get(k,(0,)*7) for k in truth]\na=np.array(vals)\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked','last','lastlike']):\n if j<5: print(name,'relevant overlap',np.mean(a[:,j]>0),'g3',np.mean(a[np.array(list(truth.values()))==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-a[a[:,0]>0,5],[0,10,25,50,75,90,100]))\n# future truth popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top100/1k/5k/10k',[(ranks<=x).mean() for x in [100,1000,5000,10000]])\nprint('all val target organic strong catalog unique',len(strong),'likes',len(likes))\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 158, "nanos": 94613375 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:52.084Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bb39763a-3ba8-418b-9da3-19dde3ee2dfd", "process_id": "53609", "command": [ "/bin/sh", "-lc", "kill 9500 9497 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 9500 9497 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3669 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:52.103Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cce9f2e487d2ad646fc1a6b33efe", "usage": { "input_tokens": 32600, "cached_input_tokens": 29952, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 29, "total_tokens": 32690 }, "turn_token_usage": { "input_tokens": 370783, "cached_input_tokens": 346624, "cache_write_input_tokens": 0, "output_tokens": 5971, "reasoning_output_tokens": 1469, "total_tokens": 376754 }, "thread_token_usage": { "input_tokens": 370783, "cached_input_tokens": 346624, "cache_write_input_tokens": 0, "output_tokens": 5971, "reasoning_output_tokens": 1469, "total_tokens": 376754 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T01:22:53.541Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:53.541Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:55.459Z", "callId": "call_IlNvFFFgPlJongSVbTHUs8ap", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in 9533 9534; do test -e /proc/$p/stat && { echo PID=$p; awk '{print \\\"state=\\\" $3,\\\"utime=\\\" $14,\\\"stime=\\\" $15,\\\"rsspages=\\\" $24}' /proc/$p/stat; cat /proc/$p/wchan; }; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:22:55.519Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nPID=9533\nstate=S utime=0 stime=0 rsspages=359\ndo_waitPID=9534\nstate=R utime=10513 stime=121 rsspages=67247\n0", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:55.499Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3b65e0b0-4751-4dc1-aa16-37811f66e10b", "process_id": "36001", "command": [ "/bin/sh", "-lc", "for p in 9533 9534; do test -e /proc/$p/stat && { echo PID=$p; awk '{print \"state=\" $3,\"utime=\" $14,\"stime=\" $15,\"rsspages=\" $24}' /proc/$p/stat; cat /proc/$p/wchan; }; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in 9533 9534; do test -e /proc/$p/stat && { echo PID=$p; awk '{print \"state=\" $3,\"utime=\" $14,\"stime=\" $15,\"rsspages=\" $24}' /proc/$p/stat; cat /proc/$p/wchan; }; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PID=9533\nstate=S utime=0 stime=0 rsspages=359\ndo_waitPID=9534\nstate=R utime=10513 stime=121 rsspages=67247\n0", "stderr": "", "aggregated_output": "PID=9533\nstate=S utime=0 stime=0 rsspages=359\ndo_waitPID=9534\nstate=R utime=10513 stime=121 rsspages=67247\n0", "exit_code": 0, "duration": { "secs": 0, "nanos": 3320 }, "formatted_output": "PID=9533\nstate=S utime=0 stime=0 rsspages=359\ndo_waitPID=9534\nstate=R utime=10513 stime=121 rsspages=67247\n0" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:55.518Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ccec566087d2816846abf9facd61", "usage": { "input_tokens": 32713, "cached_input_tokens": 32384, "cache_write_input_tokens": 0, "output_tokens": 144, "reasoning_output_tokens": 29, "total_tokens": 32857 }, "turn_token_usage": { "input_tokens": 403496, "cached_input_tokens": 379008, "cache_write_input_tokens": 0, "output_tokens": 6115, "reasoning_output_tokens": 1498, "total_tokens": 409611 }, "thread_token_usage": { "input_tokens": 403496, "cached_input_tokens": 379008, "cache_write_input_tokens": 0, "output_tokens": 6115, "reasoning_output_tokens": 1498, "total_tokens": 409611 } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T01:22:58.056Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:58.056Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:58.765Z", "callId": "call_9liYvuksg0BjsYlETzkHaIbo", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83586,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:23:28.774Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"9bd1ee\",\"wall_time_seconds\":30.001815714,\"session_id\":83586,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:58.830Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ccefd93c87d2a8d03b13ce9d4b26", "usage": { "input_tokens": 32926, "cached_input_tokens": 32512, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 49, "total_tokens": 33023 }, "turn_token_usage": { "input_tokens": 436422, "cached_input_tokens": 411520, "cache_write_input_tokens": 0, "output_tokens": 6212, "reasoning_output_tokens": 1547, "total_tokens": 442634 }, "thread_token_usage": { "input_tokens": 436422, "cached_input_tokens": 411520, "cache_write_input_tokens": 0, "output_tokens": 6212, "reasoning_output_tokens": 1547, "total_tokens": 442634 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T01:23:30.169Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:30.169Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:31.012Z", "callId": "call_Su4RKkiDYrB0A6wghS9cN9HQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"kill -INT 9534\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:23:31.083Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:31.048Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b355f271-cf56-45ad-b0ce-5d0f1e11e0ea", "process_id": "36420", "command": [ "/bin/sh", "-lc", "kill -INT 9534" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill -INT 9534" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4000 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:31.081Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd10f59c87d2bae7cb1a9f2fd8de", "usage": { "input_tokens": 33081, "cached_input_tokens": 32768, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 35, "total_tokens": 33169 }, "turn_token_usage": { "input_tokens": 469503, "cached_input_tokens": 444288, "cache_write_input_tokens": 0, "output_tokens": 6300, "reasoning_output_tokens": 1582, "total_tokens": 475803 }, "thread_token_usage": { "input_tokens": 469503, "cached_input_tokens": 444288, "cache_write_input_tokens": 0, "output_tokens": 6300, "reasoning_output_tokens": 1582, "total_tokens": 475803 } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:31.101Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-53d2976a-0241-47d1-92bf-cc7c2e9bddbc", "process_id": "83586", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time()\ntarget=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target))\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id)); print('loaded meta',time.time()-t0,flush=True)\nz=np.load(p/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp','is_organic']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0),flush=True)\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "loaded meta 0.06093788146972656\nTraceback (most recent call last):\n File \"\", line 11, in \n File \"\", line 8, in elig\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "loaded meta 0.06093788146972656\nTraceback (most recent call last):\n File \"\", line 11, in \n File \"\", line 8, in elig\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 141, "nanos": 158139392 }, "formatted_output": "loaded meta 0.06093788146972656\nTraceback (most recent call last):\n File \"\", line 11, in \n File \"\", line 8, in elig\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:23:32.903Z", "callId": "call_c1615lwTKgWtsYU8zT1L4Ubr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83586,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:23:32.969Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"f4666d\",\"wall_time_seconds\":0.00000286,\"exit_code\":130,\"original_token_count\":244,\"output\":\"Traceback (most recent call last):\\n File \\\"\\\", line 11, in \\n File \\\"\\\", line 8, in elig\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\\\", line 254, in __getitem__\\n return format.read_array(bytes,\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\\\", line 851, in read_array\\n data = _read_bytes(fp, read_size, \\\"array data\\\")\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\\\", line 986, in _read_bytes\\n r = fp.read(size - len(data))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/usr/local/lib/python3.12/zipfile/__init__.py\\\", line 1008, in read\\n data = self._read1(n)\\n ^^^^^^^^^^^^^^\\n File \\\"/usr/local/lib/python3.12/zipfile/__init__.py\\\", line 1084, in _read1\\n data = self._decompressor.decompress(data, n)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nKeyboardInterrupt\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:32.968Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd1372e087d2a9dea8aca9ee830e", "usage": { "input_tokens": 33192, "cached_input_tokens": 32896, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 33238 }, "turn_token_usage": { "input_tokens": 502695, "cached_input_tokens": 477184, "cache_write_input_tokens": 0, "output_tokens": 6346, "reasoning_output_tokens": 1582, "total_tokens": 509041 }, "thread_token_usage": { "input_tokens": 502695, "cached_input_tokens": 477184, "cache_write_input_tokens": 0, "output_tokens": 6346, "reasoning_output_tokens": 1582, "total_tokens": 509041 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-25T01:23:34.476Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:23:34.476Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:53.925Z", "callId": "call_wzu2hgW72E5P68yB13EVblgv", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\np=Path('/task/data'); t0=time.time(); target=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target)); cat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id))\\nwith np.load(p/'validation_eligible_candidates.npz') as z: zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\\nurow={int(x):i for i,x in enumerate(zu)}; icol={int(x):i for i,x in enumerate(zi)}\\ndef elig(u,i):\\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0))\\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:24:24.112Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"6530ff\",\"wall_time_seconds\":30.001630439,\"session_id\":42774,\"original_token_count\":40,\"output\":\"truth pairs 37769 users 4263 grades {1: 33352, 3: 4417} 0.5333213806152344\\ntarget catalog train listens 31062540 pairs 7868664 items 30000 9.827126026153564\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:53.963Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd153b5c87d289f5c1f07a54d9e1", "usage": { "input_tokens": 33597, "cached_input_tokens": 33024, "cache_write_input_tokens": 0, "output_tokens": 1122, "reasoning_output_tokens": 32, "total_tokens": 34719 }, "turn_token_usage": { "input_tokens": 536292, "cached_input_tokens": 510208, "cache_write_input_tokens": 0, "output_tokens": 7468, "reasoning_output_tokens": 1614, "total_tokens": 543760 }, "thread_token_usage": { "input_tokens": 536292, "cached_input_tokens": 510208, "cache_write_input_tokens": 0, "output_tokens": 7468, "reasoning_output_tokens": 1614, "total_tokens": 543760 } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-25T01:24:25.351Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:25.351Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:24:26.071Z", "callId": "call_WhWjOCXSmeQQxH7TnGL1l4Xx", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":42774,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:24:33.383Z", "content": "Script completed\nWall time 7.3 seconds\nOutput:\n\n{\"chunk_id\":\"e578be\",\"wall_time_seconds\":7.267176055,\"exit_code\":1,\"original_token_count\":295,\"output\":\"Traceback (most recent call last):\\n File \\\"\\\", line 21, in \\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\\\", line 4273, in percentile\\nany listen relevant overlap 0.0 g3 0.0\\npos50 relevant overlap 0.0 g3 0.0\\n80any relevant overlap 0.0 g3 0.0\\n80org relevant overlap 0.0 g3 0.0\\nliked relevant overlap 0.0 g3 0.0\\n return _quantile_unchecked(\\n ^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\\\", line 4550, in _quantile_unchecked\\n return _ureduce(a,\\n ^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\\\", line 3894, in _ureduce\\n r = func(a, **kwargs)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\\\", line 4727, in _quantile_ureduce_func\\n result = _quantile(arr,\\n ^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\\\", line 4853, in _quantile\\n previous = arr[previous_indexes]\\n ~~~^^^^^^^^^^^^^^^^^^\\nIndexError: index -1 is out of bounds for axis 0 with size 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:24:26.104Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd4856a887d2b2e39e8a9161a04b", "usage": { "input_tokens": 34836, "cached_input_tokens": 33408, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 34897 }, "turn_token_usage": { "input_tokens": 571128, "cached_input_tokens": 543616, "cache_write_input_tokens": 0, "output_tokens": 7529, "reasoning_output_tokens": 1627, "total_tokens": 578657 }, "thread_token_usage": { "input_tokens": 571128, "cached_input_tokens": 543616, "cache_write_input_tokens": 0, "output_tokens": 7529, "reasoning_output_tokens": 1627, "total_tokens": 578657 } } }, { "kind": "context", "timestamp": "2026-09-25T01:24:33.371Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a9118a31-17e0-42af-b130-c903c97374a2", "process_id": "42774", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time(); target=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target)); cat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id))\nwith np.load(p/'validation_eligible_candidates.npz') as z: zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nurow={int(x):i for i,x in enumerate(zu)}; icol={int(x):i for i,x in enumerate(zi)}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\np=Path('/task/data'); t0=time.time(); target=pd.read_parquet(p/'target_users.parquet',columns=['uid']).uid.to_numpy(); tset=set(map(int,target)); cat=pd.read_parquet(p/'candidate_catalog.parquet'); catset=set(map(int,cat.item_id))\nwith np.load(p/'validation_eligible_candidates.npz') as z: zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nurow={int(x):i for i,x in enumerate(zu)}; icol={int(x):i for i,x in enumerate(zi)}\ndef elig(u,i):\n c=icol.get(int(i)); r=urow.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); vk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=vl[vl.uid.isin(tset)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=vk[vk.uid.isin(tset)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth={(int(u),int(i)):1 for u,i in strong.itertuples(index=False,name=None) if elig(u,i)}; truth.update({(int(u),int(i)):3 for u,i in likes.itertuples(index=False,name=None) if elig(u,i)})\nprint('truth pairs',len(truth),'users',len(set(u for u,i in truth)),'grades',pd.Series(list(truth.values())).value_counts().to_dict(),time.time()-t0,flush=True)\ntr=pd.read_parquet(p/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(tset)&tr.item_id.isin(catset)]\nprint('target catalog train listens',len(tr),'pairs',len(tr[['uid','item_id']].drop_duplicates()),'items',tr.item_id.nunique(),time.time()-t0,flush=True)\ntr['pos50']=tr.played_ratio_pct.ge(50).astype('uint8'); tr['strong']=tr.played_ratio_pct.ge(80).astype('uint8'); tr['orgstrong']=(tr.played_ratio_pct.ge(80)&tr.is_organic.eq(1)).astype('uint8')\nh=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),n50=('pos50','sum'),n80=('strong','sum'),nos=('orgstrong','sum'),last=('timestamp','max')).reset_index(); del tr\nlk=pd.read_parquet(p/'train_likes.parquet',columns=['uid','item_id','timestamp']); lk=lk[lk.uid.isin(tset)&lk.item_id.isin(catset)]; la=lk.groupby(['uid','item_id'],sort=False,observed=True).agg(nlike=('timestamp','size'),lastlike=('timestamp','max')).reset_index(); del lk\nh=h.merge(la,on=['uid','item_id'],how='outer').fillna(0)\nhidx={(int(r.uid),int(r.item_id)):(int(r.n),int(r.n50),int(r.n80),int(r.nos),int(r.nlike),int(r.last),int(r.lastlike)) for r in h.itertuples(index=False)}\nvals=np.array([hidx.get(k,(0,)*7) for k in truth]); grade=np.array(list(truth.values()))\nfor j,name in enumerate(['any listen','pos50','80any','80org','liked']): print(name,'relevant overlap',np.mean(vals[:,j]>0),'g3',np.mean(vals[grade==3,j]>0))\nprint('last recency quantiles if repeat',np.percentile(24786800-vals[vals[:,0]>0,5],[0,10,25,50,75,90,100]))\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); ranks=np.array([rank[i] for u,i in truth])\nprint('truth pop rank pct',np.percentile(ranks,[0,10,25,50,75,90,99,100]),'top',[(x,float((ranks<=x).mean())) for x in [10,100,1000,5000,10000]],'elapsed',time.time()-t0)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "truth pairs 37769 users 4263 grades {1: 33352, 3: 4417} 0.5333213806152344\ntarget catalog train listens 31062540 pairs 7868664 items 30000 9.827126026153564\nTraceback (most recent call last):\n File \"\", line 21, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4273, in percentile\nany listen relevant overlap 0.0 g3 0.0\npos50 relevant overlap 0.0 g3 0.0\n80any relevant overlap 0.0 g3 0.0\n80org relevant overlap 0.0 g3 0.0\nliked relevant overlap 0.0 g3 0.0\n return _quantile_unchecked(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4550, in _quantile_unchecked\n return _ureduce(a,\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 3894, in _ureduce\n r = func(a, **kwargs)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4727, in _quantile_ureduce_func\n result = _quantile(arr,\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4853, in _quantile\n previous = arr[previous_indexes]\n ~~~^^^^^^^^^^^^^^^^^^\nIndexError: index -1 is out of bounds for axis 0 with size 0\n", "stderr": "", "aggregated_output": "truth pairs 37769 users 4263 grades {1: 33352, 3: 4417} 0.5333213806152344\ntarget catalog train listens 31062540 pairs 7868664 items 30000 9.827126026153564\nTraceback (most recent call last):\n File \"\", line 21, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4273, in percentile\nany listen relevant overlap 0.0 g3 0.0\npos50 relevant overlap 0.0 g3 0.0\n80any relevant overlap 0.0 g3 0.0\n80org relevant overlap 0.0 g3 0.0\nliked relevant overlap 0.0 g3 0.0\n return _quantile_unchecked(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4550, in _quantile_unchecked\n return _ureduce(a,\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 3894, in _ureduce\n r = func(a, **kwargs)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4727, in _quantile_ureduce_func\n result = _quantile(arr,\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4853, in _quantile\n previous = arr[previous_indexes]\n ~~~^^^^^^^^^^^^^^^^^^\nIndexError: index -1 is out of bounds for axis 0 with size 0\n", "exit_code": 1, "duration": { "secs": 39, "nanos": 281068441 }, "formatted_output": "truth pairs 37769 users 4263 grades {1: 33352, 3: 4417} 0.5333213806152344\ntarget catalog train listens 31062540 pairs 7868664 items 30000 9.827126026153564\nTraceback (most recent call last):\n File \"\", line 21, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4273, in percentile\nany listen relevant overlap 0.0 g3 0.0\npos50 relevant overlap 0.0 g3 0.0\n80any relevant overlap 0.0 g3 0.0\n80org relevant overlap 0.0 g3 0.0\nliked relevant overlap 0.0 g3 0.0\n return _quantile_unchecked(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4550, in _quantile_unchecked\n return _ureduce(a,\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 3894, in _ureduce\n r = func(a, **kwargs)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4727, in _quantile_ureduce_func\n result = _quantile(arr,\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py\", line 4853, in _quantile\n previous = arr[previous_indexes]\n ~~~^^^^^^^^^^^^^^^^^^\nIndexError: index -1 is out of bounds for axis 0 with size 0\n" } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-25T01:24:36.817Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:36.817Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:24:47.056Z", "callId": "call_qaQnoG6LmQRx231XSHvq3GLK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\np='/task/data'\\ntarget=set(pd.read_parquet(p+'/target_users.parquet').uid.astype(int)); cat=pd.read_parquet(p+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nwith np.load(p+'/validation_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\\nur={int(x):i for i,x in enumerate(u)}; ic={int(x):i for i,x in enumerate(items)}\\ndef ok(a,b):\\n r=ur.get(int(a)); c=ic.get(int(b)); return r is not None and c is not None and bool((pack[r,c//8]>>(c%8))&1)\\nvl=pd.read_parquet(p+'/val_listens.parquet'); vk=pd.read_parquet(p+'/val_likes.parquet')\\ns=vl[vl.uid.isin(target)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); l=vk[vk.uid.isin(target)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\\nt={(int(a),int(b)):1 for a,b in s.itertuples(index=False,name=None) if ok(a,b)}; t.update({(int(a),int(b)):3 for a,b in l.itertuples(index=False,name=None) if ok(a,b)})\\nr=np.array([rank[i] for u,i in t]); g=np.array(list(t.values()))\\nfor subset,name in [(np.ones(len(r),bool),'all'),(g==1,'g1'),(g==3,'g3')]:\\n x=r[subset]; print(name,'n',len(x),'pct',np.percentile(x,[0,1,5,10,25,50,75,90,99,100]),'top',[(q,round(float((x<=q).mean()),4)) for q in [10,100,500,1000,5000,10000,20000]])\\n# per-user label counts\\nfrom collections import Counter\\nc=Counter(a for a,b in t); print('labels/user',np.percentile(list(c.values()),[0,25,50,75,90,99,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:24:49.158Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\nall n 37769 pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.91010e+04 3.00000e+04] top [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\ng1 n 33352 pct [1.00000e+00 2.20000e+01 1.64000e+02 4.08000e+02 1.69375e+03 5.54500e+03\n 1.32740e+04 2.16560e+04 2.91020e+04 3.00000e+04] top [(10, 0.0051), (100, 0.0331), (500, 0.1148), (1000, 0.1795), (5000, 0.4751), (10000, 0.6623), (20000, 0.8746)]\ng3 n 4417 pct [2.00000e+00 3.10000e+01 2.86800e+02 6.18000e+02 2.10700e+03 6.37100e+03\n 1.41810e+04 2.18018e+04 2.88126e+04 2.99890e+04] top [(10, 0.0045), (100, 0.0229), (500, 0.0847), (1000, 0.1442), (5000, 0.4369), (10000, 0.6362), (20000, 0.8707)]\nlabels/user [ 1. 2. 4. 10. 21. 63. 442.]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:24:47.159Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd519a6887d28b776cf933b0b5be", "usage": { "input_tokens": 35333, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 721, "reasoning_output_tokens": 144, "total_tokens": 36054 }, "turn_token_usage": { "input_tokens": 606461, "cached_input_tokens": 578304, "cache_write_input_tokens": 0, "output_tokens": 8250, "reasoning_output_tokens": 1771, "total_tokens": 614711 }, "thread_token_usage": { "input_tokens": 606461, "cached_input_tokens": 578304, "cache_write_input_tokens": 0, "output_tokens": 8250, "reasoning_output_tokens": 1771, "total_tokens": 614711 } } }, { "kind": "context", "timestamp": "2026-09-25T01:24:49.153Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e01fd4c5-3cb9-471e-8cc6-f366776a910b", "process_id": "58451", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np='/task/data'\ntarget=set(pd.read_parquet(p+'/target_users.parquet').uid.astype(int)); cat=pd.read_parquet(p+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(p+'/validation_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\nur={int(x):i for i,x in enumerate(u)}; ic={int(x):i for i,x in enumerate(items)}\ndef ok(a,b):\n r=ur.get(int(a)); c=ic.get(int(b)); return r is not None and c is not None and bool((pack[r,c//8]>>(c%8))&1)\nvl=pd.read_parquet(p+'/val_listens.parquet'); vk=pd.read_parquet(p+'/val_likes.parquet')\ns=vl[vl.uid.isin(target)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); l=vk[vk.uid.isin(target)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\nt={(int(a),int(b)):1 for a,b in s.itertuples(index=False,name=None) if ok(a,b)}; t.update({(int(a),int(b)):3 for a,b in l.itertuples(index=False,name=None) if ok(a,b)})\nr=np.array([rank[i] for u,i in t]); g=np.array(list(t.values()))\nfor subset,name in [(np.ones(len(r),bool),'all'),(g==1,'g1'),(g==3,'g3')]:\n x=r[subset]; print(name,'n',len(x),'pct',np.percentile(x,[0,1,5,10,25,50,75,90,99,100]),'top',[(q,round(float((x<=q).mean()),4)) for q in [10,100,500,1000,5000,10000,20000]])\n# per-user label counts\nfrom collections import Counter\nc=Counter(a for a,b in t); print('labels/user',np.percentile(list(c.values()),[0,25,50,75,90,99,100]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np='/task/data'\ntarget=set(pd.read_parquet(p+'/target_users.parquet').uid.astype(int)); cat=pd.read_parquet(p+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(p+'/validation_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\nur={int(x):i for i,x in enumerate(u)}; ic={int(x):i for i,x in enumerate(items)}\ndef ok(a,b):\n r=ur.get(int(a)); c=ic.get(int(b)); return r is not None and c is not None and bool((pack[r,c//8]>>(c%8))&1)\nvl=pd.read_parquet(p+'/val_listens.parquet'); vk=pd.read_parquet(p+'/val_likes.parquet')\ns=vl[vl.uid.isin(target)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); l=vk[vk.uid.isin(target)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\nt={(int(a),int(b)):1 for a,b in s.itertuples(index=False,name=None) if ok(a,b)}; t.update({(int(a),int(b)):3 for a,b in l.itertuples(index=False,name=None) if ok(a,b)})\nr=np.array([rank[i] for u,i in t]); g=np.array(list(t.values()))\nfor subset,name in [(np.ones(len(r),bool),'all'),(g==1,'g1'),(g==3,'g3')]:\n x=r[subset]; print(name,'n',len(x),'pct',np.percentile(x,[0,1,5,10,25,50,75,90,99,100]),'top',[(q,round(float((x<=q).mean()),4)) for q in [10,100,500,1000,5000,10000,20000]])\n# per-user label counts\nfrom collections import Counter\nc=Counter(a for a,b in t); print('labels/user',np.percentile(list(c.values()),[0,25,50,75,90,99,100]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "all n 37769 pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.91010e+04 3.00000e+04] top [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\ng1 n 33352 pct [1.00000e+00 2.20000e+01 1.64000e+02 4.08000e+02 1.69375e+03 5.54500e+03\n 1.32740e+04 2.16560e+04 2.91020e+04 3.00000e+04] top [(10, 0.0051), (100, 0.0331), (500, 0.1148), (1000, 0.1795), (5000, 0.4751), (10000, 0.6623), (20000, 0.8746)]\ng3 n 4417 pct [2.00000e+00 3.10000e+01 2.86800e+02 6.18000e+02 2.10700e+03 6.37100e+03\n 1.41810e+04 2.18018e+04 2.88126e+04 2.99890e+04] top [(10, 0.0045), (100, 0.0229), (500, 0.0847), (1000, 0.1442), (5000, 0.4369), (10000, 0.6362), (20000, 0.8707)]\nlabels/user [ 1. 2. 4. 10. 21. 63. 442.]\n", "stderr": "", "aggregated_output": "all n 37769 pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.91010e+04 3.00000e+04] top [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\ng1 n 33352 pct [1.00000e+00 2.20000e+01 1.64000e+02 4.08000e+02 1.69375e+03 5.54500e+03\n 1.32740e+04 2.16560e+04 2.91020e+04 3.00000e+04] top [(10, 0.0051), (100, 0.0331), (500, 0.1148), (1000, 0.1795), (5000, 0.4751), (10000, 0.6623), (20000, 0.8746)]\ng3 n 4417 pct [2.00000e+00 3.10000e+01 2.86800e+02 6.18000e+02 2.10700e+03 6.37100e+03\n 1.41810e+04 2.18018e+04 2.88126e+04 2.99890e+04] top [(10, 0.0045), (100, 0.0229), (500, 0.0847), (1000, 0.1442), (5000, 0.4369), (10000, 0.6362), (20000, 0.8707)]\nlabels/user [ 1. 2. 4. 10. 21. 63. 442.]\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 926239883 }, "formatted_output": "all n 37769 pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.91010e+04 3.00000e+04] top [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\ng1 n 33352 pct [1.00000e+00 2.20000e+01 1.64000e+02 4.08000e+02 1.69375e+03 5.54500e+03\n 1.32740e+04 2.16560e+04 2.91020e+04 3.00000e+04] top [(10, 0.0051), (100, 0.0331), (500, 0.1148), (1000, 0.1795), (5000, 0.4751), (10000, 0.6623), (20000, 0.8746)]\ng3 n 4417 pct [2.00000e+00 3.10000e+01 2.86800e+02 6.18000e+02 2.10700e+03 6.37100e+03\n 1.41810e+04 2.18018e+04 2.88126e+04 2.99890e+04] top [(10, 0.0045), (100, 0.0229), (500, 0.0847), (1000, 0.1442), (5000, 0.4369), (10000, 0.6362), (20000, 0.8707)]\nlabels/user [ 1. 2. 4. 10. 21. 63. 442.]\n" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-25T01:24:59.262Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:24:59.262Z", "text": "" } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-25T01:25:08.601Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:25:08.601Z", "text": "" } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T01:25:12.262Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:25:12.262Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:25:16.605Z", "callId": "call_4hoGxPiq2fDkOK3uIr7kAJUh", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\np='/task/data'\\nwith np.load(p+'/validation_eligible_candidates.npz') as z:\\n u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\\nout=[]\\nfor r,uid in enumerate(u):\\n mask=np.unpackbits(pack[r],count=len(items),bitorder='little').astype(bool)\\n top=items[mask][:100]\\n out.append(pd.DataFrame({'uid':int(uid),'item_id':top.astype('int64'),'rank':np.arange(1,101,dtype='int64')}))\\npd.concat(out,ignore_index=True).to_parquet('/app/solution/baseline_pop_validation.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/baseline_pop_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:25:22.893Z", "content": "Script completed\nWall time 6.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/baseline_pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:25:16.703Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd615b5c87d2ac23a2a3b1513ec4", "usage": { "input_tokens": 36576, "cached_input_tokens": 35200, "cache_write_input_tokens": 0, "output_tokens": 1483, "reasoning_output_tokens": 1233, "total_tokens": 38059 }, "turn_token_usage": { "input_tokens": 643037, "cached_input_tokens": 613504, "cache_write_input_tokens": 0, "output_tokens": 9733, "reasoning_output_tokens": 3004, "total_tokens": 652770 }, "thread_token_usage": { "input_tokens": 643037, "cached_input_tokens": 613504, "cache_write_input_tokens": 0, "output_tokens": 9733, "reasoning_output_tokens": 3004, "total_tokens": 652770 } } }, { "kind": "context", "timestamp": "2026-09-25T01:25:22.890Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a6a3959e-639a-474a-98fd-887ce7022cb7", "process_id": "93453", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\np='/task/data'\nwith np.load(p+'/validation_eligible_candidates.npz') as z:\n u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\nout=[]\nfor r,uid in enumerate(u):\n mask=np.unpackbits(pack[r],count=len(items),bitorder='little').astype(bool)\n top=items[mask][:100]\n out.append(pd.DataFrame({'uid':int(uid),'item_id':top.astype('int64'),'rank':np.arange(1,101,dtype='int64')}))\npd.concat(out,ignore_index=True).to_parquet('/app/solution/baseline_pop_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/baseline_pop_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\np='/task/data'\nwith np.load(p+'/validation_eligible_candidates.npz') as z:\n u=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\nout=[]\nfor r,uid in enumerate(u):\n mask=np.unpackbits(pack[r],count=len(items),bitorder='little').astype(bool)\n top=items[mask][:100]\n out.append(pd.DataFrame({'uid':int(uid),'item_id':top.astype('int64'),'rank':np.arange(1,101,dtype='int64')}))\npd.concat(out,ignore_index=True).to_parquet('/app/solution/baseline_pop_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/baseline_pop_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/baseline_pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/baseline_pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 119922488 }, "formatted_output": "{\n \"model\": \"/app/solution/baseline_pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-25T01:25:27.409Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:25:27.409Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:25:29.395Z", "text": "A crucial finding is that eligible tracks are effectively novel to each listener: none of the public relevant user–track pairs had appeared in that user’s training history. So exact-repeat heuristics are useless. The model needs genuine collaborative generalization—pooling embeddings of known tracks into a taste vector and matching that vector to unseen catalog embeddings. The raw popularity baseline is NDCG@10 = 0.010710; I’m using it as the floor while I tune the learned model." }, { "kind": "tool", "timestamp": "2026-09-25T01:25:44.610Z", "callId": "call_RhhymMpX40zJRuzKzK0kj1rA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np, subprocess,json\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution'); cutoff=24786800\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\\nx=x[x.item_id.isin(set(cat.item_id.astype(int)))]\\nframes=[]\\nfor days in [7,30,90,1000]:\\n y=x[(x.timestamp>=cutoff-days*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)]\\n frames.append(y.groupby('item_id').size().rename(f'os{days}'))\\nfor days in [30,90,1000]:\\n y=x[(x.timestamp>=cutoff-days*86400)&(x.played_ratio_pct>=50)]\\n frames.append(y.groupby('item_id').size().rename(f'p{days}'))\\ndel x\\nlk=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic']); lk=lk[lk.item_id.isin(set(cat.item_id.astype(int)))]\\nfor days in [30,90,1000]:\\n frames.append(lk[(lk.timestamp>=cutoff-days*86400)&(lk.is_organic==1)].groupby('item_id').size().rename(f'like{days}'))\\nst=cat.set_index('item_id').join(frames).fillna(0).reset_index()\\n# sort options and log combos\\norders={\\n 'catalog':cat.item_id.to_numpy(),\\n 'organic_all':st.sort_values(['os1000','organic_positive_listens'],ascending=False).item_id.to_numpy(),\\n 'organic_90':st.sort_values(['os90','os1000'],ascending=False).item_id.to_numpy(),\\n 'organic_30':st.sort_values(['os30','os1000'],ascending=False).item_id.to_numpy(),\\n 'organic_7':st.sort_values(['os7','os30'],ascending=False).item_id.to_numpy(),\\n 'like_all':st.sort_values(['like1000','os1000'],ascending=False).item_id.to_numpy(),\\n 'blend':st.assign(score=np.log1p(st.os30)+.35*np.log1p(st.like90)+.25*np.log1p(st.os1000)).sort_values('score',ascending=False).item_id.to_numpy(),\\n}\\nwith np.load(D/'validation_eligible_candidates.npz') as z: u=z['uids'].copy(); ids=z['item_ids'].copy(); pk=z['packed_eligible'].copy()\\npos={int(v):i for i,v in enumerate(ids)}\\nfor name,order in orders.items():\\n cols=np.array([pos[int(v)] for v in order]); rows=[]\\n for r,uid in enumerate(u):\\n elig=((pk[r,cols//8]>>(cols%8))&1).astype(bool); top=order[elig][:100]\\n rows.append(pd.DataFrame({'uid':np.full(100,int(uid),dtype=np.int64),'item_id':top.astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\\n path=O/f'global_{name}_validation.parquet'; pd.concat(rows,ignore_index=True).to_parquet(path,index=False)\\n print(name,flush=True)\\n subprocess.run(['/opt/task-venv/bin/python','/task/evaluate_public.py',str(path)])\\nst.to_parquet(O/'item_stats_train.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:26:14.780Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"fec1a5\",\"wall_time_seconds\":30.001150661,\"session_id\":85541,\"original_token_count\":120,\"output\":\"catalog\\n{\\n \\\"model\\\": \\\"/app/solution/global_catalog_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.01071,\\n \\\"quality_users\\\": 4263\\n }\\n}\\norganic_all\\n{\\n \\\"model\\\": \\\"/app/solution/global_organic_all_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.010551,\\n \\\"quality_users\\\": 4263\\n }\\n}\\norganic_90\\n{\\n \\\"model\\\": \\\"/app/solution/global_organic_90_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.015822,\\n \\\"quality_users\\\": 4263\\n }\\n}\\norganic_30\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:25:44.702Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cd831e1c87d2a75bda4193f1a7c9", "usage": { "input_tokens": 38130, "cached_input_tokens": 36352, "cache_write_input_tokens": 0, "output_tokens": 1168, "reasoning_output_tokens": 209, "total_tokens": 39298 }, "turn_token_usage": { "input_tokens": 681167, "cached_input_tokens": 649856, "cache_write_input_tokens": 0, "output_tokens": 10901, "reasoning_output_tokens": 3213, "total_tokens": 692068 }, "thread_token_usage": { "input_tokens": 681167, "cached_input_tokens": 649856, "cache_write_input_tokens": 0, "output_tokens": 10901, "reasoning_output_tokens": 3213, "total_tokens": 692068 } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-25T01:26:16.828Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:26:16.828Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:26:17.527Z", "callId": "call_C1t56IlfuE6GWQqutb6IlRhs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85541,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:26:35.278Z", "content": "Script completed\nWall time 17.7 seconds\nOutput:\n\n{\"chunk_id\":\"741990\",\"wall_time_seconds\":17.732646878,\"exit_code\":0,\"original_token_count\":151,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/global_organic_30_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.013313,\\n \\\"quality_users\\\": 4263\\n }\\n}\\norganic_7\\n{\\n \\\"model\\\": \\\"/app/solution/global_organic_7_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.028131,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nlike_all\\n{\\n \\\"model\\\": \\\"/app/solution/global_like_all_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.012054,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nblend\\n{\\n \\\"model\\\": \\\"/app/solution/global_blend_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.018073,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:26:17.627Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cdb72a8887d28c393a9e72ecddf9", "usage": { "input_tokens": 39532, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 39586 }, "turn_token_usage": { "input_tokens": 720699, "cached_input_tokens": 687744, "cache_write_input_tokens": 0, "output_tokens": 10955, "reasoning_output_tokens": 3219, "total_tokens": 731654 }, "thread_token_usage": { "input_tokens": 720699, "cached_input_tokens": 687744, "cache_write_input_tokens": 0, "output_tokens": 10955, "reasoning_output_tokens": 3219, "total_tokens": 731654 } } }, { "kind": "context", "timestamp": "2026-09-25T01:26:35.266Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c09b2146-60c2-44eb-92da-9d6df4ca82ed", "process_id": "85541", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np, subprocess,json\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); cutoff=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[x.item_id.isin(set(cat.item_id.astype(int)))]\nframes=[]\nfor days in [7,30,90,1000]:\n y=x[(x.timestamp>=cutoff-days*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)]\n frames.append(y.groupby('item_id').size().rename(f'os{days}'))\nfor days in [30,90,1000]:\n y=x[(x.timestamp>=cutoff-days*86400)&(x.played_ratio_pct>=50)]\n frames.append(y.groupby('item_id').size().rename(f'p{days}'))\ndel x\nlk=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic']); lk=lk[lk.item_id.isin(set(cat.item_id.astype(int)))]\nfor days in [30,90,1000]:\n frames.append(lk[(lk.timestamp>=cutoff-days*86400)&(lk.is_organic==1)].groupby('item_id').size().rename(f'like{days}'))\nst=cat.set_index('item_id').join(frames).fillna(0).reset_index()\n# sort options and log combos\norders={\n 'catalog':cat.item_id.to_numpy(),\n 'organic_all':st.sort_values(['os1000','organic_positive_listens'],ascending=False).item_id.to_numpy(),\n 'organic_90':st.sort_values(['os90','os1000'],ascending=False).item_id.to_numpy(),\n 'organic_30':st.sort_values(['os30','os1000'],ascending=False).item_id.to_numpy(),\n 'organic_7':st.sort_values(['os7','os30'],ascending=False).item_id.to_numpy(),\n 'like_all':st.sort_values(['like1000','os1000'],ascending=False).item_id.to_numpy(),\n 'blend':st.assign(score=np.log1p(st.os30)+.35*np.log1p(st.like90)+.25*np.log1p(st.os1000)).sort_values('score',ascending=False).item_id.to_numpy(),\n}\nwith np.load(D/'validation_eligible_candidates.npz') as z: u=z['uids'].copy(); ids=z['item_ids'].copy(); pk=z['packed_eligible'].copy()\npos={int(v):i for i,v in enumerate(ids)}\nfor name,order in orders.items():\n cols=np.array([pos[int(v)] for v in order]); rows=[]\n for r,uid in enumerate(u):\n elig=((pk[r,cols//8]>>(cols%8))&1).astype(bool); top=order[elig][:100]\n rows.append(pd.DataFrame({'uid':np.full(100,int(uid),dtype=np.int64),'item_id':top.astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n path=O/f'global_{name}_validation.parquet'; pd.concat(rows,ignore_index=True).to_parquet(path,index=False)\n print(name,flush=True)\n subprocess.run(['/opt/task-venv/bin/python','/task/evaluate_public.py',str(path)])\nst.to_parquet(O/'item_stats_train.parquet',index=False)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np, subprocess,json\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); cutoff=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[x.item_id.isin(set(cat.item_id.astype(int)))]\nframes=[]\nfor days in [7,30,90,1000]:\n y=x[(x.timestamp>=cutoff-days*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)]\n frames.append(y.groupby('item_id').size().rename(f'os{days}'))\nfor days in [30,90,1000]:\n y=x[(x.timestamp>=cutoff-days*86400)&(x.played_ratio_pct>=50)]\n frames.append(y.groupby('item_id').size().rename(f'p{days}'))\ndel x\nlk=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic']); lk=lk[lk.item_id.isin(set(cat.item_id.astype(int)))]\nfor days in [30,90,1000]:\n frames.append(lk[(lk.timestamp>=cutoff-days*86400)&(lk.is_organic==1)].groupby('item_id').size().rename(f'like{days}'))\nst=cat.set_index('item_id').join(frames).fillna(0).reset_index()\n# sort options and log combos\norders={\n 'catalog':cat.item_id.to_numpy(),\n 'organic_all':st.sort_values(['os1000','organic_positive_listens'],ascending=False).item_id.to_numpy(),\n 'organic_90':st.sort_values(['os90','os1000'],ascending=False).item_id.to_numpy(),\n 'organic_30':st.sort_values(['os30','os1000'],ascending=False).item_id.to_numpy(),\n 'organic_7':st.sort_values(['os7','os30'],ascending=False).item_id.to_numpy(),\n 'like_all':st.sort_values(['like1000','os1000'],ascending=False).item_id.to_numpy(),\n 'blend':st.assign(score=np.log1p(st.os30)+.35*np.log1p(st.like90)+.25*np.log1p(st.os1000)).sort_values('score',ascending=False).item_id.to_numpy(),\n}\nwith np.load(D/'validation_eligible_candidates.npz') as z: u=z['uids'].copy(); ids=z['item_ids'].copy(); pk=z['packed_eligible'].copy()\npos={int(v):i for i,v in enumerate(ids)}\nfor name,order in orders.items():\n cols=np.array([pos[int(v)] for v in order]); rows=[]\n for r,uid in enumerate(u):\n elig=((pk[r,cols//8]>>(cols%8))&1).astype(bool); top=order[elig][:100]\n rows.append(pd.DataFrame({'uid':np.full(100,int(uid),dtype=np.int64),'item_id':top.astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n path=O/f'global_{name}_validation.parquet'; pd.concat(rows,ignore_index=True).to_parquet(path,index=False)\n print(name,flush=True)\n subprocess.run(['/opt/task-venv/bin/python','/task/evaluate_public.py',str(path)])\nst.to_parquet(O/'item_stats_train.parquet',index=False)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "catalog\n{\n \"model\": \"/app/solution/global_catalog_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\norganic_all\n{\n \"model\": \"/app/solution/global_organic_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\norganic_90\n{\n \"model\": \"/app/solution/global_organic_90_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015822,\n \"quality_users\": 4263\n }\n}\norganic_30\n{\n \"model\": \"/app/solution/global_organic_30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013313,\n \"quality_users\": 4263\n }\n}\norganic_7\n{\n \"model\": \"/app/solution/global_organic_7_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028131,\n \"quality_users\": 4263\n }\n}\nlike_all\n{\n \"model\": \"/app/solution/global_like_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012054,\n \"quality_users\": 4263\n }\n}\nblend\n{\n \"model\": \"/app/solution/global_blend_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018073,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "catalog\n{\n \"model\": \"/app/solution/global_catalog_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\norganic_all\n{\n \"model\": \"/app/solution/global_organic_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\norganic_90\n{\n \"model\": \"/app/solution/global_organic_90_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015822,\n \"quality_users\": 4263\n }\n}\norganic_30\n{\n \"model\": \"/app/solution/global_organic_30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013313,\n \"quality_users\": 4263\n }\n}\norganic_7\n{\n \"model\": \"/app/solution/global_organic_7_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028131,\n \"quality_users\": 4263\n }\n}\nlike_all\n{\n \"model\": \"/app/solution/global_like_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012054,\n \"quality_users\": 4263\n }\n}\nblend\n{\n \"model\": \"/app/solution/global_blend_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018073,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 50, "nanos": 487614623 }, "formatted_output": "catalog\n{\n \"model\": \"/app/solution/global_catalog_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\norganic_all\n{\n \"model\": \"/app/solution/global_organic_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\norganic_90\n{\n \"model\": \"/app/solution/global_organic_90_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015822,\n \"quality_users\": 4263\n }\n}\norganic_30\n{\n \"model\": \"/app/solution/global_organic_30_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013313,\n \"quality_users\": 4263\n }\n}\norganic_7\n{\n \"model\": \"/app/solution/global_organic_7_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028131,\n \"quality_users\": 4263\n }\n}\nlike_all\n{\n \"model\": \"/app/solution/global_like_all_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012054,\n \"quality_users\": 4263\n }\n}\nblend\n{\n \"model\": \"/app/solution/global_blend_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018073,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-25T01:26:45.386Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:26:45.386Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-25T01:26:54.706Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:26:54.706Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-25T01:27:04.029Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:04.029Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-25T01:27:13.363Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:13.363Z", "text": "" } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-25T01:27:22.682Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:22.682Z", "text": "" } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-25T01:27:32.013Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:32.013Z", "text": "" } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-25T01:27:36.247Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:36.247Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:27:37.873Z", "callId": "call_WeItisKlOX0cNgB3kfOkLJSo", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"getconf _NPROCESSORS_ONLN || true\\nlscpu 2>/dev/null | sed -n '1,40p' || true\\nls -la /app/solution | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:27:37.975Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n8\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\ntotal 6104\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:26 .\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:14 ..\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 baseline_pop_validation.parquet\n-rw-r--r-- 1 agent agent 675730 Sep 25 01:26 global_blend_validation.parquet\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 global_catalog_validation.parquet\n-rw-r--r-- 1 agent agent 705416 Sep 25 01:26 global_like_all_validation.parquet\n-rw-r--r-- 1 agent agent 642340 Sep 25 01:26 global_organic_30_validation.parquet\n-rw-r--r-- 1 agent agent 562067 Sep 25 01:26 global_organic_7_validation.parquet\n-rw-r--r-- 1 agent agent 674343 Sep 25 01:26 global_organic_90_validation.parquet\n-rw-r--r-- 1 agent agent 703499 Sep 25 01:26 global_organic_all_validation.parquet\n-rw-r--r-- 1 agent agent 802839 Sep 25 01:26 item_stats_train.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:37.927Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-682fa0f0-b5f8-4fc3-bcf9-b9810db3f9b6", "process_id": "42123", "command": [ "/bin/sh", "-lc", "getconf _NPROCESSORS_ONLN || true\nlscpu 2>/dev/null | sed -n '1,40p' || true\nls -la /app/solution | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "getconf _NPROCESSORS_ONLN || true\nlscpu 2>/dev/null | sed -n '1,40p' || true\nls -la /app/solution | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\ntotal 6104\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:26 .\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:14 ..\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 baseline_pop_validation.parquet\n-rw-r--r-- 1 agent agent 675730 Sep 25 01:26 global_blend_validation.parquet\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 global_catalog_validation.parquet\n-rw-r--r-- 1 agent agent 705416 Sep 25 01:26 global_like_all_validation.parquet\n-rw-r--r-- 1 agent agent 642340 Sep 25 01:26 global_organic_30_validation.parquet\n-rw-r--r-- 1 agent agent 562067 Sep 25 01:26 global_organic_7_validation.parquet\n-rw-r--r-- 1 agent agent 674343 Sep 25 01:26 global_organic_90_validation.parquet\n-rw-r--r-- 1 agent agent 703499 Sep 25 01:26 global_organic_all_validation.parquet\n-rw-r--r-- 1 agent agent 802839 Sep 25 01:26 item_stats_train.parquet\n", "stderr": "", "aggregated_output": "8\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\ntotal 6104\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:26 .\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:14 ..\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 baseline_pop_validation.parquet\n-rw-r--r-- 1 agent agent 675730 Sep 25 01:26 global_blend_validation.parquet\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 global_catalog_validation.parquet\n-rw-r--r-- 1 agent agent 705416 Sep 25 01:26 global_like_all_validation.parquet\n-rw-r--r-- 1 agent agent 642340 Sep 25 01:26 global_organic_30_validation.parquet\n-rw-r--r-- 1 agent agent 562067 Sep 25 01:26 global_organic_7_validation.parquet\n-rw-r--r-- 1 agent agent 674343 Sep 25 01:26 global_organic_90_validation.parquet\n-rw-r--r-- 1 agent agent 703499 Sep 25 01:26 global_organic_all_validation.parquet\n-rw-r--r-- 1 agent agent 802839 Sep 25 01:26 item_stats_train.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2800 }, "formatted_output": "8\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\ntotal 6104\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:26 .\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:14 ..\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 baseline_pop_validation.parquet\n-rw-r--r-- 1 agent agent 675730 Sep 25 01:26 global_blend_validation.parquet\n-rw-r--r-- 1 agent agent 728599 Sep 25 01:25 global_catalog_validation.parquet\n-rw-r--r-- 1 agent agent 705416 Sep 25 01:26 global_like_all_validation.parquet\n-rw-r--r-- 1 agent agent 642340 Sep 25 01:26 global_organic_30_validation.parquet\n-rw-r--r-- 1 agent agent 562067 Sep 25 01:26 global_organic_7_validation.parquet\n-rw-r--r-- 1 agent agent 674343 Sep 25 01:26 global_organic_90_validation.parquet\n-rw-r--r-- 1 agent agent 703499 Sep 25 01:26 global_organic_all_validation.parquet\n-rw-r--r-- 1 agent agent 802839 Sep 25 01:26 item_stats_train.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:27:37.974Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cdcb830887d2a234c3e1017da368", "usage": { "input_tokens": 39869, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 3432, "reasoning_output_tokens": 3336, "total_tokens": 43301 }, "turn_token_usage": { "input_tokens": 760568, "cached_input_tokens": 727040, "cache_write_input_tokens": 0, "output_tokens": 14387, "reasoning_output_tokens": 6555, "total_tokens": 774955 }, "thread_token_usage": { "input_tokens": 760568, "cached_input_tokens": 727040, "cache_write_input_tokens": 0, "output_tokens": 14387, "reasoning_output_tokens": 6555, "total_tokens": 774955 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-25T01:27:49.698Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:49.698Z", "text": "" } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-25T01:27:58.589Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:58.589Z", "text": "" } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-25T01:28:07.897Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:07.897Z", "text": "" } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-25T01:28:08.197Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:08.197Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:28:10.375Z", "callId": "call_tr32l9NKBkfGgOQTM1WTmu2R", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfor f in ['train_listens','train_likes','train_dislikes','train_unlikes','train_undislikes']:\\n x=pd.read_parquet('/task/data/'+f+'.parquet').head(15)\\n print('\\\\n',f); print(x.to_string(index=False))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:12.688Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n\n train_listens\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\n 100 40110 732449 0 100 240\n 100 40360 3397170 0 46 130\n 100 40380 7849270 0 100 205\n 100 40640 1449307 0 100 205\n 100 40785 6887232 0 100 145\n 100 40885 2996197 0 100 95\n 100 41130 6474571 0 100 245\n 100 41545 7847600 0 27 250\n 100 41580 7509517 0 15 200\n 100 41580 470320 0 2 165\n 100 42115 6641244 0 1 200\n 100 42115 2581903 0 37 155\n\n train_likes\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\n 100 1260005 7371186 0\n 100 1263935 4943655 0\n 100 2055015 410868 1\n 100 2494620 1730273 1\n 100 3939350 2621343 0\n 100 3940755 6399268 0\n 100 6902675 3982388 0\n 100 6903355 3633039 0\n 100 7035995 4221787 1\n 100 10314390 4099299 1\n 100 18304655 6005659 0\n 100 23471875 8200811 0\n\n train_dislikes\n uid timestamp item_id is_organic\n 100 3087560 9170134 1\n 100 3936560 8661238 1\n 100 20432825 4927727 1\n 400 2980800 399811 1\n 400 3157520 1575108 1\n 400 3417600 9229946 1\n 400 4968885 6593949 1\n 400 4968905 9198404 1\n 400 5519800 999732 1\n 400 5754475 1254798 1\n 400 5754910 3615680 1\n 400 5756180 5826350 1\n 400 5832130 5123138 1\n 400 5832975 4570886 1\n 400 5839290 5105089 1\n\n train_unlikes\n uid timestamp item_id is_organic\n 100 3087555 5038504 1\n 100 3087555 9170134 1\n 100 4012270 7833676 0\n 100 21210445 7610592 0\n 100 21210465 7610592 0\n 300 3002130 5196757 1\n 300 3002215 112196 1\n 300 3002215 1876467 1\n 300 3002365 9221711 1\n 300 3002700 7237185 1\n 300 3002755 5747334 1\n 300 3002755 9104028 1\n 300 3002875 9380688 1\n 300 5499715 5096553 1\n 300 5499855 6937744 1\n\n train_undislikes\n uid timestamp item_id is_organic\n 100 7895900 9170134 1\n 100 7895900 5038504 1\n1400 8317355 6175319 1\n1400 24001340 7064726 1\n1400 24001340 8966868 1\n1400 24001340 7757137 1\n1400 24001340 1297781 1\n1400 24001340 3662064 1\n1400 24001340 4120170 1\n1900 22426980 6783683 0\n1900 22426980 9208732 0\n2200 17176865 5580391 1\n2300 23869180 316291 1\n2300 24473970 5481367 1\n2700 22771190 6190054 1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:10.459Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ce0a421087d2a998ccf0f48d3dfd", "usage": { "input_tokens": 44327, "cached_input_tokens": 39680, "cache_write_input_tokens": 0, "output_tokens": 1692, "reasoning_output_tokens": 1563, "total_tokens": 46019 }, "turn_token_usage": { "input_tokens": 804895, "cached_input_tokens": 766720, "cache_write_input_tokens": 0, "output_tokens": 16079, "reasoning_output_tokens": 8118, "total_tokens": 820974 }, "thread_token_usage": { "input_tokens": 804895, "cached_input_tokens": 766720, "cache_write_input_tokens": 0, "output_tokens": 16079, "reasoning_output_tokens": 8118, "total_tokens": 820974 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:12.674Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da6e9216-b963-4431-8b5a-8b855b109315", "process_id": "41098", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor f in ['train_listens','train_likes','train_dislikes','train_unlikes','train_undislikes']:\n x=pd.read_parquet('/task/data/'+f+'.parquet').head(15)\n print('\\n',f); print(x.to_string(index=False))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor f in ['train_listens','train_likes','train_dislikes','train_unlikes','train_undislikes']:\n x=pd.read_parquet('/task/data/'+f+'.parquet').head(15)\n print('\\n',f); print(x.to_string(index=False))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n train_listens\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\n 100 40110 732449 0 100 240\n 100 40360 3397170 0 46 130\n 100 40380 7849270 0 100 205\n 100 40640 1449307 0 100 205\n 100 40785 6887232 0 100 145\n 100 40885 2996197 0 100 95\n 100 41130 6474571 0 100 245\n 100 41545 7847600 0 27 250\n 100 41580 7509517 0 15 200\n 100 41580 470320 0 2 165\n 100 42115 6641244 0 1 200\n 100 42115 2581903 0 37 155\n\n train_likes\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\n 100 1260005 7371186 0\n 100 1263935 4943655 0\n 100 2055015 410868 1\n 100 2494620 1730273 1\n 100 3939350 2621343 0\n 100 3940755 6399268 0\n 100 6902675 3982388 0\n 100 6903355 3633039 0\n 100 7035995 4221787 1\n 100 10314390 4099299 1\n 100 18304655 6005659 0\n 100 23471875 8200811 0\n\n train_dislikes\n uid timestamp item_id is_organic\n 100 3087560 9170134 1\n 100 3936560 8661238 1\n 100 20432825 4927727 1\n 400 2980800 399811 1\n 400 3157520 1575108 1\n 400 3417600 9229946 1\n 400 4968885 6593949 1\n 400 4968905 9198404 1\n 400 5519800 999732 1\n 400 5754475 1254798 1\n 400 5754910 3615680 1\n 400 5756180 5826350 1\n 400 5832130 5123138 1\n 400 5832975 4570886 1\n 400 5839290 5105089 1\n\n train_unlikes\n uid timestamp item_id is_organic\n 100 3087555 5038504 1\n 100 3087555 9170134 1\n 100 4012270 7833676 0\n 100 21210445 7610592 0\n 100 21210465 7610592 0\n 300 3002130 5196757 1\n 300 3002215 112196 1\n 300 3002215 1876467 1\n 300 3002365 9221711 1\n 300 3002700 7237185 1\n 300 3002755 5747334 1\n 300 3002755 9104028 1\n 300 3002875 9380688 1\n 300 5499715 5096553 1\n 300 5499855 6937744 1\n\n train_undislikes\n uid timestamp item_id is_organic\n 100 7895900 9170134 1\n 100 7895900 5038504 1\n1400 8317355 6175319 1\n1400 24001340 7064726 1\n1400 24001340 8966868 1\n1400 24001340 7757137 1\n1400 24001340 1297781 1\n1400 24001340 3662064 1\n1400 24001340 4120170 1\n1900 22426980 6783683 0\n1900 22426980 9208732 0\n2200 17176865 5580391 1\n2300 23869180 316291 1\n2300 24473970 5481367 1\n2700 22771190 6190054 1\n", "stderr": "", "aggregated_output": "\n train_listens\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\n 100 40110 732449 0 100 240\n 100 40360 3397170 0 46 130\n 100 40380 7849270 0 100 205\n 100 40640 1449307 0 100 205\n 100 40785 6887232 0 100 145\n 100 40885 2996197 0 100 95\n 100 41130 6474571 0 100 245\n 100 41545 7847600 0 27 250\n 100 41580 7509517 0 15 200\n 100 41580 470320 0 2 165\n 100 42115 6641244 0 1 200\n 100 42115 2581903 0 37 155\n\n train_likes\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\n 100 1260005 7371186 0\n 100 1263935 4943655 0\n 100 2055015 410868 1\n 100 2494620 1730273 1\n 100 3939350 2621343 0\n 100 3940755 6399268 0\n 100 6902675 3982388 0\n 100 6903355 3633039 0\n 100 7035995 4221787 1\n 100 10314390 4099299 1\n 100 18304655 6005659 0\n 100 23471875 8200811 0\n\n train_dislikes\n uid timestamp item_id is_organic\n 100 3087560 9170134 1\n 100 3936560 8661238 1\n 100 20432825 4927727 1\n 400 2980800 399811 1\n 400 3157520 1575108 1\n 400 3417600 9229946 1\n 400 4968885 6593949 1\n 400 4968905 9198404 1\n 400 5519800 999732 1\n 400 5754475 1254798 1\n 400 5754910 3615680 1\n 400 5756180 5826350 1\n 400 5832130 5123138 1\n 400 5832975 4570886 1\n 400 5839290 5105089 1\n\n train_unlikes\n uid timestamp item_id is_organic\n 100 3087555 5038504 1\n 100 3087555 9170134 1\n 100 4012270 7833676 0\n 100 21210445 7610592 0\n 100 21210465 7610592 0\n 300 3002130 5196757 1\n 300 3002215 112196 1\n 300 3002215 1876467 1\n 300 3002365 9221711 1\n 300 3002700 7237185 1\n 300 3002755 5747334 1\n 300 3002755 9104028 1\n 300 3002875 9380688 1\n 300 5499715 5096553 1\n 300 5499855 6937744 1\n\n train_undislikes\n uid timestamp item_id is_organic\n 100 7895900 9170134 1\n 100 7895900 5038504 1\n1400 8317355 6175319 1\n1400 24001340 7064726 1\n1400 24001340 8966868 1\n1400 24001340 7757137 1\n1400 24001340 1297781 1\n1400 24001340 3662064 1\n1400 24001340 4120170 1\n1900 22426980 6783683 0\n1900 22426980 9208732 0\n2200 17176865 5580391 1\n2300 23869180 316291 1\n2300 24473970 5481367 1\n2700 22771190 6190054 1\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 124751862 }, "formatted_output": "\n train_listens\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\n 100 40110 732449 0 100 240\n 100 40360 3397170 0 46 130\n 100 40380 7849270 0 100 205\n 100 40640 1449307 0 100 205\n 100 40785 6887232 0 100 145\n 100 40885 2996197 0 100 95\n 100 41130 6474571 0 100 245\n 100 41545 7847600 0 27 250\n 100 41580 7509517 0 15 200\n 100 41580 470320 0 2 165\n 100 42115 6641244 0 1 200\n 100 42115 2581903 0 37 155\n\n train_likes\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\n 100 1260005 7371186 0\n 100 1263935 4943655 0\n 100 2055015 410868 1\n 100 2494620 1730273 1\n 100 3939350 2621343 0\n 100 3940755 6399268 0\n 100 6902675 3982388 0\n 100 6903355 3633039 0\n 100 7035995 4221787 1\n 100 10314390 4099299 1\n 100 18304655 6005659 0\n 100 23471875 8200811 0\n\n train_dislikes\n uid timestamp item_id is_organic\n 100 3087560 9170134 1\n 100 3936560 8661238 1\n 100 20432825 4927727 1\n 400 2980800 399811 1\n 400 3157520 1575108 1\n 400 3417600 9229946 1\n 400 4968885 6593949 1\n 400 4968905 9198404 1\n 400 5519800 999732 1\n 400 5754475 1254798 1\n 400 5754910 3615680 1\n 400 5756180 5826350 1\n 400 5832130 5123138 1\n 400 5832975 4570886 1\n 400 5839290 5105089 1\n\n train_unlikes\n uid timestamp item_id is_organic\n 100 3087555 5038504 1\n 100 3087555 9170134 1\n 100 4012270 7833676 0\n 100 21210445 7610592 0\n 100 21210465 7610592 0\n 300 3002130 5196757 1\n 300 3002215 112196 1\n 300 3002215 1876467 1\n 300 3002365 9221711 1\n 300 3002700 7237185 1\n 300 3002755 5747334 1\n 300 3002755 9104028 1\n 300 3002875 9380688 1\n 300 5499715 5096553 1\n 300 5499855 6937744 1\n\n train_undislikes\n uid timestamp item_id is_organic\n 100 7895900 9170134 1\n 100 7895900 5038504 1\n1400 8317355 6175319 1\n1400 24001340 7064726 1\n1400 24001340 8966868 1\n1400 24001340 7757137 1\n1400 24001340 1297781 1\n1400 24001340 3662064 1\n1400 24001340 4120170 1\n1900 22426980 6783683 0\n1900 22426980 9208732 0\n2200 17176865 5580391 1\n2300 23869180 316291 1\n2300 24473970 5481367 1\n2700 22771190 6190054 1\n" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-25T01:28:22.922Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:22.922Z", "text": "" } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T01:28:24.300Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:24.300Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:28:27.291Z", "callId": "call_H6xDx1xdtC7AIIk0GLkRV0bl", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\ncat=set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.astype(int))\\nfor st in ['validation','test']:\\n p=pd.read_parquet('/task/data/'+st+'_preference_state.parquet')\\n print(st,len(p),p.state.value_counts().to_dict(),p.item_id.isin(cat).mean(),p[p.item_id.isin(cat)].state.value_counts().to_dict(), 'pairs both',p.duplicated(['uid','item_id'],False).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:29.269Z", "content": "Script completed\nWall time 2.0 seconds\nOutput:\n\nvalidation 469124 {'liked': 418435, 'disliked': 50689} 1.0 {'liked': 418435, 'disliked': 50689} pairs both 3420\ntest 484776 {'liked': 432659, 'disliked': 52117} 1.0 {'liked': 432659, 'disliked': 52117} pairs both 3552\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:27.388Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ce2ce8a087d2aa64b16a2b528649", "usage": { "input_tokens": 47392, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 764, "reasoning_output_tokens": 589, "total_tokens": 48156 }, "turn_token_usage": { "input_tokens": 852287, "cached_input_tokens": 810880, "cache_write_input_tokens": 0, "output_tokens": 16843, "reasoning_output_tokens": 8707, "total_tokens": 869130 }, "thread_token_usage": { "input_tokens": 852287, "cached_input_tokens": 810880, "cache_write_input_tokens": 0, "output_tokens": 16843, "reasoning_output_tokens": 8707, "total_tokens": 869130 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:29.247Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f4bcd729-0f35-45d2-b933-c4d83e235768", "process_id": "69573", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\ncat=set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.astype(int))\nfor st in ['validation','test']:\n p=pd.read_parquet('/task/data/'+st+'_preference_state.parquet')\n print(st,len(p),p.state.value_counts().to_dict(),p.item_id.isin(cat).mean(),p[p.item_id.isin(cat)].state.value_counts().to_dict(), 'pairs both',p.duplicated(['uid','item_id'],False).sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\ncat=set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.astype(int))\nfor st in ['validation','test']:\n p=pd.read_parquet('/task/data/'+st+'_preference_state.parquet')\n print(st,len(p),p.state.value_counts().to_dict(),p.item_id.isin(cat).mean(),p[p.item_id.isin(cat)].state.value_counts().to_dict(), 'pairs both',p.duplicated(['uid','item_id'],False).sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation 469124 {'liked': 418435, 'disliked': 50689} 1.0 {'liked': 418435, 'disliked': 50689} pairs both 3420\ntest 484776 {'liked': 432659, 'disliked': 52117} 1.0 {'liked': 432659, 'disliked': 52117} pairs both 3552\n", "stderr": "", "aggregated_output": "validation 469124 {'liked': 418435, 'disliked': 50689} 1.0 {'liked': 418435, 'disliked': 50689} pairs both 3420\ntest 484776 {'liked': 432659, 'disliked': 52117} 1.0 {'liked': 432659, 'disliked': 52117} pairs both 3552\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 780060990 }, "formatted_output": "validation 469124 {'liked': 418435, 'disliked': 50689} 1.0 {'liked': 418435, 'disliked': 50689} pairs both 3420\ntest 484776 {'liked': 432659, 'disliked': 52117} 1.0 {'liked': 432659, 'disliked': 52117} pairs both 3552\n" } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T01:28:39.783Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:39.783Z", "text": "" } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T01:28:50.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:50.022Z", "text": "" } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-25T01:28:58.187Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:58.187Z", "text": "" } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-25T01:29:07.523Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:07.523Z", "text": "" } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-25T01:29:16.813Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:16.813Z", "text": "" } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-25T01:29:26.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:26.152Z", "text": "" } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-25T01:29:35.478Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:35.478Z", "text": "" } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T01:29:44.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:44.805Z", "text": "" } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-25T01:29:54.126Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:54.126Z", "text": "" } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-25T01:30:00.538Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:00.538Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:30:03.051Z", "callId": "call_JNnnH3qUV5TtHv89rz8X6PzV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfor s in ['validation','test']:\\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\\n print(s,x.head(20).to_string(index=False)); print('uid monotonic',x.uid.is_monotonic_increasing,'item within first',x[x.uid==x.uid.iloc[0]].item_id.head(20).tolist())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:30:04.674Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\nvalidation uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\n 800 40055 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\ntest uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3526521 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3526521, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:03.154Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ce3d819887d2804a5c434dc36e12", "usage": { "input_tokens": 48267, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 5157, "reasoning_output_tokens": 5010, "total_tokens": 53424 }, "turn_token_usage": { "input_tokens": 900554, "cached_input_tokens": 858112, "cache_write_input_tokens": 0, "output_tokens": 22000, "reasoning_output_tokens": 13717, "total_tokens": 922554 }, "thread_token_usage": { "input_tokens": 900554, "cached_input_tokens": 858112, "cache_write_input_tokens": 0, "output_tokens": 22000, "reasoning_output_tokens": 13717, "total_tokens": 922554 } } }, { "kind": "context", "timestamp": "2026-09-25T01:30:04.661Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2fd26a89-274c-4942-aaff-a258acac900e", "process_id": "89903", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n print(s,x.head(20).to_string(index=False)); print('uid monotonic',x.uid.is_monotonic_increasing,'item within first',x[x.uid==x.uid.iloc[0]].item_id.head(20).tolist())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n print(s,x.head(20).to_string(index=False)); print('uid monotonic',x.uid.is_monotonic_increasing,'item within first',x[x.uid==x.uid.iloc[0]].item_id.head(20).tolist())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\n 800 40055 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\ntest uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3526521 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3526521, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\n", "stderr": "", "aggregated_output": "validation uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\n 800 40055 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\ntest uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3526521 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3526521, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 434043537 }, "formatted_output": "validation uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\n 800 40055 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\ntest uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\n 100 1730273 liked\n 100 2621343 liked\n 100 3526521 liked\n 100 3535902 liked\n 100 3633039 liked\n 100 4099299 liked\n 100 4221787 liked\n 100 4927727 disliked\n 100 4943655 liked\n 100 5590855 liked\n 100 6568592 liked\n 100 8200811 liked\n 100 8661238 disliked\n 300 5258914 liked\n 300 6961827 liked\n 300 7153398 liked\n 800 31754 liked\nuid monotonic True item within first [410868, 732449, 982903, 1730273, 2621343, 3526521, 3535902, 3633039, 4099299, 4221787, 4927727, 4943655, 5590855, 6568592, 8200811, 8661238]\n" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T01:30:08.041Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:08.041Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:30:11.026Z", "callId": "call_epuOWPq2juk4cmCBmfh5fWQ2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfor s in ['validation','test']:\\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\\n for state in ['liked','disliked']:\\n q=x[x.state==state].groupby('uid').size()\\n print(s,state,'users',len(q),'quantiles',np.percentile(q,[0,25,50,75,90,95,99,100]),'covered64 mean',np.minimum(q,64).sum()/q.sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:30:12.785Z", "content": "Script completed\nWall time 1.7 seconds\nOutput:\n\nvalidation liked users 6769 quantiles [1.0000e+00 7.0000e+00 2.8000e+01 7.5000e+01 1.5800e+02 2.3860e+02\n 4.7024e+02 1.3270e+03] covered64 mean 0.5232951354451707\nvalidation disliked users 4277 quantiles [1.000e+00 1.000e+00 3.000e+00 7.000e+00 2.000e+01 4.000e+01 1.502e+02\n 1.703e+03] covered64 mean 0.6690406202529149\ntest liked users 6831 quantiles [1.000e+00 7.000e+00 2.800e+01 7.700e+01 1.610e+02 2.415e+02 4.757e+02\n 1.838e+03] covered64 mean 0.5169660171174065\ntest disliked users 4349 quantiles [1.00e+00 1.00e+00 3.00e+00 7.00e+00 2.10e+01 4.00e+01 1.56e+02 1.71e+03] covered64 mean 0.669033136980256\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:11.109Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5ce9ce32087d2961ebea5da1f8efd", "usage": { "input_tokens": 54015, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 284, "reasoning_output_tokens": 105, "total_tokens": 54299 }, "turn_token_usage": { "input_tokens": 954569, "cached_input_tokens": 906240, "cache_write_input_tokens": 0, "output_tokens": 22284, "reasoning_output_tokens": 13822, "total_tokens": 976853 }, "thread_token_usage": { "input_tokens": 954569, "cached_input_tokens": 906240, "cache_write_input_tokens": 0, "output_tokens": 22284, "reasoning_output_tokens": 13822, "total_tokens": 976853 } } }, { "kind": "context", "timestamp": "2026-09-25T01:30:12.772Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e0504f8c-e3b9-4f37-ab8d-088a06978c19", "process_id": "21572", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n for state in ['liked','disliked']:\n q=x[x.state==state].groupby('uid').size()\n print(s,state,'users',len(q),'quantiles',np.percentile(q,[0,25,50,75,90,95,99,100]),'covered64 mean',np.minimum(q,64).sum()/q.sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n for state in ['liked','disliked']:\n q=x[x.state==state].groupby('uid').size()\n print(s,state,'users',len(q),'quantiles',np.percentile(q,[0,25,50,75,90,95,99,100]),'covered64 mean',np.minimum(q,64).sum()/q.sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation liked users 6769 quantiles [1.0000e+00 7.0000e+00 2.8000e+01 7.5000e+01 1.5800e+02 2.3860e+02\n 4.7024e+02 1.3270e+03] covered64 mean 0.5232951354451707\nvalidation disliked users 4277 quantiles [1.000e+00 1.000e+00 3.000e+00 7.000e+00 2.000e+01 4.000e+01 1.502e+02\n 1.703e+03] covered64 mean 0.6690406202529149\ntest liked users 6831 quantiles [1.000e+00 7.000e+00 2.800e+01 7.700e+01 1.610e+02 2.415e+02 4.757e+02\n 1.838e+03] covered64 mean 0.5169660171174065\ntest disliked users 4349 quantiles [1.00e+00 1.00e+00 3.00e+00 7.00e+00 2.10e+01 4.00e+01 1.56e+02 1.71e+03] covered64 mean 0.669033136980256\n", "stderr": "", "aggregated_output": "validation liked users 6769 quantiles [1.0000e+00 7.0000e+00 2.8000e+01 7.5000e+01 1.5800e+02 2.3860e+02\n 4.7024e+02 1.3270e+03] covered64 mean 0.5232951354451707\nvalidation disliked users 4277 quantiles [1.000e+00 1.000e+00 3.000e+00 7.000e+00 2.000e+01 4.000e+01 1.502e+02\n 1.703e+03] covered64 mean 0.6690406202529149\ntest liked users 6831 quantiles [1.000e+00 7.000e+00 2.800e+01 7.700e+01 1.610e+02 2.415e+02 4.757e+02\n 1.838e+03] covered64 mean 0.5169660171174065\ntest disliked users 4349 quantiles [1.00e+00 1.00e+00 3.00e+00 7.00e+00 2.10e+01 4.00e+01 1.56e+02 1.71e+03] covered64 mean 0.669033136980256\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 571196940 }, "formatted_output": "validation liked users 6769 quantiles [1.0000e+00 7.0000e+00 2.8000e+01 7.5000e+01 1.5800e+02 2.3860e+02\n 4.7024e+02 1.3270e+03] covered64 mean 0.5232951354451707\nvalidation disliked users 4277 quantiles [1.000e+00 1.000e+00 3.000e+00 7.000e+00 2.000e+01 4.000e+01 1.502e+02\n 1.703e+03] covered64 mean 0.6690406202529149\ntest liked users 6831 quantiles [1.000e+00 7.000e+00 2.800e+01 7.700e+01 1.610e+02 2.415e+02 4.757e+02\n 1.838e+03] covered64 mean 0.5169660171174065\ntest disliked users 4349 quantiles [1.00e+00 1.00e+00 3.00e+00 7.00e+00 2.10e+01 4.00e+01 1.56e+02 1.71e+03] covered64 mean 0.669033136980256\n" } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-25T01:30:24.204Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:24.204Z", "text": "" } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-25T01:30:33.518Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:33.518Z", "text": "" } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-25T01:30:43.166Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:43.166Z", "text": "" } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-25T01:30:52.464Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:52.464Z", "text": "" } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-25T01:31:01.601Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:01.601Z", "text": "" } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-25T01:31:11.593Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:11.593Z", "text": "" } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-25T01:31:13.398Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:13.398Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:31:14.371Z", "text": "I’m implementing the ranker around rolling, cutoff-correct training examples: each example pools only pre-cutoff history and predicts a previously unseen organic discovery in the next seven-day window. The candidate tower also receives only pre-cutoff trend statistics. This mirrors the backtest while staying scalable—ragged event streams and sampled negatives, with no global interaction matrix or decomposition." }, { "kind": "tool", "timestamp": "2026-09-25T01:34:13.817Z", "callId": "call_8wrYVEr07Rw45RYSg1vtjhqB", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_and_rank.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a CPU PyTorch two-tower discovery ranker.\\n+\\n+The implementation intentionally operates on event streams and sampled\\n+positive/negative pairs. It never materializes or factorizes a dense global\\n+user-item interaction matrix. Candidate vectors are computed independently\\n+of users and the final score is exactly a dot product.\\n+\\\"\\\"\\\"\\n+\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+import time\\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_END = 24_786_800\\n+WEEK = 7 * 24 * 60 * 60\\n+GAP = 30 * 60\\n+VAL_END = TRAIN_END + GAP + WEEK\\n+\\n+RECENT_LEN = 64\\n+ORGANIC_LEN = 64\\n+LIKE_LEN = 96\\n+DISLIKE_LEN = 32\\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 read_events(family: str, stage: str, columns: list[str]) -> pd.DataFrame:\\n+ frames = [pd.read_parquet(DATA / f\\\"train_{family}.parquet\\\", columns=columns)]\\n+ if stage == \\\"final\\\":\\n+ frames.append(pd.read_parquet(DATA / f\\\"val_{family}.parquet\\\", columns=columns))\\n+ frame = pd.concat(frames, ignore_index=True)\\n+ return frame\\n+\\n+\\n+def map_catalog(raw: np.ndarray, sorted_ids: np.ndarray, sorted_to_catalog: np.ndarray):\\n+ loc = np.searchsorted(sorted_ids, raw)\\n+ valid = loc < len(sorted_ids)\\n+ safe = np.minimum(loc, len(sorted_ids) - 1)\\n+ valid &= sorted_ids[safe] == raw\\n+ return valid, sorted_to_catalog[safe]\\n+\\n+\\n+def sorted_stream(frame: pd.DataFrame, final_stage: bool) -> pd.DataFrame:\\n+ if final_stage or not frame.uid.is_monotonic_increasing:\\n+ frame = frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\")\\n+ return frame.reset_index(drop=True)\\n+\\n+\\n+def user_bounds(stream_uids: np.ndarray, users: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ return (\\n+ np.searchsorted(stream_uids, users, side=\\\"left\\\"),\\n+ np.searchsorted(stream_uids, users, side=\\\"right\\\"),\\n+ )\\n+\\n+\\n+def aggregate_aligned_weeks(\\n+ timestamps: np.ndarray,\\n+ item_idx: np.ndarray,\\n+ first_cutoff: int,\\n+ n_windows: int,\\n+ n_items: int,\\n+ lookback: int = 13,\\n+) -> tuple[np.ndarray, int]:\\n+ \\\"\\\"\\\"Aggregate events into weeks aligned to the rolling cutoffs.\\\"\\\"\\\"\\n+ origin = first_cutoff - lookback * WEEK\\n+ bucket = np.floor_divide(timestamps.astype(np.int64) - origin, WEEK)\\n+ rows = lookback + n_windows\\n+ valid = (bucket >= 0) & (bucket < rows)\\n+ flat = bucket[valid] * n_items + item_idx[valid].astype(np.int64)\\n+ counts = np.bincount(flat, minlength=rows * n_items).reshape(rows, n_items)\\n+ return counts.astype(np.float32), lookback\\n+\\n+\\n+def window_sum(counts: np.ndarray, edge: int, weeks: int) -> np.ndarray:\\n+ lo = max(0, edge - weeks)\\n+ return counts[lo:edge].sum(axis=0, dtype=np.float32)\\n+\\n+\\n+def exact_count(\\n+ timestamps: np.ndarray,\\n+ item_idx: np.ndarray,\\n+ cutoff: int,\\n+ seconds: int,\\n+ n_items: int,\\n+) -> np.ndarray:\\n+ mask = (timestamps < cutoff) & (timestamps >= cutoff - seconds)\\n+ return np.bincount(item_idx[mask], minlength=n_items).astype(np.float32)\\n+\\n+\\n+def rank_trend(one_week: np.ndarray, four_week: np.ndarray, twelve_week: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Return a smooth score whose ordering is the desired popularity prior.\\\"\\\"\\\"\\n+ order = np.lexsort((np.arange(len(one_week)), -twelve_week, -four_week, -one_week))\\n+ ranks = np.empty(len(order), dtype=np.int32)\\n+ ranks[order] = np.arange(len(order), dtype=np.int32)\\n+ score = -np.log1p(ranks.astype(np.float32))\\n+ return ((score - score.mean()) / (score.std() + 1e-6)).astype(np.float32)\\n+\\n+\\n+def make_item_features(\\n+ cutoffs: np.ndarray,\\n+ inference_cutoff: int,\\n+ n_items: int,\\n+ positive_ts: np.ndarray,\\n+ positive_item: np.ndarray,\\n+ organic_ts: np.ndarray,\\n+ organic_item: np.ndarray,\\n+ organic_like_ts: np.ndarray,\\n+ organic_like_item: np.ndarray,\\n+) -> tuple[np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Build cutoff-correct rolling item features plus an explicit trend prior.\\\"\\\"\\\"\\n+ nwin = len(cutoffs)\\n+ streams = []\\n+ for ts, item in [\\n+ (positive_ts, positive_item),\\n+ (organic_ts, organic_item),\\n+ (organic_like_ts, organic_like_item),\\n+ ]:\\n+ streams.append(aggregate_aligned_weeks(ts, item, int(cutoffs[0]), nwin, n_items))\\n+\\n+ raw = np.zeros((nwin + 1, n_items, 9), dtype=np.float32)\\n+ trend = np.zeros((nwin + 1, n_items), dtype=np.float32)\\n+ for ci in range(nwin):\\n+ edge = streams[0][1] + ci\\n+ vals: list[np.ndarray] = []\\n+ for counts, _ in streams:\\n+ vals.extend([window_sum(counts, edge, w) for w in (1, 4, 12)])\\n+ raw[ci] = np.stack(vals, axis=1)\\n+ trend[ci] = rank_trend(vals[3], vals[4], vals[5])\\n+\\n+ vals = []\\n+ for ts, item in [\\n+ (positive_ts, positive_item),\\n+ (organic_ts, organic_item),\\n+ (organic_like_ts, organic_like_item),\\n+ ]:\\n+ vals.extend(\\n+ [\\n+ exact_count(ts, item, inference_cutoff, w * WEEK, n_items)\\n+ for w in (1, 4, 12)\\n+ ]\\n+ )\\n+ raw[-1] = np.stack(vals, axis=1)\\n+ trend[-1] = rank_trend(vals[3], vals[4], vals[5])\\n+\\n+ logged = np.log1p(raw)\\n+ # Normalize per cutoff. Thus a feature has the same interpretation as the\\n+ # catalog grows and traffic volume changes in production.\\n+ mean = logged.mean(axis=1, keepdims=True)\\n+ std = logged.std(axis=1, keepdims=True) + 1e-5\\n+ features = np.clip((logged - mean) / std, -4.0, 8.0).astype(np.float32)\\n+ return features, trend\\n+\\n+\\n+def build_cache(stage: str, windows: int, force: bool = False) -> Path:\\n+ cache = OUT / f\\\"cache_{stage}_w{windows}.npz\\\"\\n+ if cache.exists() and not force:\\n+ print(f\\\"using existing cache {cache}\\\", flush=True)\\n+ return cache\\n+\\n+ t0 = time.time()\\n+ catalog_frame = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog = catalog_frame.item_id.to_numpy(dtype=np.int64)\\n+ n_items = len(catalog)\\n+ cat_order = np.argsort(catalog)\\n+ sorted_ids = catalog[cat_order]\\n+\\n+ listen_cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ listens = read_events(\\\"listens\\\", stage, listen_cols)\\n+ raw_item = listens.item_id.to_numpy(dtype=np.int64, copy=False)\\n+ valid, mapped = map_catalog(raw_item, sorted_ids, cat_order)\\n+ listens = listens.loc[valid].copy()\\n+ listens[\\\"item_idx\\\"] = mapped[valid].astype(np.int32)\\n+ listens = sorted_stream(listens, stage == \\\"final\\\")\\n+ users = np.sort(listens.uid.unique().astype(np.int64))\\n+ n_users = len(users)\\n+ print(\\n+ f\\\"catalog listens={len(listens):,}, users={n_users:,}, items={n_items:,} \\\"\\n+ f\\\"({time.time()-t0:.1f}s)\\\",\\n+ flush=True,\\n+ )\\n+\\n+ action_frames: dict[str, pd.DataFrame] = {}\\n+ for family in (\\\"likes\\\", \\\"unlikes\\\", \\\"dislikes\\\", \\\"undislikes\\\"):\\n+ frame = read_events(family, stage, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ raw = frame.item_id.to_numpy(dtype=np.int64, copy=False)\\n+ keep, idx = map_catalog(raw, sorted_ids, cat_order)\\n+ frame = frame.loc[keep].copy()\\n+ frame[\\\"item_idx\\\"] = idx[keep].astype(np.int32)\\n+ action_frames[family] = sorted_stream(frame, stage == \\\"final\\\")\\n+\\n+ inference_cutoff = TRAIN_END if stage == \\\"validation\\\" else VAL_END\\n+ last_training_cutoff = inference_cutoff - GAP - WEEK\\n+ cutoffs = last_training_cutoff - WEEK * np.arange(windows - 1, -1, -1, dtype=np.int64)\\n+ all_cutoffs = np.concatenate([cutoffs, np.asarray([inference_cutoff], dtype=np.int64)])\\n+\\n+ pos = listens[listens.played_ratio_pct >= 50]\\n+ org = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\\n+ org_likes = action_frames[\\\"likes\\\"][action_frames[\\\"likes\\\"].is_organic == 1]\\n+ item_features, trend = make_item_features(\\n+ cutoffs,\\n+ inference_cutoff,\\n+ n_items,\\n+ pos.timestamp.to_numpy(dtype=np.int64),\\n+ pos.item_idx.to_numpy(dtype=np.int32),\\n+ org.timestamp.to_numpy(dtype=np.int64),\\n+ org.item_idx.to_numpy(dtype=np.int32),\\n+ org_likes.timestamp.to_numpy(dtype=np.int64),\\n+ org_likes.item_idx.to_numpy(dtype=np.int32),\\n+ )\\n+ print(f\\\"item features built ({time.time()-t0:.1f}s)\\\", flush=True)\\n+\\n+ pad = n_items\\n+ n_profiles = len(all_cutoffs) * n_users\\n+ recent = np.full((n_profiles, RECENT_LEN), pad, dtype=np.int32)\\n+ organic = np.full((n_profiles, ORGANIC_LEN), pad, dtype=np.int32)\\n+ liked = np.full((n_profiles, LIKE_LEN), pad, dtype=np.int32)\\n+ disliked = np.full((n_profiles, DISLIKE_LEN), pad, dtype=np.int32)\\n+ profile_stats = np.zeros((n_profiles, 8), dtype=np.float32)\\n+\\n+ pos_uid = pos.uid.to_numpy(dtype=np.int64)\\n+ org_uid = org.uid.to_numpy(dtype=np.int64)\\n+ pos_bounds = user_bounds(pos_uid, users)\\n+ org_bounds = user_bounds(org_uid, users)\\n+ pos_ts = pos.timestamp.to_numpy(dtype=np.int64)\\n+ pos_it = pos.item_idx.to_numpy(dtype=np.int32)\\n+ org_ts = org.timestamp.to_numpy(dtype=np.int64)\\n+ org_it = org.item_idx.to_numpy(dtype=np.int32)\\n+\\n+ def action_stream(on_name: str, off_name: str):\\n+ on = action_frames[on_name][[\\\"uid\\\", \\\"timestamp\\\", \\\"item_idx\\\"]].copy()\\n+ on[\\\"active\\\"] = np.int8(1)\\n+ off = action_frames[off_name][[\\\"uid\\\", \\\"timestamp\\\", \\\"item_idx\\\"]].copy()\\n+ off[\\\"active\\\"] = np.int8(0)\\n+ stream = pd.concat([on, off], ignore_index=True).sort_values(\\n+ [\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\"\\n+ )\\n+ suid = stream.uid.to_numpy(dtype=np.int64)\\n+ return (\\n+ stream.timestamp.to_numpy(dtype=np.int64),\\n+ stream.item_idx.to_numpy(dtype=np.int32),\\n+ stream.active.to_numpy(dtype=np.int8),\\n+ user_bounds(suid, users),\\n+ )\\n+\\n+ like_ts, like_it, like_active, like_bounds = action_stream(\\\"likes\\\", \\\"unlikes\\\")\\n+ dislike_ts, dislike_it, dislike_active, dislike_bounds = action_stream(\\n+ \\\"dislikes\\\", \\\"undislikes\\\"\\n+ )\\n+\\n+ # Authoritative explicit state is used for the request-time profile.\\n+ pref_name = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pvalid, pidx = map_catalog(pref.item_id.to_numpy(dtype=np.int64), sorted_ids, cat_order)\\n+ pref = pref.loc[pvalid].copy()\\n+ pref[\\\"item_idx\\\"] = pidx[pvalid].astype(np.int32)\\n+ pref_lookup: dict[tuple[int, str], np.ndarray] = {\\n+ (int(uid), str(state)): group.item_idx.to_numpy(dtype=np.int32)\\n+ for (uid, state), group in pref.groupby([\\\"uid\\\", \\\"state\\\"], sort=False)\\n+ }\\n+\\n+ for ui, uid in enumerate(users):\\n+ ps, pe = int(pos_bounds[0][ui]), int(pos_bounds[1][ui])\\n+ os, oe = int(org_bounds[0][ui]), int(org_bounds[1][ui])\\n+ l_s, l_e = int(like_bounds[0][ui]), int(like_bounds[1][ui])\\n+ d_s, d_e = int(dislike_bounds[0][ui]), int(dislike_bounds[1][ui])\\n+ lp, dp = l_s, d_s\\n+ like_state: dict[int, int] = {}\\n+ dislike_state: dict[int, int] = {}\\n+ for ci, cutoff in enumerate(all_cutoffs):\\n+ pid = ci * n_users + ui\\n+ pend = ps + int(np.searchsorted(pos_ts[ps:pe], cutoff, side=\\\"left\\\"))\\n+ oend = os + int(np.searchsorted(org_ts[os:oe], cutoff, side=\\\"left\\\"))\\n+ pitems = pos_it[max(ps, pend - RECENT_LEN) : pend][::-1]\\n+ oitems = org_it[max(os, oend - ORGANIC_LEN) : oend][::-1]\\n+ recent[pid, : len(pitems)] = pitems\\n+ organic[pid, : len(oitems)] = oitems\\n+\\n+ while lp < l_e and like_ts[lp] < cutoff:\\n+ item = int(like_it[lp])\\n+ if like_active[lp]:\\n+ like_state[item] = int(like_ts[lp])\\n+ else:\\n+ like_state.pop(item, None)\\n+ lp += 1\\n+ while dp < d_e and dislike_ts[dp] < cutoff:\\n+ item = int(dislike_it[dp])\\n+ if dislike_active[dp]:\\n+ dislike_state[item] = int(dislike_ts[dp])\\n+ else:\\n+ dislike_state.pop(item, None)\\n+ dp += 1\\n+\\n+ lorder = sorted(like_state, key=like_state.get, reverse=True)\\n+ dorder = sorted(dislike_state, key=dislike_state.get, reverse=True)\\n+ if ci == len(all_cutoffs) - 1:\\n+ supplied_l = pref_lookup.get((int(uid), \\\"liked\\\"))\\n+ supplied_d = pref_lookup.get((int(uid), \\\"disliked\\\"))\\n+ if supplied_l is not None:\\n+ lorder = sorted(\\n+ map(int, supplied_l), key=lambda x: like_state.get(x, -1), reverse=True\\n+ )\\n+ if supplied_d is not None:\\n+ dorder = sorted(\\n+ map(int, supplied_d),\\n+ key=lambda x: dislike_state.get(x, -1),\\n+ reverse=True,\\n+ )\\n+ litems = np.asarray(lorder[:LIKE_LEN], dtype=np.int32)\\n+ ditems = np.asarray(dorder[:DISLIKE_LEN], dtype=np.int32)\\n+ liked[pid, : len(litems)] = litems\\n+ disliked[pid, : len(ditems)] = ditems\\n+ profile_stats[pid] = np.log1p(\\n+ [\\n+ pend - ps,\\n+ oend - os,\\n+ len(like_state),\\n+ len(dislike_state),\\n+ min(RECENT_LEN, pend - ps),\\n+ min(ORGANIC_LEN, oend - os),\\n+ min(LIKE_LEN, len(lorder)),\\n+ min(DISLIKE_LEN, len(dorder)),\\n+ ]\\n+ )\\n+ if (ui + 1) % 2000 == 0:\\n+ print(f\\\"profiles {ui+1:,}/{n_users:,}\\\", flush=True)\\n+\\n+ stat_mean = profile_stats.mean(axis=0, keepdims=True)\\n+ stat_std = profile_stats.std(axis=0, keepdims=True) + 1e-5\\n+ profile_stats = np.clip((profile_stats - stat_mean) / stat_std, -4, 6).astype(np.float32)\\n+ print(f\\\"profiles built ({time.time()-t0:.1f}s)\\\", flush=True)\\n+\\n+ # Rolling labels: an organic 80%-complete listen is grade 1 and an organic\\n+ # like is grade 3. Restrict to items first heard after that cutoff to model\\n+ # discovery/eligibility rather than memorizing repeats.\\n+ first_start = int(cutoffs[0] + GAP)\\n+ target_parts = []\\n+ for frame, grade in ((org, 1), (org_likes, 3)):\\n+ ts = frame.timestamp.to_numpy(dtype=np.int64)\\n+ wi = np.floor_divide(ts - first_start, WEEK)\\n+ keep = (wi >= 0) & (wi < windows)\\n+ part = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": frame.uid.to_numpy(dtype=np.int64)[keep],\\n+ \\\"item\\\": frame.item_idx.to_numpy(dtype=np.int32)[keep],\\n+ \\\"wi\\\": wi[keep].astype(np.int16),\\n+ \\\"grade\\\": np.full(int(keep.sum()), grade, dtype=np.int8),\\n+ }\\n+ )\\n+ target_parts.append(part)\\n+ targets = pd.concat(target_parts, ignore_index=True)\\n+ targets = targets.groupby([\\\"uid\\\", \\\"item\\\", \\\"wi\\\"], as_index=False, sort=False).grade.max()\\n+\\n+ first_heard = (\\n+ listens.groupby([\\\"uid\\\", \\\"item_idx\\\"], sort=False, observed=True)\\n+ .timestamp.min()\\n+ .rename(\\\"first_ts\\\")\\n+ .reset_index()\\n+ .rename(columns={\\\"item_idx\\\": \\\"item\\\"})\\n+ )\\n+ targets = targets.merge(first_heard, on=[\\\"uid\\\", \\\"item\\\"], how=\\\"left\\\")\\n+ target_cutoff = cutoffs[targets.wi.to_numpy(dtype=np.int64)]\\n+ targets = targets[targets.first_ts.fillna(np.iinfo(np.int64).max).to_numpy() >= target_cutoff]\\n+ targets[\\\"ui\\\"] = np.searchsorted(users, targets.uid.to_numpy(dtype=np.int64)).astype(np.int32)\\n+ targets = targets.sort_values([\\\"wi\\\", \\\"uid\\\", \\\"item\\\"], kind=\\\"stable\\\")\\n+ print(\\n+ f\\\"training pairs={len(targets):,}, grade3={(targets.grade==3).sum():,} \\\"\\n+ f\\\"({time.time()-t0:.1f}s)\\\",\\n+ flush=True,\\n+ )\\n+\\n+ target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ target_ui = np.searchsorted(users, target_users).astype(np.int32)\\n+ if not np.array_equal(users[target_ui], target_users):\\n+ raise RuntimeError(\\\"target user missing from interaction-user index\\\")\\n+\\n+ pair_items = targets.item.to_numpy(dtype=np.int32)\\n+ frequency = np.bincount(pair_items, minlength=n_items).astype(np.float64)\\n+ neg_prob = np.sqrt(frequency + 1.0)\\n+ neg_prob /= neg_prob.sum()\\n+\\n+ np.savez(\\n+ cache,\\n+ users=users,\\n+ catalog=catalog,\\n+ cutoffs=cutoffs,\\n+ inference_cutoff=np.asarray(inference_cutoff, dtype=np.int64),\\n+ recent=recent,\\n+ organic=organic,\\n+ liked=liked,\\n+ disliked=disliked,\\n+ profile_stats=profile_stats,\\n+ item_features=item_features,\\n+ trend=trend,\\n+ ex_ci=targets.wi.to_numpy(dtype=np.int16),\\n+ ex_ui=targets.ui.to_numpy(dtype=np.int32),\\n+ ex_item=pair_items,\\n+ ex_grade=targets.grade.to_numpy(dtype=np.int8),\\n+ target_users=target_users,\\n+ target_ui=target_ui,\\n+ neg_prob=neg_prob.astype(np.float32),\\n+ )\\n+ print(f\\\"saved {cache} ({cache.stat().st_size/1e6:.1f} MB, {time.time()-t0:.1f}s)\\\", flush=True)\\n+ return cache\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user tower; deliberately contains no user-ID embedding.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, hist_dim: int, out_dim: int, stats_dim: int):\\n+ super().__init__()\\n+ self.n_items = n_items\\n+ self.embedding = nn.Embedding(n_items + 1, hist_dim, padding_idx=n_items)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(4 * hist_dim + stats_dim, 192),\\n+ nn.LayerNorm(192),\\n+ nn.GELU(),\\n+ nn.Linear(192, 128),\\n+ nn.GELU(),\\n+ nn.Linear(128, out_dim),\\n+ )\\n+ self.register_buffer(\\\"recent_weight\\\", torch.exp(-torch.arange(RECENT_LEN) / 28.0))\\n+ self.register_buffer(\\\"organic_weight\\\", torch.exp(-torch.arange(ORGANIC_LEN) / 36.0))\\n+ self.register_buffer(\\\"like_weight\\\", torch.exp(-torch.arange(LIKE_LEN) / 72.0))\\n+ self.register_buffer(\\\"dislike_weight\\\", torch.exp(-torch.arange(DISLIKE_LEN) / 24.0))\\n+\\n+ def pool(self, ids: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\\n+ mask = ids.ne(self.n_items)\\n+ weight = weights[: ids.shape[1]].to(dtype=torch.float32).unsqueeze(0) * mask\\n+ emb = self.embedding(ids)\\n+ return (emb * weight.unsqueeze(-1)).sum(1) / weight.sum(1, keepdim=True).clamp_min(1.0)\\n+\\n+ def forward(\\n+ self,\\n+ recent: torch.Tensor,\\n+ organic: torch.Tensor,\\n+ liked: torch.Tensor,\\n+ disliked: torch.Tensor,\\n+ stats: torch.Tensor,\\n+ semantic_scale: float,\\n+ ) -> torch.Tensor:\\n+ pooled = torch.cat(\\n+ [\\n+ self.pool(recent, self.recent_weight),\\n+ self.pool(organic, self.organic_weight),\\n+ self.pool(liked, self.like_weight),\\n+ self.pool(disliked, self.dislike_weight),\\n+ stats,\\n+ ],\\n+ dim=1,\\n+ )\\n+ semantic = F.normalize(self.network(pooled), dim=1) * semantic_scale\\n+ # The final constant coordinate turns the item's trend coordinate into\\n+ # a legal dot-product bias while preserving independent precomputation.\\n+ return torch.cat([semantic, torch.ones_like(semantic[:, :1])], dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Candidate tower using item identity and cutoff-correct offline features.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, id_dim: int, feature_dim: int, out_dim: int):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(n_items, id_dim)\\n+ self.feature_network = nn.Sequential(\\n+ nn.Linear(feature_dim, 48), nn.GELU(), nn.Linear(48, 48), nn.GELU()\\n+ )\\n+ self.network = nn.Sequential(\\n+ nn.Linear(id_dim + 48, 160),\\n+ nn.LayerNorm(160),\\n+ nn.GELU(),\\n+ nn.Linear(160, out_dim),\\n+ )\\n+\\n+ def forward(\\n+ self, ids: torch.Tensor, features: torch.Tensor, trend: torch.Tensor, alpha: float\\n+ ) -> torch.Tensor:\\n+ shape = ids.shape\\n+ flat_ids = ids.reshape(-1)\\n+ flat_features = features.reshape(-1, features.shape[-1])\\n+ semantic = self.network(\\n+ torch.cat([self.embedding(flat_ids), self.feature_network(flat_features)], dim=1)\\n+ )\\n+ semantic = F.normalize(semantic, dim=1).reshape(*shape, -1)\\n+ bias = (trend.reshape(*shape, 1) * alpha).to(semantic.dtype)\\n+ return torch.cat([semantic, bias], dim=-1)\\n+\\n+\\n+class TwoTowerRanker(nn.Module):\\n+ def __init__(\\n+ self,\\n+ n_items: int,\\n+ feature_dim: int,\\n+ stats_dim: int,\\n+ hist_dim: int = 48,\\n+ id_dim: int = 48,\\n+ semantic_dim: int = 64,\\n+ semantic_scale: float = 10.0,\\n+ ):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, hist_dim, semantic_dim, stats_dim)\\n+ self.item_tower = ItemTower(n_items, id_dim, feature_dim, semantic_dim)\\n+ self.semantic_scale = semantic_scale\\n+\\n+ def encode_user(self, recent, organic, liked, disliked, stats):\\n+ return self.user_tower(\\n+ recent, organic, liked, disliked, stats, self.semantic_scale\\n+ )\\n+\\n+ def encode_item(self, ids, features, trend, alpha: float):\\n+ return self.item_tower(ids, features, trend, alpha)\\n+\\n+\\n+def load_cache(path: Path) -> dict[str, np.ndarray]:\\n+ with np.load(path, allow_pickle=False) as artifact:\\n+ return {name: artifact[name].copy() for name in artifact.files}\\n+\\n+\\n+def cache_tensors(cache: dict[str, np.ndarray]) -> dict[str, torch.Tensor]:\\n+ names = [\\\"recent\\\", \\\"organic\\\", \\\"liked\\\", \\\"disliked\\\", \\\"profile_stats\\\", \\\"item_features\\\", \\\"trend\\\"]\\n+ return {name: torch.from_numpy(cache[name]) for name in names}\\n+\\n+\\n+def model_config(cache: dict[str, np.ndarray], args: argparse.Namespace) -> dict[str, int | float]:\\n+ return {\\n+ \\\"n_items\\\": int(len(cache[\\\"catalog\\\"])),\\n+ \\\"feature_dim\\\": int(cache[\\\"item_features\\\"].shape[-1]),\\n+ \\\"stats_dim\\\": int(cache[\\\"profile_stats\\\"].shape[-1]),\\n+ \\\"hist_dim\\\": int(args.hist_dim),\\n+ \\\"id_dim\\\": int(args.id_dim),\\n+ \\\"semantic_dim\\\": int(args.dim),\\n+ \\\"semantic_scale\\\": float(args.semantic_scale),\\n+ }\\n+\\n+\\n+def train_model(cache_path: Path, args: argparse.Namespace) -> Path:\\n+ seed_everything(args.seed)\\n+ torch.set_num_threads(args.threads)\\n+ cache = load_cache(cache_path)\\n+ tensors = cache_tensors(cache)\\n+ config = model_config(cache, args)\\n+ model = TwoTowerRanker(**config)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)\\n+\\n+ ex_ci = cache[\\\"ex_ci\\\"].astype(np.int64)\\n+ ex_ui = cache[\\\"ex_ui\\\"].astype(np.int64)\\n+ ex_item = cache[\\\"ex_item\\\"].astype(np.int64)\\n+ ex_grade = cache[\\\"ex_grade\\\"].astype(np.int64)\\n+ nwin = len(cache[\\\"cutoffs\\\"])\\n+ nusers = len(cache[\\\"users\\\"])\\n+ by_window = [np.flatnonzero(ex_ci == ci) for ci in range(nwin)]\\n+ available = np.asarray([len(x) > 0 for x in by_window])\\n+ recency = np.exp((np.arange(nwin) - nwin + 1) / args.window_half_life * math.log(2.0))\\n+ window_prob = np.sqrt(np.asarray([max(1, len(x)) for x in by_window])) * recency * available\\n+ window_prob /= window_prob.sum()\\n+ g1 = [idx[ex_grade[idx] == 1] for idx in by_window]\\n+ g3 = [idx[ex_grade[idx] == 3] for idx in by_window]\\n+ rng = np.random.default_rng(args.seed)\\n+ all_positive_items = ex_item\\n+\\n+ ckpt_path = OUT / f\\\"{args.name}.pt\\\"\\n+ model.train()\\n+ started = time.time()\\n+ smooth = None\\n+ for step in range(1, args.steps + 1):\\n+ ci = int(rng.choice(nwin, p=window_prob))\\n+ n3 = int(round(args.batch_size * args.like_fraction)) if len(g3[ci]) else 0\\n+ n1 = args.batch_size - n3\\n+ chosen = np.concatenate(\\n+ [\\n+ rng.choice(g1[ci] if len(g1[ci]) else by_window[ci], n1, replace=True),\\n+ rng.choice(g3[ci], n3, replace=True) if n3 else np.empty(0, dtype=np.int64),\\n+ ]\\n+ )\\n+ rng.shuffle(chosen)\\n+ ui = ex_ui[chosen]\\n+ positive = ex_item[chosen]\\n+ pids = ci * nusers + ui\\n+\\n+ half = args.negatives // 2\\n+ negative = np.empty((args.batch_size, args.negatives), dtype=np.int64)\\n+ negative[:, :half] = rng.integers(0, config[\\\"n_items\\\"], size=(args.batch_size, half))\\n+ pick = rng.integers(0, len(all_positive_items), size=(args.batch_size, args.negatives - half))\\n+ negative[:, half:] = all_positive_items[pick]\\n+ collision = negative == positive[:, None]\\n+ if collision.any():\\n+ negative[collision] = rng.integers(0, config[\\\"n_items\\\"], size=int(collision.sum()))\\n+ candidate = np.concatenate([positive[:, None], negative], axis=1)\\n+\\n+ pid_t = torch.from_numpy(pids)\\n+ cand_t = torch.from_numpy(candidate)\\n+ user_vec = model.encode_user(\\n+ tensors[\\\"recent\\\"][pid_t],\\n+ tensors[\\\"organic\\\"][pid_t],\\n+ tensors[\\\"liked\\\"][pid_t],\\n+ tensors[\\\"disliked\\\"][pid_t],\\n+ tensors[\\\"profile_stats\\\"][pid_t],\\n+ )\\n+ item_vec = model.encode_item(\\n+ cand_t,\\n+ tensors[\\\"item_features\\\"][ci, cand_t],\\n+ tensors[\\\"trend\\\"][ci, cand_t],\\n+ args.train_alpha,\\n+ )\\n+ logits = torch.einsum(\\\"bd,bnd->bn\\\", user_vec, item_vec)\\n+ loss = F.cross_entropy(logits, torch.zeros(args.batch_size, dtype=torch.long))\\n+\\n+ optimizer.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ value = float(loss.detach())\\n+ smooth = value if smooth is None else 0.98 * smooth + 0.02 * value\\n+ if step == 1 or step % args.log_every == 0:\\n+ rate = step * args.batch_size / max(1e-6, time.time() - started)\\n+ print(\\n+ f\\\"step={step}/{args.steps} loss={value:.4f} ema={smooth:.4f} \\\"\\n+ f\\\"pairs/s={rate:,.0f} window={ci}\\\",\\n+ flush=True,\\n+ )\\n+ if step % args.save_every == 0 or step == args.steps:\\n+ payload = {\\n+ \\\"model\\\": model.state_dict(),\\n+ \\\"config\\\": config,\\n+ \\\"stage\\\": args.stage,\\n+ \\\"windows\\\": args.windows,\\n+ \\\"step\\\": step,\\n+ \\\"train_alpha\\\": args.train_alpha,\\n+ \\\"seed\\\": args.seed,\\n+ }\\n+ torch.save(payload, ckpt_path)\\n+ if step != args.steps:\\n+ torch.save(payload, OUT / f\\\"{args.name}_step{step}.pt\\\")\\n+ print(f\\\"saved model {ckpt_path} ({time.time()-started:.1f}s)\\\", flush=True)\\n+ return ckpt_path\\n+\\n+\\n+def load_model(checkpoint: Path) -> tuple[TwoTowerRanker, dict]:\\n+ payload = torch.load(checkpoint, map_location=\\\"cpu\\\", weights_only=False)\\n+ model = TwoTowerRanker(**payload[\\\"config\\\"])\\n+ model.load_state_dict(payload[\\\"model\\\"])\\n+ model.eval()\\n+ return model, payload\\n+\\n+\\n+def rank_model(\\n+ cache_path: Path,\\n+ checkpoint: Path,\\n+ output: Path,\\n+ alpha: float,\\n+ threads: int,\\n+) -> None:\\n+ torch.set_num_threads(threads)\\n+ cache = load_cache(cache_path)\\n+ tensors = cache_tensors(cache)\\n+ model, _ = load_model(checkpoint)\\n+ nusers = len(cache[\\\"users\\\"])\\n+ inference_ci = len(cache[\\\"cutoffs\\\"])\\n+ target_ui = cache[\\\"target_ui\\\"].astype(np.int64)\\n+ pids = inference_ci * nusers + target_ui\\n+ item_ids = torch.arange(len(cache[\\\"catalog\\\"]), dtype=torch.long)\\n+ with torch.inference_mode():\\n+ item_vec = model.encode_item(\\n+ item_ids,\\n+ tensors[\\\"item_features\\\"][inference_ci],\\n+ tensors[\\\"trend\\\"][inference_ci],\\n+ alpha,\\n+ )\\n+\\n+ eligibility_name = (\\n+ \\\"validation_eligible_candidates.npz\\\"\\n+ if \\\"validation\\\" in cache_path.name\\n+ else \\\"test_eligible_candidates.npz\\\"\\n+ )\\n+ with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\\n+ eligible_uids = artifact[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = artifact[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = artifact[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+ if not np.array_equal(eligible_uids, cache[\\\"target_users\\\"]):\\n+ raise RuntimeError(\\\"eligibility and target-user order differ\\\")\\n+ if not np.array_equal(eligible_items, cache[\\\"catalog\\\"]):\\n+ raise RuntimeError(\\\"eligibility and catalog order differ\\\")\\n+\\n+ result_uid: list[np.ndarray] = []\\n+ result_item: list[np.ndarray] = []\\n+ result_rank: list[np.ndarray] = []\\n+ batch = 256\\n+ with torch.inference_mode():\\n+ for start in range(0, len(pids), batch):\\n+ stop = min(start + batch, len(pids))\\n+ pid_t = torch.from_numpy(pids[start:stop])\\n+ user_vec = model.encode_user(\\n+ tensors[\\\"recent\\\"][pid_t],\\n+ tensors[\\\"organic\\\"][pid_t],\\n+ tensors[\\\"liked\\\"][pid_t],\\n+ tensors[\\\"disliked\\\"][pid_t],\\n+ tensors[\\\"profile_stats\\\"][pid_t],\\n+ )\\n+ scores = user_vec @ item_vec.T\\n+ mask = np.unpackbits(\\n+ packed[start:stop], axis=1, count=len(cache[\\\"catalog\\\"]), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\\n+ top = torch.topk(scores, k=100, dim=1, largest=True, sorted=True).indices.numpy()\\n+ bsz = stop - start\\n+ result_uid.append(np.repeat(cache[\\\"target_users\\\"][start:stop], 100))\\n+ result_item.append(cache[\\\"catalog\\\"][top.reshape(-1)])\\n+ result_rank.append(np.tile(np.arange(1, 101, dtype=np.int64), bsz))\\n+ if stop % 2048 < batch:\\n+ print(f\\\"ranked {stop:,}/{len(pids):,}\\\", flush=True)\\n+ ranking = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.concatenate(result_uid).astype(np.int64),\\n+ \\\"item_id\\\": np.concatenate(result_item).astype(np.int64),\\n+ \\\"rank\\\": np.concatenate(result_rank).astype(np.int64),\\n+ }\\n+ )\\n+ ranking.to_parquet(output, index=False)\\n+ print(f\\\"saved {output} rows={len(ranking):,} alpha={alpha}\\\", flush=True)\\n+\\n+\\n+def parse_args() -> argparse.Namespace:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], default=\\\"validation\\\")\\n+ parser.add_argument(\\\"--mode\\\", choices=[\\\"prepare\\\", \\\"train\\\", \\\"rank\\\", \\\"all\\\"], default=\\\"all\\\")\\n+ parser.add_argument(\\\"--windows\\\", type=int, default=28)\\n+ parser.add_argument(\\\"--force-cache\\\", action=\\\"store_true\\\")\\n+ parser.add_argument(\\\"--name\\\", default=\\\"two_tower_validation\\\")\\n+ parser.add_argument(\\\"--checkpoint\\\", type=Path)\\n+ parser.add_argument(\\\"--output\\\", type=Path)\\n+ parser.add_argument(\\\"--steps\\\", type=int, default=3000)\\n+ parser.add_argument(\\\"--batch-size\\\", type=int, default=256)\\n+ parser.add_argument(\\\"--negatives\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--like-fraction\\\", type=float, default=0.28)\\n+ parser.add_argument(\\\"--lr\\\", type=float, default=0.002)\\n+ parser.add_argument(\\\"--weight-decay\\\", type=float, default=2e-5)\\n+ parser.add_argument(\\\"--window-half-life\\\", type=float, default=16.0)\\n+ parser.add_argument(\\\"--dim\\\", type=int, default=64)\\n+ parser.add_argument(\\\"--hist-dim\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--id-dim\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--semantic-scale\\\", type=float, default=10.0)\\n+ parser.add_argument(\\\"--train-alpha\\\", type=float, default=1.0)\\n+ parser.add_argument(\\\"--alpha\\\", type=float, default=1.0)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ parser.add_argument(\\\"--threads\\\", type=int, default=8)\\n+ parser.add_argument(\\\"--log-every\\\", type=int, default=100)\\n+ parser.add_argument(\\\"--save-every\\\", type=int, default=1000)\\n+ return parser.parse_args()\\n+\\n+\\n+def main() -> None:\\n+ args = parse_args()\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ print(json.dumps(vars(args), default=str, indent=2), flush=True)\\n+ cache = build_cache(args.stage, args.windows, args.force_cache)\\n+ if args.mode == \\\"prepare\\\":\\n+ return\\n+ checkpoint = args.checkpoint\\n+ if args.mode in (\\\"train\\\", \\\"all\\\"):\\n+ checkpoint = train_model(cache, args)\\n+ if args.mode in (\\\"rank\\\", \\\"all\\\"):\\n+ if checkpoint is None:\\n+ raise SystemExit(\\\"--checkpoint is required in rank mode\\\")\\n+ output = args.output or OUT / (\\n+ \\\"validation.parquet\\\" if args.stage == \\\"validation\\\" else \\\"submission.parquet\\\"\\n+ )\\n+ rank_model(cache, checkpoint, output, args.alpha, args.threads)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:34:13.863Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:13.834Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a49686ba-3b23-4255-b9aa-555321501379", "changes": { "/app/solution/train_and_rank.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a CPU PyTorch two-tower discovery ranker.\n\nThe implementation intentionally operates on event streams and sampled\npositive/negative pairs. It never materializes or factorizes a dense global\nuser-item interaction matrix. Candidate vectors are computed independently\nof users and the final score is exactly a dot product.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\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_END = 24_786_800\nWEEK = 7 * 24 * 60 * 60\nGAP = 30 * 60\nVAL_END = TRAIN_END + GAP + WEEK\n\nRECENT_LEN = 64\nORGANIC_LEN = 64\nLIKE_LEN = 96\nDISLIKE_LEN = 32\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 read_events(family: str, stage: str, columns: list[str]) -> pd.DataFrame:\n frames = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n if stage == \"final\":\n frames.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n frame = pd.concat(frames, ignore_index=True)\n return frame\n\n\ndef map_catalog(raw: np.ndarray, sorted_ids: np.ndarray, sorted_to_catalog: np.ndarray):\n loc = np.searchsorted(sorted_ids, raw)\n valid = loc < len(sorted_ids)\n safe = np.minimum(loc, len(sorted_ids) - 1)\n valid &= sorted_ids[safe] == raw\n return valid, sorted_to_catalog[safe]\n\n\ndef sorted_stream(frame: pd.DataFrame, final_stage: bool) -> pd.DataFrame:\n if final_stage or not frame.uid.is_monotonic_increasing:\n frame = frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\")\n return frame.reset_index(drop=True)\n\n\ndef user_bounds(stream_uids: np.ndarray, users: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n return (\n np.searchsorted(stream_uids, users, side=\"left\"),\n np.searchsorted(stream_uids, users, side=\"right\"),\n )\n\n\ndef aggregate_aligned_weeks(\n timestamps: np.ndarray,\n item_idx: np.ndarray,\n first_cutoff: int,\n n_windows: int,\n n_items: int,\n lookback: int = 13,\n) -> tuple[np.ndarray, int]:\n \"\"\"Aggregate events into weeks aligned to the rolling cutoffs.\"\"\"\n origin = first_cutoff - lookback * WEEK\n bucket = np.floor_divide(timestamps.astype(np.int64) - origin, WEEK)\n rows = lookback + n_windows\n valid = (bucket >= 0) & (bucket < rows)\n flat = bucket[valid] * n_items + item_idx[valid].astype(np.int64)\n counts = np.bincount(flat, minlength=rows * n_items).reshape(rows, n_items)\n return counts.astype(np.float32), lookback\n\n\ndef window_sum(counts: np.ndarray, edge: int, weeks: int) -> np.ndarray:\n lo = max(0, edge - weeks)\n return counts[lo:edge].sum(axis=0, dtype=np.float32)\n\n\ndef exact_count(\n timestamps: np.ndarray,\n item_idx: np.ndarray,\n cutoff: int,\n seconds: int,\n n_items: int,\n) -> np.ndarray:\n mask = (timestamps < cutoff) & (timestamps >= cutoff - seconds)\n return np.bincount(item_idx[mask], minlength=n_items).astype(np.float32)\n\n\ndef rank_trend(one_week: np.ndarray, four_week: np.ndarray, twelve_week: np.ndarray) -> np.ndarray:\n \"\"\"Return a smooth score whose ordering is the desired popularity prior.\"\"\"\n order = np.lexsort((np.arange(len(one_week)), -twelve_week, -four_week, -one_week))\n ranks = np.empty(len(order), dtype=np.int32)\n ranks[order] = np.arange(len(order), dtype=np.int32)\n score = -np.log1p(ranks.astype(np.float32))\n return ((score - score.mean()) / (score.std() + 1e-6)).astype(np.float32)\n\n\ndef make_item_features(\n cutoffs: np.ndarray,\n inference_cutoff: int,\n n_items: int,\n positive_ts: np.ndarray,\n positive_item: np.ndarray,\n organic_ts: np.ndarray,\n organic_item: np.ndarray,\n organic_like_ts: np.ndarray,\n organic_like_item: np.ndarray,\n) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Build cutoff-correct rolling item features plus an explicit trend prior.\"\"\"\n nwin = len(cutoffs)\n streams = []\n for ts, item in [\n (positive_ts, positive_item),\n (organic_ts, organic_item),\n (organic_like_ts, organic_like_item),\n ]:\n streams.append(aggregate_aligned_weeks(ts, item, int(cutoffs[0]), nwin, n_items))\n\n raw = np.zeros((nwin + 1, n_items, 9), dtype=np.float32)\n trend = np.zeros((nwin + 1, n_items), dtype=np.float32)\n for ci in range(nwin):\n edge = streams[0][1] + ci\n vals: list[np.ndarray] = []\n for counts, _ in streams:\n vals.extend([window_sum(counts, edge, w) for w in (1, 4, 12)])\n raw[ci] = np.stack(vals, axis=1)\n trend[ci] = rank_trend(vals[3], vals[4], vals[5])\n\n vals = []\n for ts, item in [\n (positive_ts, positive_item),\n (organic_ts, organic_item),\n (organic_like_ts, organic_like_item),\n ]:\n vals.extend(\n [\n exact_count(ts, item, inference_cutoff, w * WEEK, n_items)\n for w in (1, 4, 12)\n ]\n )\n raw[-1] = np.stack(vals, axis=1)\n trend[-1] = rank_trend(vals[3], vals[4], vals[5])\n\n logged = np.log1p(raw)\n # Normalize per cutoff. Thus a feature has the same interpretation as the\n # catalog grows and traffic volume changes in production.\n mean = logged.mean(axis=1, keepdims=True)\n std = logged.std(axis=1, keepdims=True) + 1e-5\n features = np.clip((logged - mean) / std, -4.0, 8.0).astype(np.float32)\n return features, trend\n\n\ndef build_cache(stage: str, windows: int, force: bool = False) -> Path:\n cache = OUT / f\"cache_{stage}_w{windows}.npz\"\n if cache.exists() and not force:\n print(f\"using existing cache {cache}\", flush=True)\n return cache\n\n t0 = time.time()\n catalog_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n catalog = catalog_frame.item_id.to_numpy(dtype=np.int64)\n n_items = len(catalog)\n cat_order = np.argsort(catalog)\n sorted_ids = catalog[cat_order]\n\n listen_cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n listens = read_events(\"listens\", stage, listen_cols)\n raw_item = listens.item_id.to_numpy(dtype=np.int64, copy=False)\n valid, mapped = map_catalog(raw_item, sorted_ids, cat_order)\n listens = listens.loc[valid].copy()\n listens[\"item_idx\"] = mapped[valid].astype(np.int32)\n listens = sorted_stream(listens, stage == \"final\")\n users = np.sort(listens.uid.unique().astype(np.int64))\n n_users = len(users)\n print(\n f\"catalog listens={len(listens):,}, users={n_users:,}, items={n_items:,} \"\n f\"({time.time()-t0:.1f}s)\",\n flush=True,\n )\n\n action_frames: dict[str, pd.DataFrame] = {}\n for family in (\"likes\", \"unlikes\", \"dislikes\", \"undislikes\"):\n frame = read_events(family, stage, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n raw = frame.item_id.to_numpy(dtype=np.int64, copy=False)\n keep, idx = map_catalog(raw, sorted_ids, cat_order)\n frame = frame.loc[keep].copy()\n frame[\"item_idx\"] = idx[keep].astype(np.int32)\n action_frames[family] = sorted_stream(frame, stage == \"final\")\n\n inference_cutoff = TRAIN_END if stage == \"validation\" else VAL_END\n last_training_cutoff = inference_cutoff - GAP - WEEK\n cutoffs = last_training_cutoff - WEEK * np.arange(windows - 1, -1, -1, dtype=np.int64)\n all_cutoffs = np.concatenate([cutoffs, np.asarray([inference_cutoff], dtype=np.int64)])\n\n pos = listens[listens.played_ratio_pct >= 50]\n org = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n org_likes = action_frames[\"likes\"][action_frames[\"likes\"].is_organic == 1]\n item_features, trend = make_item_features(\n cutoffs,\n inference_cutoff,\n n_items,\n pos.timestamp.to_numpy(dtype=np.int64),\n pos.item_idx.to_numpy(dtype=np.int32),\n org.timestamp.to_numpy(dtype=np.int64),\n org.item_idx.to_numpy(dtype=np.int32),\n org_likes.timestamp.to_numpy(dtype=np.int64),\n org_likes.item_idx.to_numpy(dtype=np.int32),\n )\n print(f\"item features built ({time.time()-t0:.1f}s)\", flush=True)\n\n pad = n_items\n n_profiles = len(all_cutoffs) * n_users\n recent = np.full((n_profiles, RECENT_LEN), pad, dtype=np.int32)\n organic = np.full((n_profiles, ORGANIC_LEN), pad, dtype=np.int32)\n liked = np.full((n_profiles, LIKE_LEN), pad, dtype=np.int32)\n disliked = np.full((n_profiles, DISLIKE_LEN), pad, dtype=np.int32)\n profile_stats = np.zeros((n_profiles, 8), dtype=np.float32)\n\n pos_uid = pos.uid.to_numpy(dtype=np.int64)\n org_uid = org.uid.to_numpy(dtype=np.int64)\n pos_bounds = user_bounds(pos_uid, users)\n org_bounds = user_bounds(org_uid, users)\n pos_ts = pos.timestamp.to_numpy(dtype=np.int64)\n pos_it = pos.item_idx.to_numpy(dtype=np.int32)\n org_ts = org.timestamp.to_numpy(dtype=np.int64)\n org_it = org.item_idx.to_numpy(dtype=np.int32)\n\n def action_stream(on_name: str, off_name: str):\n on = action_frames[on_name][[\"uid\", \"timestamp\", \"item_idx\"]].copy()\n on[\"active\"] = np.int8(1)\n off = action_frames[off_name][[\"uid\", \"timestamp\", \"item_idx\"]].copy()\n off[\"active\"] = np.int8(0)\n stream = pd.concat([on, off], ignore_index=True).sort_values(\n [\"uid\", \"timestamp\"], kind=\"stable\"\n )\n suid = stream.uid.to_numpy(dtype=np.int64)\n return (\n stream.timestamp.to_numpy(dtype=np.int64),\n stream.item_idx.to_numpy(dtype=np.int32),\n stream.active.to_numpy(dtype=np.int8),\n user_bounds(suid, users),\n )\n\n like_ts, like_it, like_active, like_bounds = action_stream(\"likes\", \"unlikes\")\n dislike_ts, dislike_it, dislike_active, dislike_bounds = action_stream(\n \"dislikes\", \"undislikes\"\n )\n\n # Authoritative explicit state is used for the request-time profile.\n pref_name = \"validation_preference_state.parquet\" if stage == \"validation\" else \"test_preference_state.parquet\"\n pref = pd.read_parquet(DATA / pref_name)\n pvalid, pidx = map_catalog(pref.item_id.to_numpy(dtype=np.int64), sorted_ids, cat_order)\n pref = pref.loc[pvalid].copy()\n pref[\"item_idx\"] = pidx[pvalid].astype(np.int32)\n pref_lookup: dict[tuple[int, str], np.ndarray] = {\n (int(uid), str(state)): group.item_idx.to_numpy(dtype=np.int32)\n for (uid, state), group in pref.groupby([\"uid\", \"state\"], sort=False)\n }\n\n for ui, uid in enumerate(users):\n ps, pe = int(pos_bounds[0][ui]), int(pos_bounds[1][ui])\n os, oe = int(org_bounds[0][ui]), int(org_bounds[1][ui])\n l_s, l_e = int(like_bounds[0][ui]), int(like_bounds[1][ui])\n d_s, d_e = int(dislike_bounds[0][ui]), int(dislike_bounds[1][ui])\n lp, dp = l_s, d_s\n like_state: dict[int, int] = {}\n dislike_state: dict[int, int] = {}\n for ci, cutoff in enumerate(all_cutoffs):\n pid = ci * n_users + ui\n pend = ps + int(np.searchsorted(pos_ts[ps:pe], cutoff, side=\"left\"))\n oend = os + int(np.searchsorted(org_ts[os:oe], cutoff, side=\"left\"))\n pitems = pos_it[max(ps, pend - RECENT_LEN) : pend][::-1]\n oitems = org_it[max(os, oend - ORGANIC_LEN) : oend][::-1]\n recent[pid, : len(pitems)] = pitems\n organic[pid, : len(oitems)] = oitems\n\n while lp < l_e and like_ts[lp] < cutoff:\n item = int(like_it[lp])\n if like_active[lp]:\n like_state[item] = int(like_ts[lp])\n else:\n like_state.pop(item, None)\n lp += 1\n while dp < d_e and dislike_ts[dp] < cutoff:\n item = int(dislike_it[dp])\n if dislike_active[dp]:\n dislike_state[item] = int(dislike_ts[dp])\n else:\n dislike_state.pop(item, None)\n dp += 1\n\n lorder = sorted(like_state, key=like_state.get, reverse=True)\n dorder = sorted(dislike_state, key=dislike_state.get, reverse=True)\n if ci == len(all_cutoffs) - 1:\n supplied_l = pref_lookup.get((int(uid), \"liked\"))\n supplied_d = pref_lookup.get((int(uid), \"disliked\"))\n if supplied_l is not None:\n lorder = sorted(\n map(int, supplied_l), key=lambda x: like_state.get(x, -1), reverse=True\n )\n if supplied_d is not None:\n dorder = sorted(\n map(int, supplied_d),\n key=lambda x: dislike_state.get(x, -1),\n reverse=True,\n )\n litems = np.asarray(lorder[:LIKE_LEN], dtype=np.int32)\n ditems = np.asarray(dorder[:DISLIKE_LEN], dtype=np.int32)\n liked[pid, : len(litems)] = litems\n disliked[pid, : len(ditems)] = ditems\n profile_stats[pid] = np.log1p(\n [\n pend - ps,\n oend - os,\n len(like_state),\n len(dislike_state),\n min(RECENT_LEN, pend - ps),\n min(ORGANIC_LEN, oend - os),\n min(LIKE_LEN, len(lorder)),\n min(DISLIKE_LEN, len(dorder)),\n ]\n )\n if (ui + 1) % 2000 == 0:\n print(f\"profiles {ui+1:,}/{n_users:,}\", flush=True)\n\n stat_mean = profile_stats.mean(axis=0, keepdims=True)\n stat_std = profile_stats.std(axis=0, keepdims=True) + 1e-5\n profile_stats = np.clip((profile_stats - stat_mean) / stat_std, -4, 6).astype(np.float32)\n print(f\"profiles built ({time.time()-t0:.1f}s)\", flush=True)\n\n # Rolling labels: an organic 80%-complete listen is grade 1 and an organic\n # like is grade 3. Restrict to items first heard after that cutoff to model\n # discovery/eligibility rather than memorizing repeats.\n first_start = int(cutoffs[0] + GAP)\n target_parts = []\n for frame, grade in ((org, 1), (org_likes, 3)):\n ts = frame.timestamp.to_numpy(dtype=np.int64)\n wi = np.floor_divide(ts - first_start, WEEK)\n keep = (wi >= 0) & (wi < windows)\n part = pd.DataFrame(\n {\n \"uid\": frame.uid.to_numpy(dtype=np.int64)[keep],\n \"item\": frame.item_idx.to_numpy(dtype=np.int32)[keep],\n \"wi\": wi[keep].astype(np.int16),\n \"grade\": np.full(int(keep.sum()), grade, dtype=np.int8),\n }\n )\n target_parts.append(part)\n targets = pd.concat(target_parts, ignore_index=True)\n targets = targets.groupby([\"uid\", \"item\", \"wi\"], as_index=False, sort=False).grade.max()\n\n first_heard = (\n listens.groupby([\"uid\", \"item_idx\"], sort=False, observed=True)\n .timestamp.min()\n .rename(\"first_ts\")\n .reset_index()\n .rename(columns={\"item_idx\": \"item\"})\n )\n targets = targets.merge(first_heard, on=[\"uid\", \"item\"], how=\"left\")\n target_cutoff = cutoffs[targets.wi.to_numpy(dtype=np.int64)]\n targets = targets[targets.first_ts.fillna(np.iinfo(np.int64).max).to_numpy() >= target_cutoff]\n targets[\"ui\"] = np.searchsorted(users, targets.uid.to_numpy(dtype=np.int64)).astype(np.int32)\n targets = targets.sort_values([\"wi\", \"uid\", \"item\"], kind=\"stable\")\n print(\n f\"training pairs={len(targets):,}, grade3={(targets.grade==3).sum():,} \"\n f\"({time.time()-t0:.1f}s)\",\n flush=True,\n )\n\n target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(dtype=np.int64)\n target_ui = np.searchsorted(users, target_users).astype(np.int32)\n if not np.array_equal(users[target_ui], target_users):\n raise RuntimeError(\"target user missing from interaction-user index\")\n\n pair_items = targets.item.to_numpy(dtype=np.int32)\n frequency = np.bincount(pair_items, minlength=n_items).astype(np.float64)\n neg_prob = np.sqrt(frequency + 1.0)\n neg_prob /= neg_prob.sum()\n\n np.savez(\n cache,\n users=users,\n catalog=catalog,\n cutoffs=cutoffs,\n inference_cutoff=np.asarray(inference_cutoff, dtype=np.int64),\n recent=recent,\n organic=organic,\n liked=liked,\n disliked=disliked,\n profile_stats=profile_stats,\n item_features=item_features,\n trend=trend,\n ex_ci=targets.wi.to_numpy(dtype=np.int16),\n ex_ui=targets.ui.to_numpy(dtype=np.int32),\n ex_item=pair_items,\n ex_grade=targets.grade.to_numpy(dtype=np.int8),\n target_users=target_users,\n target_ui=target_ui,\n neg_prob=neg_prob.astype(np.float32),\n )\n print(f\"saved {cache} ({cache.stat().st_size/1e6:.1f} MB, {time.time()-t0:.1f}s)\", flush=True)\n return cache\n\n\nclass UserTower(nn.Module):\n \"\"\"History-only user tower; deliberately contains no user-ID embedding.\"\"\"\n\n def __init__(self, n_items: int, hist_dim: int, out_dim: int, stats_dim: int):\n super().__init__()\n self.n_items = n_items\n self.embedding = nn.Embedding(n_items + 1, hist_dim, padding_idx=n_items)\n self.network = nn.Sequential(\n nn.Linear(4 * hist_dim + stats_dim, 192),\n nn.LayerNorm(192),\n nn.GELU(),\n nn.Linear(192, 128),\n nn.GELU(),\n nn.Linear(128, out_dim),\n )\n self.register_buffer(\"recent_weight\", torch.exp(-torch.arange(RECENT_LEN) / 28.0))\n self.register_buffer(\"organic_weight\", torch.exp(-torch.arange(ORGANIC_LEN) / 36.0))\n self.register_buffer(\"like_weight\", torch.exp(-torch.arange(LIKE_LEN) / 72.0))\n self.register_buffer(\"dislike_weight\", torch.exp(-torch.arange(DISLIKE_LEN) / 24.0))\n\n def pool(self, ids: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\n mask = ids.ne(self.n_items)\n weight = weights[: ids.shape[1]].to(dtype=torch.float32).unsqueeze(0) * mask\n emb = self.embedding(ids)\n return (emb * weight.unsqueeze(-1)).sum(1) / weight.sum(1, keepdim=True).clamp_min(1.0)\n\n def forward(\n self,\n recent: torch.Tensor,\n organic: torch.Tensor,\n liked: torch.Tensor,\n disliked: torch.Tensor,\n stats: torch.Tensor,\n semantic_scale: float,\n ) -> torch.Tensor:\n pooled = torch.cat(\n [\n self.pool(recent, self.recent_weight),\n self.pool(organic, self.organic_weight),\n self.pool(liked, self.like_weight),\n self.pool(disliked, self.dislike_weight),\n stats,\n ],\n dim=1,\n )\n semantic = F.normalize(self.network(pooled), dim=1) * semantic_scale\n # The final constant coordinate turns the item's trend coordinate into\n # a legal dot-product bias while preserving independent precomputation.\n return torch.cat([semantic, torch.ones_like(semantic[:, :1])], dim=1)\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate tower using item identity and cutoff-correct offline features.\"\"\"\n\n def __init__(self, n_items: int, id_dim: int, feature_dim: int, out_dim: int):\n super().__init__()\n self.embedding = nn.Embedding(n_items, id_dim)\n self.feature_network = nn.Sequential(\n nn.Linear(feature_dim, 48), nn.GELU(), nn.Linear(48, 48), nn.GELU()\n )\n self.network = nn.Sequential(\n nn.Linear(id_dim + 48, 160),\n nn.LayerNorm(160),\n nn.GELU(),\n nn.Linear(160, out_dim),\n )\n\n def forward(\n self, ids: torch.Tensor, features: torch.Tensor, trend: torch.Tensor, alpha: float\n ) -> torch.Tensor:\n shape = ids.shape\n flat_ids = ids.reshape(-1)\n flat_features = features.reshape(-1, features.shape[-1])\n semantic = self.network(\n torch.cat([self.embedding(flat_ids), self.feature_network(flat_features)], dim=1)\n )\n semantic = F.normalize(semantic, dim=1).reshape(*shape, -1)\n bias = (trend.reshape(*shape, 1) * alpha).to(semantic.dtype)\n return torch.cat([semantic, bias], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(\n self,\n n_items: int,\n feature_dim: int,\n stats_dim: int,\n hist_dim: int = 48,\n id_dim: int = 48,\n semantic_dim: int = 64,\n semantic_scale: float = 10.0,\n ):\n super().__init__()\n self.user_tower = UserTower(n_items, hist_dim, semantic_dim, stats_dim)\n self.item_tower = ItemTower(n_items, id_dim, feature_dim, semantic_dim)\n self.semantic_scale = semantic_scale\n\n def encode_user(self, recent, organic, liked, disliked, stats):\n return self.user_tower(\n recent, organic, liked, disliked, stats, self.semantic_scale\n )\n\n def encode_item(self, ids, features, trend, alpha: float):\n return self.item_tower(ids, features, trend, alpha)\n\n\ndef load_cache(path: Path) -> dict[str, np.ndarray]:\n with np.load(path, allow_pickle=False) as artifact:\n return {name: artifact[name].copy() for name in artifact.files}\n\n\ndef cache_tensors(cache: dict[str, np.ndarray]) -> dict[str, torch.Tensor]:\n names = [\"recent\", \"organic\", \"liked\", \"disliked\", \"profile_stats\", \"item_features\", \"trend\"]\n return {name: torch.from_numpy(cache[name]) for name in names}\n\n\ndef model_config(cache: dict[str, np.ndarray], args: argparse.Namespace) -> dict[str, int | float]:\n return {\n \"n_items\": int(len(cache[\"catalog\"])),\n \"feature_dim\": int(cache[\"item_features\"].shape[-1]),\n \"stats_dim\": int(cache[\"profile_stats\"].shape[-1]),\n \"hist_dim\": int(args.hist_dim),\n \"id_dim\": int(args.id_dim),\n \"semantic_dim\": int(args.dim),\n \"semantic_scale\": float(args.semantic_scale),\n }\n\n\ndef train_model(cache_path: Path, args: argparse.Namespace) -> Path:\n seed_everything(args.seed)\n torch.set_num_threads(args.threads)\n cache = load_cache(cache_path)\n tensors = cache_tensors(cache)\n config = model_config(cache, args)\n model = TwoTowerRanker(**config)\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)\n\n ex_ci = cache[\"ex_ci\"].astype(np.int64)\n ex_ui = cache[\"ex_ui\"].astype(np.int64)\n ex_item = cache[\"ex_item\"].astype(np.int64)\n ex_grade = cache[\"ex_grade\"].astype(np.int64)\n nwin = len(cache[\"cutoffs\"])\n nusers = len(cache[\"users\"])\n by_window = [np.flatnonzero(ex_ci == ci) for ci in range(nwin)]\n available = np.asarray([len(x) > 0 for x in by_window])\n recency = np.exp((np.arange(nwin) - nwin + 1) / args.window_half_life * math.log(2.0))\n window_prob = np.sqrt(np.asarray([max(1, len(x)) for x in by_window])) * recency * available\n window_prob /= window_prob.sum()\n g1 = [idx[ex_grade[idx] == 1] for idx in by_window]\n g3 = [idx[ex_grade[idx] == 3] for idx in by_window]\n rng = np.random.default_rng(args.seed)\n all_positive_items = ex_item\n\n ckpt_path = OUT / f\"{args.name}.pt\"\n model.train()\n started = time.time()\n smooth = None\n for step in range(1, args.steps + 1):\n ci = int(rng.choice(nwin, p=window_prob))\n n3 = int(round(args.batch_size * args.like_fraction)) if len(g3[ci]) else 0\n n1 = args.batch_size - n3\n chosen = np.concatenate(\n [\n rng.choice(g1[ci] if len(g1[ci]) else by_window[ci], n1, replace=True),\n rng.choice(g3[ci], n3, replace=True) if n3 else np.empty(0, dtype=np.int64),\n ]\n )\n rng.shuffle(chosen)\n ui = ex_ui[chosen]\n positive = ex_item[chosen]\n pids = ci * nusers + ui\n\n half = args.negatives // 2\n negative = np.empty((args.batch_size, args.negatives), dtype=np.int64)\n negative[:, :half] = rng.integers(0, config[\"n_items\"], size=(args.batch_size, half))\n pick = rng.integers(0, len(all_positive_items), size=(args.batch_size, args.negatives - half))\n negative[:, half:] = all_positive_items[pick]\n collision = negative == positive[:, None]\n if collision.any():\n negative[collision] = rng.integers(0, config[\"n_items\"], size=int(collision.sum()))\n candidate = np.concatenate([positive[:, None], negative], axis=1)\n\n pid_t = torch.from_numpy(pids)\n cand_t = torch.from_numpy(candidate)\n user_vec = model.encode_user(\n tensors[\"recent\"][pid_t],\n tensors[\"organic\"][pid_t],\n tensors[\"liked\"][pid_t],\n tensors[\"disliked\"][pid_t],\n tensors[\"profile_stats\"][pid_t],\n )\n item_vec = model.encode_item(\n cand_t,\n tensors[\"item_features\"][ci, cand_t],\n tensors[\"trend\"][ci, cand_t],\n args.train_alpha,\n )\n logits = torch.einsum(\"bd,bnd->bn\", user_vec, item_vec)\n loss = F.cross_entropy(logits, torch.zeros(args.batch_size, dtype=torch.long))\n\n optimizer.zero_grad(set_to_none=True)\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n optimizer.step()\n value = float(loss.detach())\n smooth = value if smooth is None else 0.98 * smooth + 0.02 * value\n if step == 1 or step % args.log_every == 0:\n rate = step * args.batch_size / max(1e-6, time.time() - started)\n print(\n f\"step={step}/{args.steps} loss={value:.4f} ema={smooth:.4f} \"\n f\"pairs/s={rate:,.0f} window={ci}\",\n flush=True,\n )\n if step % args.save_every == 0 or step == args.steps:\n payload = {\n \"model\": model.state_dict(),\n \"config\": config,\n \"stage\": args.stage,\n \"windows\": args.windows,\n \"step\": step,\n \"train_alpha\": args.train_alpha,\n \"seed\": args.seed,\n }\n torch.save(payload, ckpt_path)\n if step != args.steps:\n torch.save(payload, OUT / f\"{args.name}_step{step}.pt\")\n print(f\"saved model {ckpt_path} ({time.time()-started:.1f}s)\", flush=True)\n return ckpt_path\n\n\ndef load_model(checkpoint: Path) -> tuple[TwoTowerRanker, dict]:\n payload = torch.load(checkpoint, map_location=\"cpu\", weights_only=False)\n model = TwoTowerRanker(**payload[\"config\"])\n model.load_state_dict(payload[\"model\"])\n model.eval()\n return model, payload\n\n\ndef rank_model(\n cache_path: Path,\n checkpoint: Path,\n output: Path,\n alpha: float,\n threads: int,\n) -> None:\n torch.set_num_threads(threads)\n cache = load_cache(cache_path)\n tensors = cache_tensors(cache)\n model, _ = load_model(checkpoint)\n nusers = len(cache[\"users\"])\n inference_ci = len(cache[\"cutoffs\"])\n target_ui = cache[\"target_ui\"].astype(np.int64)\n pids = inference_ci * nusers + target_ui\n item_ids = torch.arange(len(cache[\"catalog\"]), dtype=torch.long)\n with torch.inference_mode():\n item_vec = model.encode_item(\n item_ids,\n tensors[\"item_features\"][inference_ci],\n tensors[\"trend\"][inference_ci],\n alpha,\n )\n\n eligibility_name = (\n \"validation_eligible_candidates.npz\"\n if \"validation\" in cache_path.name\n else \"test_eligible_candidates.npz\"\n )\n with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\n eligible_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n eligible_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n if not np.array_equal(eligible_uids, cache[\"target_users\"]):\n raise RuntimeError(\"eligibility and target-user order differ\")\n if not np.array_equal(eligible_items, cache[\"catalog\"]):\n raise RuntimeError(\"eligibility and catalog order differ\")\n\n result_uid: list[np.ndarray] = []\n result_item: list[np.ndarray] = []\n result_rank: list[np.ndarray] = []\n batch = 256\n with torch.inference_mode():\n for start in range(0, len(pids), batch):\n stop = min(start + batch, len(pids))\n pid_t = torch.from_numpy(pids[start:stop])\n user_vec = model.encode_user(\n tensors[\"recent\"][pid_t],\n tensors[\"organic\"][pid_t],\n tensors[\"liked\"][pid_t],\n tensors[\"disliked\"][pid_t],\n tensors[\"profile_stats\"][pid_t],\n )\n scores = user_vec @ item_vec.T\n mask = np.unpackbits(\n packed[start:stop], axis=1, count=len(cache[\"catalog\"]), bitorder=\"little\"\n ).astype(bool)\n scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\n top = torch.topk(scores, k=100, dim=1, largest=True, sorted=True).indices.numpy()\n bsz = stop - start\n result_uid.append(np.repeat(cache[\"target_users\"][start:stop], 100))\n result_item.append(cache[\"catalog\"][top.reshape(-1)])\n result_rank.append(np.tile(np.arange(1, 101, dtype=np.int64), bsz))\n if stop % 2048 < batch:\n print(f\"ranked {stop:,}/{len(pids):,}\", flush=True)\n ranking = pd.DataFrame(\n {\n \"uid\": np.concatenate(result_uid).astype(np.int64),\n \"item_id\": np.concatenate(result_item).astype(np.int64),\n \"rank\": np.concatenate(result_rank).astype(np.int64),\n }\n )\n ranking.to_parquet(output, index=False)\n print(f\"saved {output} rows={len(ranking):,} alpha={alpha}\", flush=True)\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"validation\")\n parser.add_argument(\"--mode\", choices=[\"prepare\", \"train\", \"rank\", \"all\"], default=\"all\")\n parser.add_argument(\"--windows\", type=int, default=28)\n parser.add_argument(\"--force-cache\", action=\"store_true\")\n parser.add_argument(\"--name\", default=\"two_tower_validation\")\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n parser.add_argument(\"--steps\", type=int, default=3000)\n parser.add_argument(\"--batch-size\", type=int, default=256)\n parser.add_argument(\"--negatives\", type=int, default=48)\n parser.add_argument(\"--like-fraction\", type=float, default=0.28)\n parser.add_argument(\"--lr\", type=float, default=0.002)\n parser.add_argument(\"--weight-decay\", type=float, default=2e-5)\n parser.add_argument(\"--window-half-life\", type=float, default=16.0)\n parser.add_argument(\"--dim\", type=int, default=64)\n parser.add_argument(\"--hist-dim\", type=int, default=48)\n parser.add_argument(\"--id-dim\", type=int, default=48)\n parser.add_argument(\"--semantic-scale\", type=float, default=10.0)\n parser.add_argument(\"--train-alpha\", type=float, default=1.0)\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--log-every\", type=int, default=100)\n parser.add_argument(\"--save-every\", type=int, default=1000)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n OUT.mkdir(parents=True, exist_ok=True)\n print(json.dumps(vars(args), default=str, indent=2), flush=True)\n cache = build_cache(args.stage, args.windows, args.force_cache)\n if args.mode == \"prepare\":\n return\n checkpoint = args.checkpoint\n if args.mode in (\"train\", \"all\"):\n checkpoint = train_model(cache, args)\n if args.mode in (\"rank\", \"all\"):\n if checkpoint is None:\n raise SystemExit(\"--checkpoint is required in rank mode\")\n output = args.output or OUT / (\n \"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\"\n )\n rank_model(cache, checkpoint, output, args.alpha, args.threads)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:34:13.862Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cea5054087d2bbb30b3aa32b3551", "usage": { "input_tokens": 54644, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 13275, "reasoning_output_tokens": 3167, "total_tokens": 67919 }, "turn_token_usage": { "input_tokens": 1009213, "cached_input_tokens": 960128, "cache_write_input_tokens": 0, "output_tokens": 35559, "reasoning_output_tokens": 16989, "total_tokens": 1044772 }, "thread_token_usage": { "input_tokens": 1009213, "cached_input_tokens": 960128, "cache_write_input_tokens": 0, "output_tokens": 35559, "reasoning_output_tokens": 16989, "total_tokens": 1044772 } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-25T01:34:15.582Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:15.582Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:34:16.844Z", "callId": "call_BFVw8dVlV25jCC3LpsuofUTA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:34:17.021Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 32K Sep 25 01:34 /app/solution/train_and_rank.py\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:16.929Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cf96228c87d29c26203e8cf5b9c1", "usage": { "input_tokens": 67943, "cached_input_tokens": 54528, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 18, "total_tokens": 68040 }, "turn_token_usage": { "input_tokens": 1077156, "cached_input_tokens": 1014656, "cache_write_input_tokens": 0, "output_tokens": 35656, "reasoning_output_tokens": 17007, "total_tokens": 1112812 }, "thread_token_usage": { "input_tokens": 1077156, "cached_input_tokens": 1014656, "cache_write_input_tokens": 0, "output_tokens": 35656, "reasoning_output_tokens": 17007, "total_tokens": 1112812 } } }, { "kind": "context", "timestamp": "2026-09-25T01:34:17.010Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b5d230b4-a53e-45e1-8283-40f324964096", "process_id": "54520", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 32K Sep 25 01:34 /app/solution/train_and_rank.py\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 32K Sep 25 01:34 /app/solution/train_and_rank.py\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2220 }, "formatted_output": "-rw-r--r-- 1 agent agent 32K Sep 25 01:34 /app/solution/train_and_rank.py\n" } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-25T01:34:21.712Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:21.712Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:34:21.713Z", "callId": "call_RKXClhEvDzT8HQuF7WUxo5kJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:34:43.620Z", "content": "Script completed\nWall time 21.9 seconds\nOutput:\n\n{\"chunk_id\":\"8c81ad\",\"wall_time_seconds\":21.73780919,\"exit_code\":1,\"original_token_count\":314,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"prepare\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\ncatalog listens=31,249,613, users=9,217, items=30,000 (5.0s)\\nitem features built (6.7s)\\nprofiles 2,000/9,217\\nprofiles 4,000/9,217\\nprofiles 6,000/9,217\\nprofiles 8,000/9,217\\nprofiles built (11.9s)\\ntraining pairs=947,358, grade3=166,697 (17.8s)\\nTraceback (most recent call last):\\n File \\\"/app/solution/train_and_rank.py\\\", line 787, in \\n main()\\n File \\\"/app/solution/train_and_rank.py\\\", line 771, in main\\n cache = build_cache(args.stage, args.windows, args.force_cache)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/train_and_rank.py\\\", line 392, in build_cache\\n raise RuntimeError(\\\"target user missing from interaction-user index\\\")\\nRuntimeError: target user missing from interaction-user index\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:21.787Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cf99347487d2af70615aafcf91b7", "usage": { "input_tokens": 68092, "cached_input_tokens": 67712, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 23, "total_tokens": 68189 }, "turn_token_usage": { "input_tokens": 1145248, "cached_input_tokens": 1082368, "cache_write_input_tokens": 0, "output_tokens": 35753, "reasoning_output_tokens": 17030, "total_tokens": 1181001 }, "thread_token_usage": { "input_tokens": 1145248, "cached_input_tokens": 1082368, "cache_write_input_tokens": 0, "output_tokens": 35753, "reasoning_output_tokens": 17030, "total_tokens": 1181001 } } }, { "kind": "context", "timestamp": "2026-09-25T01:34:43.614Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4dd454dc-4597-4b1c-8551-0404f99ee6ea", "process_id": "21624", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,217, items=30,000 (5.0s)\nitem features built (6.7s)\nprofiles 2,000/9,217\nprofiles 4,000/9,217\nprofiles 6,000/9,217\nprofiles 8,000/9,217\nprofiles built (11.9s)\ntraining pairs=947,358, grade3=166,697 (17.8s)\nTraceback (most recent call last):\n File \"/app/solution/train_and_rank.py\", line 787, in \n main()\n File \"/app/solution/train_and_rank.py\", line 771, in main\n cache = build_cache(args.stage, args.windows, args.force_cache)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_and_rank.py\", line 392, in build_cache\n raise RuntimeError(\"target user missing from interaction-user index\")\nRuntimeError: target user missing from interaction-user index\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,217, items=30,000 (5.0s)\nitem features built (6.7s)\nprofiles 2,000/9,217\nprofiles 4,000/9,217\nprofiles 6,000/9,217\nprofiles 8,000/9,217\nprofiles built (11.9s)\ntraining pairs=947,358, grade3=166,697 (17.8s)\nTraceback (most recent call last):\n File \"/app/solution/train_and_rank.py\", line 787, in \n main()\n File \"/app/solution/train_and_rank.py\", line 771, in main\n cache = build_cache(args.stage, args.windows, args.force_cache)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_and_rank.py\", line 392, in build_cache\n raise RuntimeError(\"target user missing from interaction-user index\")\nRuntimeError: target user missing from interaction-user index\n", "exit_code": 1, "duration": { "secs": 21, "nanos": 737805660 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,217, items=30,000 (5.0s)\nitem features built (6.7s)\nprofiles 2,000/9,217\nprofiles 4,000/9,217\nprofiles 6,000/9,217\nprofiles 8,000/9,217\nprofiles built (11.9s)\ntraining pairs=947,358, grade3=166,697 (17.8s)\nTraceback (most recent call last):\n File \"/app/solution/train_and_rank.py\", line 787, in \n main()\n File \"/app/solution/train_and_rank.py\", line 771, in main\n cache = build_cache(args.stage, args.windows, args.force_cache)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_and_rank.py\", line 392, in build_cache\n raise RuntimeError(\"target user missing from interaction-user index\")\nRuntimeError: target user missing from interaction-user index\n" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T01:34:46.751Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:46.751Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:34:48.034Z", "text": "Preprocessing is fast (about 18 seconds) and produced 947k rolling discovery pairs. It exposed one edge case: a few target listeners have no catalog-mapped playback despite meeting the overall activity threshold. I’m adding them as valid empty-history profiles so coverage remains exact; their ranking will naturally fall back to the learned trend prior." }, { "kind": "tool", "timestamp": "2026-09-25T01:34:51.456Z", "callId": "call_Ll7KPloW63bjQVrC5qwmlyI5", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_rank.py\\n@@\\n- users = np.sort(listens.uid.unique().astype(np.int64))\\n+ target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ # Keep target users even when every one of their historical tracks falls\\n+ # outside the 30k retrieval catalog; those profiles use the trend fallback.\\n+ users = np.union1d(listens.uid.unique().astype(np.int64), target_users)\\n@@\\n- target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n target_ui = np.searchsorted(users, target_users).astype(np.int32)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:34:51.522Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:51.473Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8d46e4bf-8352-43a7-b185-886bf1369092", "changes": { "/app/solution/train_and_rank.py": { "type": "update", "unified_diff": "@@ -191,3 +191,6 @@\n listens = sorted_stream(listens, stage == \"final\")\n- users = np.sort(listens.uid.unique().astype(np.int64))\n+ target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(dtype=np.int64)\n+ # Keep target users even when every one of their historical tracks falls\n+ # outside the 30k retrieval catalog; those profiles use the trend fallback.\n+ users = np.union1d(listens.uid.unique().astype(np.int64), target_users)\n n_users = len(users)\n@@ -388,3 +391,2 @@\n \n- target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(dtype=np.int64)\n target_ui = np.searchsorted(users, target_users).astype(np.int32)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:34:51.521Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cfb3e13c87d2ac81331423cc41c4", "usage": { "input_tokens": 68707, "cached_input_tokens": 67968, "cache_write_input_tokens": 0, "output_tokens": 359, "reasoning_output_tokens": 87, "total_tokens": 69066 }, "turn_token_usage": { "input_tokens": 1213955, "cached_input_tokens": 1150336, "cache_write_input_tokens": 0, "output_tokens": 36112, "reasoning_output_tokens": 17117, "total_tokens": 1250067 }, "thread_token_usage": { "input_tokens": 1213955, "cached_input_tokens": 1150336, "cache_write_input_tokens": 0, "output_tokens": 36112, "reasoning_output_tokens": 17117, "total_tokens": 1250067 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T01:34:53.576Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:53.576Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:34:54.737Z", "callId": "call_WLBI7iIyY87N1pFNEbSbVC8l", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:35:16.360Z", "content": "Script completed\nWall time 21.6 seconds\nOutput:\n\n{\"chunk_id\":\"019730\",\"wall_time_seconds\":21.446507387,\"exit_code\":0,\"original_token_count\":202,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"prepare\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\ncatalog listens=31,249,613, users=9,221, items=30,000 (5.0s)\\nitem features built (6.5s)\\nprofiles 2,000/9,221\\nprofiles 4,000/9,221\\nprofiles 6,000/9,221\\nprofiles 8,000/9,221\\nprofiles built (11.7s)\\ntraining pairs=947,358, grade3=166,697 (17.3s)\\nsaved /app/solution/cache_validation_w28.npz (328.1 MB, 17.4s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:54.843Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cfbbc90087d2b32b4cc4133164b1", "usage": { "input_tokens": 69090, "cached_input_tokens": 68480, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 8, "total_tokens": 69172 }, "turn_token_usage": { "input_tokens": 1283045, "cached_input_tokens": 1218816, "cache_write_input_tokens": 0, "output_tokens": 36194, "reasoning_output_tokens": 17125, "total_tokens": 1319239 }, "thread_token_usage": { "input_tokens": 1283045, "cached_input_tokens": 1218816, "cache_write_input_tokens": 0, "output_tokens": 36194, "reasoning_output_tokens": 17125, "total_tokens": 1319239 } } }, { "kind": "context", "timestamp": "2026-09-25T01:35:16.347Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a945dcb2-9357-4c0e-9917-4acea785267a", "process_id": "54990", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode prepare --windows 28" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,221, items=30,000 (5.0s)\nitem features built (6.5s)\nprofiles 2,000/9,221\nprofiles 4,000/9,221\nprofiles 6,000/9,221\nprofiles 8,000/9,221\nprofiles built (11.7s)\ntraining pairs=947,358, grade3=166,697 (17.3s)\nsaved /app/solution/cache_validation_w28.npz (328.1 MB, 17.4s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,221, items=30,000 (5.0s)\nitem features built (6.5s)\nprofiles 2,000/9,221\nprofiles 4,000/9,221\nprofiles 6,000/9,221\nprofiles 8,000/9,221\nprofiles built (11.7s)\ntraining pairs=947,358, grade3=166,697 (17.3s)\nsaved /app/solution/cache_validation_w28.npz (328.1 MB, 17.4s)\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 446501047 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=31,249,613, users=9,221, items=30,000 (5.0s)\nitem features built (6.5s)\nprofiles 2,000/9,221\nprofiles 4,000/9,221\nprofiles 6,000/9,221\nprofiles 8,000/9,221\nprofiles built (11.7s)\ntraining pairs=947,358, grade3=166,697 (17.3s)\nsaved /app/solution/cache_validation_w28.npz (328.1 MB, 17.4s)\n" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T01:35:18.757Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:35:18.757Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:35:20.843Z", "callId": "call_FWGMlraXP5xpxWOlNtmGgsCC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v1 --steps 3000 --save-every 1000 --log-every 100 --batch-size 256 --negatives 48 --lr 0.002 --train-alpha 1.0\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:35:51.028Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"d9e5b9\",\"wall_time_seconds\":30.002531714,\"session_id\":44659,\"original_token_count\":244,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_v1\\\",\\n \\\"checkpoint\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nstep=1/3000 loss=5.3897 ema=5.3897 pairs/s=536 window=7\\nstep=100/3000 loss=3.8330 ema=4.0146 pairs/s=6,001 window=12\\nstep=200/3000 loss=3.7529 ema=3.7624 pairs/s=6,714 window=22\\nstep=300/3000 loss=3.7962 ema=3.7163 pairs/s=6,974 window=21\\nstep=400/3000 loss=3.6945 ema=3.6869 pairs/s=7,126 window=15\\nstep=500/3000 loss=3.6563 ema=3.6885 pairs/s=7,226 window=18\\nstep=600/3000 loss=3.6232 ema=3.6584 pairs/s=7,253 window=26\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:35:20.939Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cfd48cb487d2b2c20ef755a93e0f", "usage": { "input_tokens": 69584, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 162, "reasoning_output_tokens": 40, "total_tokens": 69746 }, "turn_token_usage": { "input_tokens": 1352629, "cached_input_tokens": 1287680, "cache_write_input_tokens": 0, "output_tokens": 36356, "reasoning_output_tokens": 17165, "total_tokens": 1388985 }, "thread_token_usage": { "input_tokens": 1352629, "cached_input_tokens": 1287680, "cache_write_input_tokens": 0, "output_tokens": 36356, "reasoning_output_tokens": 17165, "total_tokens": 1388985 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T01:35:52.639Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:35:52.639Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:35:53.386Z", "callId": "call_k3UuN6jKS7Jfj2hLFgZ5FzqB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":44659,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:36:23.415Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"09056f\",\"wall_time_seconds\":30.00088806,\"session_id\":44659,\"original_token_count\":154,\"output\":\"step=700/3000 loss=3.6067 ema=3.6560 pairs/s=7,286 window=19\\nstep=800/3000 loss=3.6063 ema=3.6458 pairs/s=7,310 window=23\\nstep=900/3000 loss=3.5900 ema=3.6405 pairs/s=7,341 window=21\\nstep=1000/3000 loss=3.5609 ema=3.6236 pairs/s=7,377 window=27\\nstep=1100/3000 loss=3.6358 ema=3.6222 pairs/s=7,398 window=2\\nstep=1200/3000 loss=3.3798 ema=3.6011 pairs/s=7,412 window=27\\nstep=1300/3000 loss=3.6288 ema=3.5886 pairs/s=7,423 window=20\\nstep=1400/3000 loss=3.5413 ema=3.5803 pairs/s=7,438 window=25\\nstep=1500/3000 loss=3.5926 ema=3.5849 pairs/s=7,453 window=3\\nstep=1600/3000 loss=3.5891 ema=3.5766 pairs/s=7,463 window=25\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:35:53.474Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5cff754dc87d28c8fcadef489bf26", "usage": { "input_tokens": 70242, "cached_input_tokens": 69376, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 27, "total_tokens": 70317 }, "turn_token_usage": { "input_tokens": 1422871, "cached_input_tokens": 1357056, "cache_write_input_tokens": 0, "output_tokens": 36431, "reasoning_output_tokens": 17192, "total_tokens": 1459302 }, "thread_token_usage": { "input_tokens": 1422871, "cached_input_tokens": 1357056, "cache_write_input_tokens": 0, "output_tokens": 36431, "reasoning_output_tokens": 17192, "total_tokens": 1459302 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T01:36:26.187Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:26.187Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:36:27.410Z", "text": "Training is proceeding at roughly 7,400 sampled pairs/second. The contrastive loss has fallen from 5.39 to about 3.58, and intermediate checkpoints are being preserved every 1,000 steps so I can compare undertraining versus convergence on the public metric rather than assume the last checkpoint is best." }, { "kind": "tool", "timestamp": "2026-09-25T01:36:28.118Z", "callId": "call_fJshK0yQNVyeyLIjdIaNOIFk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":44659,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:36:58.148Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"05400f\",\"wall_time_seconds\":30.001021175,\"session_id\":44659,\"original_token_count\":155,\"output\":\"step=1700/3000 loss=3.5290 ema=3.5668 pairs/s=7,459 window=24\\nstep=1800/3000 loss=3.4028 ema=3.5628 pairs/s=7,289 window=27\\nstep=1900/3000 loss=3.5822 ema=3.5641 pairs/s=7,304 window=11\\nstep=2000/3000 loss=3.3096 ema=3.5538 pairs/s=7,319 window=27\\nstep=2100/3000 loss=3.5656 ema=3.5551 pairs/s=7,321 window=10\\nstep=2200/3000 loss=3.6030 ema=3.5477 pairs/s=7,335 window=21\\nstep=2300/3000 loss=3.5361 ema=3.5229 pairs/s=7,346 window=1\\nstep=2400/3000 loss=3.6008 ema=3.5273 pairs/s=7,357 window=0\\nstep=2500/3000 loss=3.4731 ema=3.5249 pairs/s=7,365 window=13\\nstep=2600/3000 loss=3.4580 ema=3.5027 pairs/s=7,368 window=19\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:28.195Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d018443487d287d6822ff7f209f9", "usage": { "input_tokens": 70660, "cached_input_tokens": 70016, "cache_write_input_tokens": 0, "output_tokens": 131, "reasoning_output_tokens": 11, "total_tokens": 70791 }, "turn_token_usage": { "input_tokens": 1493531, "cached_input_tokens": 1427072, "cache_write_input_tokens": 0, "output_tokens": 36562, "reasoning_output_tokens": 17203, "total_tokens": 1530093 }, "thread_token_usage": { "input_tokens": 1493531, "cached_input_tokens": 1427072, "cache_write_input_tokens": 0, "output_tokens": 36562, "reasoning_output_tokens": 17203, "total_tokens": 1530093 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:36:59.855Z", "callId": "call_vgH5ZhI9cX76Wt49FtL93Mav", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":44659,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:37:12.027Z", "content": "Script completed\nWall time 12.1 seconds\nOutput:\n\n{\"chunk_id\":\"71d73f\",\"wall_time_seconds\":12.118721737,\"exit_code\":0,\"original_token_count\":75,\"output\":\"step=2700/3000 loss=3.4179 ema=3.4916 pairs/s=7,373 window=27\\nstep=2800/3000 loss=3.3689 ema=3.4941 pairs/s=7,380 window=27\\nstep=2900/3000 loss=3.4926 ema=3.4958 pairs/s=7,384 window=20\\nstep=3000/3000 loss=3.5873 ema=3.4883 pairs/s=7,389 window=25\\nsaved model /app/solution/two_tower_v1.pt (104.0s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:59.974Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d03a61a887d2a4b5bc801e23abc6", "usage": { "input_tokens": 71137, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 71183 }, "turn_token_usage": { "input_tokens": 1564668, "cached_input_tokens": 1497600, "cache_write_input_tokens": 0, "output_tokens": 36608, "reasoning_output_tokens": 17203, "total_tokens": 1601276 }, "thread_token_usage": { "input_tokens": 1564668, "cached_input_tokens": 1497600, "cache_write_input_tokens": 0, "output_tokens": 36608, "reasoning_output_tokens": 17203, "total_tokens": 1601276 } } }, { "kind": "context", "timestamp": "2026-09-25T01:37:12.025Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e0652fcc-feff-4a92-9049-00e470450ce6", "process_id": "44659", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v1 --steps 3000 --save-every 1000 --log-every 100 --batch-size 256 --negatives 48 --lr 0.002 --train-alpha 1.0" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v1 --steps 3000 --save-every 1000 --log-every 100 --batch-size 256 --negatives 48 --lr 0.002 --train-alpha 1.0" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v1\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/3000 loss=5.3897 ema=5.3897 pairs/s=536 window=7\nstep=100/3000 loss=3.8330 ema=4.0146 pairs/s=6,001 window=12\nstep=200/3000 loss=3.7529 ema=3.7624 pairs/s=6,714 window=22\nstep=300/3000 loss=3.7962 ema=3.7163 pairs/s=6,974 window=21\nstep=400/3000 loss=3.6945 ema=3.6869 pairs/s=7,126 window=15\nstep=500/3000 loss=3.6563 ema=3.6885 pairs/s=7,226 window=18\nstep=600/3000 loss=3.6232 ema=3.6584 pairs/s=7,253 window=26\nstep=700/3000 loss=3.6067 ema=3.6560 pairs/s=7,286 window=19\nstep=800/3000 loss=3.6063 ema=3.6458 pairs/s=7,310 window=23\nstep=900/3000 loss=3.5900 ema=3.6405 pairs/s=7,341 window=21\nstep=1000/3000 loss=3.5609 ema=3.6236 pairs/s=7,377 window=27\nstep=1100/3000 loss=3.6358 ema=3.6222 pairs/s=7,398 window=2\nstep=1200/3000 loss=3.3798 ema=3.6011 pairs/s=7,412 window=27\nstep=1300/3000 loss=3.6288 ema=3.5886 pairs/s=7,423 window=20\nstep=1400/3000 loss=3.5413 ema=3.5803 pairs/s=7,438 window=25\nstep=1500/3000 loss=3.5926 ema=3.5849 pairs/s=7,453 window=3\nstep=1600/3000 loss=3.5891 ema=3.5766 pairs/s=7,463 window=25\nstep=1700/3000 loss=3.5290 ema=3.5668 pairs/s=7,459 window=24\nstep=1800/3000 loss=3.4028 ema=3.5628 pairs/s=7,289 window=27\nstep=1900/3000 loss=3.5822 ema=3.5641 pairs/s=7,304 window=11\nstep=2000/3000 loss=3.3096 ema=3.5538 pairs/s=7,319 window=27\nstep=2100/3000 loss=3.5656 ema=3.5551 pairs/s=7,321 window=10\nstep=2200/3000 loss=3.6030 ema=3.5477 pairs/s=7,335 window=21\nstep=2300/3000 loss=3.5361 ema=3.5229 pairs/s=7,346 window=1\nstep=2400/3000 loss=3.6008 ema=3.5273 pairs/s=7,357 window=0\nstep=2500/3000 loss=3.4731 ema=3.5249 pairs/s=7,365 window=13\nstep=2600/3000 loss=3.4580 ema=3.5027 pairs/s=7,368 window=19\nstep=2700/3000 loss=3.4179 ema=3.4916 pairs/s=7,373 window=27\nstep=2800/3000 loss=3.3689 ema=3.4941 pairs/s=7,380 window=27\nstep=2900/3000 loss=3.4926 ema=3.4958 pairs/s=7,384 window=20\nstep=3000/3000 loss=3.5873 ema=3.4883 pairs/s=7,389 window=25\nsaved model /app/solution/two_tower_v1.pt (104.0s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v1\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/3000 loss=5.3897 ema=5.3897 pairs/s=536 window=7\nstep=100/3000 loss=3.8330 ema=4.0146 pairs/s=6,001 window=12\nstep=200/3000 loss=3.7529 ema=3.7624 pairs/s=6,714 window=22\nstep=300/3000 loss=3.7962 ema=3.7163 pairs/s=6,974 window=21\nstep=400/3000 loss=3.6945 ema=3.6869 pairs/s=7,126 window=15\nstep=500/3000 loss=3.6563 ema=3.6885 pairs/s=7,226 window=18\nstep=600/3000 loss=3.6232 ema=3.6584 pairs/s=7,253 window=26\nstep=700/3000 loss=3.6067 ema=3.6560 pairs/s=7,286 window=19\nstep=800/3000 loss=3.6063 ema=3.6458 pairs/s=7,310 window=23\nstep=900/3000 loss=3.5900 ema=3.6405 pairs/s=7,341 window=21\nstep=1000/3000 loss=3.5609 ema=3.6236 pairs/s=7,377 window=27\nstep=1100/3000 loss=3.6358 ema=3.6222 pairs/s=7,398 window=2\nstep=1200/3000 loss=3.3798 ema=3.6011 pairs/s=7,412 window=27\nstep=1300/3000 loss=3.6288 ema=3.5886 pairs/s=7,423 window=20\nstep=1400/3000 loss=3.5413 ema=3.5803 pairs/s=7,438 window=25\nstep=1500/3000 loss=3.5926 ema=3.5849 pairs/s=7,453 window=3\nstep=1600/3000 loss=3.5891 ema=3.5766 pairs/s=7,463 window=25\nstep=1700/3000 loss=3.5290 ema=3.5668 pairs/s=7,459 window=24\nstep=1800/3000 loss=3.4028 ema=3.5628 pairs/s=7,289 window=27\nstep=1900/3000 loss=3.5822 ema=3.5641 pairs/s=7,304 window=11\nstep=2000/3000 loss=3.3096 ema=3.5538 pairs/s=7,319 window=27\nstep=2100/3000 loss=3.5656 ema=3.5551 pairs/s=7,321 window=10\nstep=2200/3000 loss=3.6030 ema=3.5477 pairs/s=7,335 window=21\nstep=2300/3000 loss=3.5361 ema=3.5229 pairs/s=7,346 window=1\nstep=2400/3000 loss=3.6008 ema=3.5273 pairs/s=7,357 window=0\nstep=2500/3000 loss=3.4731 ema=3.5249 pairs/s=7,365 window=13\nstep=2600/3000 loss=3.4580 ema=3.5027 pairs/s=7,368 window=19\nstep=2700/3000 loss=3.4179 ema=3.4916 pairs/s=7,373 window=27\nstep=2800/3000 loss=3.3689 ema=3.4941 pairs/s=7,380 window=27\nstep=2900/3000 loss=3.4926 ema=3.4958 pairs/s=7,384 window=20\nstep=3000/3000 loss=3.5873 ema=3.4883 pairs/s=7,389 window=25\nsaved model /app/solution/two_tower_v1.pt (104.0s)\n", "exit_code": 0, "duration": { "secs": 111, "nanos": 5557426 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v1\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/3000 loss=5.3897 ema=5.3897 pairs/s=536 window=7\nstep=100/3000 loss=3.8330 ema=4.0146 pairs/s=6,001 window=12\nstep=200/3000 loss=3.7529 ema=3.7624 pairs/s=6,714 window=22\nstep=300/3000 loss=3.7962 ema=3.7163 pairs/s=6,974 window=21\nstep=400/3000 loss=3.6945 ema=3.6869 pairs/s=7,126 window=15\nstep=500/3000 loss=3.6563 ema=3.6885 pairs/s=7,226 window=18\nstep=600/3000 loss=3.6232 ema=3.6584 pairs/s=7,253 window=26\nstep=700/3000 loss=3.6067 ema=3.6560 pairs/s=7,286 window=19\nstep=800/3000 loss=3.6063 ema=3.6458 pairs/s=7,310 window=23\nstep=900/3000 loss=3.5900 ema=3.6405 pairs/s=7,341 window=21\nstep=1000/3000 loss=3.5609 ema=3.6236 pairs/s=7,377 window=27\nstep=1100/3000 loss=3.6358 ema=3.6222 pairs/s=7,398 window=2\nstep=1200/3000 loss=3.3798 ema=3.6011 pairs/s=7,412 window=27\nstep=1300/3000 loss=3.6288 ema=3.5886 pairs/s=7,423 window=20\nstep=1400/3000 loss=3.5413 ema=3.5803 pairs/s=7,438 window=25\nstep=1500/3000 loss=3.5926 ema=3.5849 pairs/s=7,453 window=3\nstep=1600/3000 loss=3.5891 ema=3.5766 pairs/s=7,463 window=25\nstep=1700/3000 loss=3.5290 ema=3.5668 pairs/s=7,459 window=24\nstep=1800/3000 loss=3.4028 ema=3.5628 pairs/s=7,289 window=27\nstep=1900/3000 loss=3.5822 ema=3.5641 pairs/s=7,304 window=11\nstep=2000/3000 loss=3.3096 ema=3.5538 pairs/s=7,319 window=27\nstep=2100/3000 loss=3.5656 ema=3.5551 pairs/s=7,321 window=10\nstep=2200/3000 loss=3.6030 ema=3.5477 pairs/s=7,335 window=21\nstep=2300/3000 loss=3.5361 ema=3.5229 pairs/s=7,346 window=1\nstep=2400/3000 loss=3.6008 ema=3.5273 pairs/s=7,357 window=0\nstep=2500/3000 loss=3.4731 ema=3.5249 pairs/s=7,365 window=13\nstep=2600/3000 loss=3.4580 ema=3.5027 pairs/s=7,368 window=19\nstep=2700/3000 loss=3.4179 ema=3.4916 pairs/s=7,373 window=27\nstep=2800/3000 loss=3.3689 ema=3.4941 pairs/s=7,380 window=27\nstep=2900/3000 loss=3.4926 ema=3.4958 pairs/s=7,384 window=20\nstep=3000/3000 loss=3.5873 ema=3.4883 pairs/s=7,389 window=25\nsaved model /app/solution/two_tower_v1.pt (104.0s)\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T01:37:14.372Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:37:14.372Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:37:17.950Z", "callId": "call_ulpqkV147G9EdOwfwouimUwf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for spec in '1000:/app/solution/two_tower_v1_step1000.pt' '2000:/app/solution/two_tower_v1_step2000.pt' '3000:/app/solution/two_tower_v1.pt'; do tag=${spec%%:*}; ck=${spec#*:}; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \\\"$ck\\\" --alpha 1.0 --output \\\"/app/solution/v1_s${tag}_a1.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/v1_s${tag}_a1.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:37:44.087Z", "content": "Script completed\nWall time 26.1 seconds\nOutput:\n\n{\"chunk_id\":\"2975fd\",\"wall_time_seconds\":25.941089813,\"exit_code\":0,\"original_token_count\":669,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"rank\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": \\\"/app/solution/two_tower_v1_step1000.pt\\\",\\n \\\"output\\\": \\\"/app/solution/v1_s1000_a1.parquet\\\",\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nranked 2,048/8,456\\nranked 4,096/8,456\\nranked 6,144/8,456\\nranked 8,192/8,456\\nsaved /app/solution/v1_s1000_a1.parquet rows=845,600 alpha=1.0\\n{\\n \\\"model\\\": \\\"/app/solution/v1_s1000_a1.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.016858,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"rank\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": \\\"/app/solution/two_tower_v1_step2000.pt\\\",\\n \\\"output\\\": \\\"/app/solution/v1_s2000_a1.parquet\\\",\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nranked 2,048/8,456\\nranked 4,096/8,456\\nranked 6,144/8,456\\nranked 8,192/8,456\\nsaved /app/solution/v1_s2000_a1.parquet rows=845,600 alpha=1.0\\n{\\n \\\"model\\\": \\\"/app/solution/v1_s2000_a1.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.013277,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"rank\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": \\\"/app/solution/two_tower_v1.pt\\\",\\n \\\"output\\\": \\\"/app/solution/v1_s3000_a1.parquet\\\",\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nranked 2,048/8,456\\nranked 4,096/8,456\\nranked 6,144/8,456\\nranked 8,192/8,456\\nsaved /app/solution/v1_s3000_a1.parquet rows=845,600 alpha=1.0\\n{\\n \\\"model\\\": \\\"/app/solution/v1_s3000_a1.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.015345,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:37:18.062Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d04845a887d2a9d810598bc68e4b", "usage": { "input_tokens": 71375, "cached_input_tokens": 70912, "cache_write_input_tokens": 0, "output_tokens": 275, "reasoning_output_tokens": 72, "total_tokens": 71650 }, "turn_token_usage": { "input_tokens": 1636043, "cached_input_tokens": 1568512, "cache_write_input_tokens": 0, "output_tokens": 36883, "reasoning_output_tokens": 17275, "total_tokens": 1672926 }, "thread_token_usage": { "input_tokens": 1636043, "cached_input_tokens": 1568512, "cache_write_input_tokens": 0, "output_tokens": 36883, "reasoning_output_tokens": 17275, "total_tokens": 1672926 } } }, { "kind": "context", "timestamp": "2026-09-25T01:37:44.060Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a144ba38-08fa-4d6a-80a2-08c373a87f37", "process_id": "7127", "command": [ "/bin/sh", "-lc", "for spec in '1000:/app/solution/two_tower_v1_step1000.pt' '2000:/app/solution/two_tower_v1_step2000.pt' '3000:/app/solution/two_tower_v1.pt'; do tag=${spec%%:*}; ck=${spec#*:}; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 1.0 --output \"/app/solution/v1_s${tag}_a1.parquet\"; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s${tag}_a1.parquet\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for spec in '1000:/app/solution/two_tower_v1_step1000.pt' '2000:/app/solution/two_tower_v1_step2000.pt' '3000:/app/solution/two_tower_v1.pt'; do tag=${spec%%:*}; ck=${spec#*:}; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 1.0 --output \"/app/solution/v1_s${tag}_a1.parquet\"; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s${tag}_a1.parquet\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step1000.pt\",\n \"output\": \"/app/solution/v1_s1000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s1000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s1000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016858,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step2000.pt\",\n \"output\": \"/app/solution/v1_s2000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s2000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s2000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013277,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1.pt\",\n \"output\": \"/app/solution/v1_s3000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s3000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s3000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015345,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step1000.pt\",\n \"output\": \"/app/solution/v1_s1000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s1000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s1000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016858,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step2000.pt\",\n \"output\": \"/app/solution/v1_s2000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s2000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s2000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013277,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1.pt\",\n \"output\": \"/app/solution/v1_s3000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s3000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s3000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015345,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 941084033 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step1000.pt\",\n \"output\": \"/app/solution/v1_s1000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s1000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s1000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016858,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1_step2000.pt\",\n \"output\": \"/app/solution/v1_s2000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s2000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s2000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013277,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_v1.pt\",\n \"output\": \"/app/solution/v1_s3000_a1.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/v1_s3000_a1.parquet rows=845,600 alpha=1.0\n{\n \"model\": \"/app/solution/v1_s3000_a1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015345,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-25T01:37:48.445Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:37:48.445Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:37:51.077Z", "text": "The first neural checkpoint is personalized but its default blend underweights the very strong last-week trend signal: NDCG@10 is 0.016858 versus 0.028131 for trend alone. Later checkpoints begin to overfit the sampled objective. I’m retaining the 1,000-step checkpoint and now calibrating the trend coordinate of the exact dot product; this separates model quality from a simple score-scale mismatch." }, { "kind": "tool", "timestamp": "2026-09-25T01:37:54.432Z", "callId": "call_og47St540zMBr6dcbKaPguVU", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0 2 4 8 12 16 24 32; do /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v1_step1000.pt --alpha \\\"$a\\\" --output \\\"/app/solution/v1_s1000_a${a}.parquet\\\" >/tmp/ranklog; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/v1_s1000_a${a}.parquet\\\" | tail -5 | tr '\\\\n' ' '; echo \\\" alpha=$a\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:38:24.611Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"dfa69d\",\"wall_time_seconds\":30.00186465,\"session_id\":32742,\"original_token_count\":66,\"output\":\" \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.000185, \\\"quality_users\\\": 4263 } } alpha=0\\n \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028049, \\\"quality_users\\\": 4263 } } alpha=2\\n \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028309, \\\"quality_users\\\": 4263 } } alpha=4\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:37:54.538Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d0685c1487d284e0e43614b2d9ac", "usage": { "input_tokens": 72887, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 373, "reasoning_output_tokens": 90, "total_tokens": 73260 }, "turn_token_usage": { "input_tokens": 1708930, "cached_input_tokens": 1639680, "cache_write_input_tokens": 0, "output_tokens": 37256, "reasoning_output_tokens": 17365, "total_tokens": 1746186 }, "thread_token_usage": { "input_tokens": 1708930, "cached_input_tokens": 1639680, "cache_write_input_tokens": 0, "output_tokens": 37256, "reasoning_output_tokens": 17365, "total_tokens": 1746186 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T01:38:26.338Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:38:26.338Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:38:28.244Z", "callId": "call_t9oSQoJgHdvDwJs0FkcEiCwz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":32742,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:38:58.253Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"c674ad\",\"wall_time_seconds\":30.000660763,\"session_id\":32742,\"original_token_count\":89,\"output\":\" \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.02829, \\\"quality_users\\\": 4263 } } alpha=8\\n \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028215, \\\"quality_users\\\": 4263 } } alpha=12\\n \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028143, \\\"quality_users\\\": 4263 } } alpha=16\\n \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028143, \\\"quality_users\\\": 4263 } } alpha=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:38:28.462Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d090ced887d2b644641a93f57bcb", "usage": { "input_tokens": 73421, "cached_input_tokens": 72704, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 73475 }, "turn_token_usage": { "input_tokens": 1782351, "cached_input_tokens": 1712384, "cache_write_input_tokens": 0, "output_tokens": 37310, "reasoning_output_tokens": 17371, "total_tokens": 1819661 }, "thread_token_usage": { "input_tokens": 1782351, "cached_input_tokens": 1712384, "cache_write_input_tokens": 0, "output_tokens": 37310, "reasoning_output_tokens": 17371, "total_tokens": 1819661 } } }, { "kind": "context", "timestamp": "2026-09-25T01:39:00.221Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-241281df-f61e-4d38-bf5d-a53daa6e65b7", "process_id": "32742", "command": [ "/bin/sh", "-lc", "for a in 0 2 4 8 12 16 24 32; do /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v1_step1000.pt --alpha \"$a\" --output \"/app/solution/v1_s1000_a${a}.parquet\" >/tmp/ranklog; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s1000_a${a}.parquet\" | tail -5 | tr '\\n' ' '; echo \" alpha=$a\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0 2 4 8 12 16 24 32; do /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v1_step1000.pt --alpha \"$a\" --output \"/app/solution/v1_s1000_a${a}.parquet\" >/tmp/ranklog; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s1000_a${a}.parquet\" | tail -5 | tr '\\n' ' '; echo \" alpha=$a\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"scores\": { \"quality_ndcg@10\": 0.000185, \"quality_users\": 4263 } } alpha=0\n \"scores\": { \"quality_ndcg@10\": 0.028049, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.028309, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.02829, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.028215, \"quality_users\": 4263 } } alpha=12\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=16\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=24\n \"scores\": { \"quality_ndcg@10\": 0.028133, \"quality_users\": 4263 } } alpha=32\n", "stderr": "", "aggregated_output": " \"scores\": { \"quality_ndcg@10\": 0.000185, \"quality_users\": 4263 } } alpha=0\n \"scores\": { \"quality_ndcg@10\": 0.028049, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.028309, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.02829, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.028215, \"quality_users\": 4263 } } alpha=12\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=16\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=24\n \"scores\": { \"quality_ndcg@10\": 0.028133, \"quality_users\": 4263 } } alpha=32\n", "exit_code": 0, "duration": { "secs": 65, "nanos": 613643651 }, "formatted_output": " \"scores\": { \"quality_ndcg@10\": 0.000185, \"quality_users\": 4263 } } alpha=0\n \"scores\": { \"quality_ndcg@10\": 0.028049, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.028309, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.02829, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.028215, \"quality_users\": 4263 } } alpha=12\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=16\n \"scores\": { \"quality_ndcg@10\": 0.028143, \"quality_users\": 4263 } } alpha=24\n \"scores\": { \"quality_ndcg@10\": 0.028133, \"quality_users\": 4263 } } alpha=32\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:39:00.717Z", "callId": "call_o4F83UNj1jJ4jLi49zKrzYgq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":32742,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:39:00.802Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"6bee78\",\"wall_time_seconds\":0.00000204,\"exit_code\":0,\"original_token_count\":23,\"output\":\" \\\"scores\\\": { \\\"quality_ndcg@10\\\": 0.028133, \\\"quality_users\\\": 4263 } } alpha=32\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:39:00.799Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d0b2790487d2982fbbb71da9d779", "usage": { "input_tokens": 73671, "cached_input_tokens": 73216, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 73717 }, "turn_token_usage": { "input_tokens": 1856022, "cached_input_tokens": 1785600, "cache_write_input_tokens": 0, "output_tokens": 37356, "reasoning_output_tokens": 17371, "total_tokens": 1893378 }, "thread_token_usage": { "input_tokens": 1856022, "cached_input_tokens": 1785600, "cache_write_input_tokens": 0, "output_tokens": 37356, "reasoning_output_tokens": 17371, "total_tokens": 1893378 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T01:39:03.764Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:39:03.764Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:39:07.122Z", "callId": "call_Q37uO0ER0MNKxhseE3L4Dn9R", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for s in 2000 3000; do ck=/app/solution/two_tower_v1.pt; test \\\"$s\\\" = 2000 && ck=/app/solution/two_tower_v1_step2000.pt; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \\\"$ck\\\" --alpha 0 --output \\\"/app/solution/v1_s${s}_a0.parquet\\\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/v1_s${s}_a0.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:39:23.837Z", "content": "Script completed\nWall time 16.7 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/v1_s2000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 6.1e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/v1_s3000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 5.2e-05,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:39:07.226Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d0b5078c87d2ac4b7b19e53e44cf", "usage": { "input_tokens": 73807, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 265, "reasoning_output_tokens": 80, "total_tokens": 74072 }, "turn_token_usage": { "input_tokens": 1929829, "cached_input_tokens": 1859072, "cache_write_input_tokens": 0, "output_tokens": 37621, "reasoning_output_tokens": 17451, "total_tokens": 1967450 }, "thread_token_usage": { "input_tokens": 1929829, "cached_input_tokens": 1859072, "cache_write_input_tokens": 0, "output_tokens": 37621, "reasoning_output_tokens": 17451, "total_tokens": 1967450 } } }, { "kind": "context", "timestamp": "2026-09-25T01:39:23.799Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2c9c5975-683d-4f58-823e-82c7888380b8", "process_id": "3598", "command": [ "/bin/sh", "-lc", "for s in 2000 3000; do ck=/app/solution/two_tower_v1.pt; test \"$s\" = 2000 && ck=/app/solution/two_tower_v1_step2000.pt; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 0 --output \"/app/solution/v1_s${s}_a0.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s${s}_a0.parquet\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for s in 2000 3000; do ck=/app/solution/two_tower_v1.pt; test \"$s\" = 2000 && ck=/app/solution/two_tower_v1_step2000.pt; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 0 --output \"/app/solution/v1_s${s}_a0.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/v1_s${s}_a0.parquet\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/v1_s2000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 6.1e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/v1_s3000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 5.2e-05,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/v1_s2000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 6.1e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/v1_s3000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 5.2e-05,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 502705337 }, "formatted_output": "{\n \"model\": \"/app/solution/v1_s2000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 6.1e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/v1_s3000_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 5.2e-05,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-25T01:39:29.679Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:39:29.679Z", "text": "" } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-25T01:39:30.871Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:39:30.871Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:39:33.272Z", "callId": "call_qGZ1KE1Iz3gzMHceGPn4XDAW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v2_semantic --steps 5000 --save-every 1000 --log-every 200 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:40:03.456Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"712f8a\",\"wall_time_seconds\":30.001561934,\"session_id\":54551,\"original_token_count\":186,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_v2_semantic\\\",\\n \\\"checkpoint\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 5000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 200,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nstep=1/5000 loss=4.7231 ema=4.7231 pairs/s=4,546 window=9\\nstep=200/5000 loss=4.0668 ema=4.0153 pairs/s=6,376 window=16\\nstep=400/5000 loss=3.8224 ema=3.9432 pairs/s=6,353 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:39:33.324Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d0cc361487d29764cd4d7240f279", "usage": { "input_tokens": 74199, "cached_input_tokens": 73600, "cache_write_input_tokens": 0, "output_tokens": 417, "reasoning_output_tokens": 281, "total_tokens": 74616 }, "turn_token_usage": { "input_tokens": 2004028, "cached_input_tokens": 1932672, "cache_write_input_tokens": 0, "output_tokens": 38038, "reasoning_output_tokens": 17732, "total_tokens": 2042066 }, "thread_token_usage": { "input_tokens": 2004028, "cached_input_tokens": 1932672, "cache_write_input_tokens": 0, "output_tokens": 38038, "reasoning_output_tokens": 17732, "total_tokens": 2042066 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-25T01:40:05.226Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:40:05.226Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:40:06.652Z", "text": "The calibrated blend reaches 0.028309, a small gain over trend alone. The semantic component was being bypassed during training, so I’ve started a second run with the trend coordinate disabled in the loss. That forces the history and candidate networks to learn collaborative structure; trend will be added back only after measuring the semantic checkpoint." }, { "kind": "tool", "timestamp": "2026-09-25T01:40:08.679Z", "callId": "call_vIQOpk2nWZY47q6zCvuCDh2L", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:40:38.731Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1fc8e9\",\"wall_time_seconds\":30.000765438,\"session_id\":54551,\"original_token_count\":77,\"output\":\"step=600/5000 loss=3.9083 ema=3.9055 pairs/s=6,334 window=19\\nstep=800/5000 loss=3.8436 ema=3.8970 pairs/s=6,336 window=26\\nstep=1000/5000 loss=3.7426 ema=3.8736 pairs/s=6,334 window=27\\nstep=1200/5000 loss=3.7531 ema=3.8656 pairs/s=6,326 window=25\\nstep=1400/5000 loss=3.8009 ema=3.8197 pairs/s=6,170 window=21\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:40:08.779Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d0f3bc7887d29a77ffc54787fed2", "usage": { "input_tokens": 75002, "cached_input_tokens": 73984, "cache_write_input_tokens": 0, "output_tokens": 147, "reasoning_output_tokens": 25, "total_tokens": 75149 }, "turn_token_usage": { "input_tokens": 2079030, "cached_input_tokens": 2006656, "cache_write_input_tokens": 0, "output_tokens": 38185, "reasoning_output_tokens": 17757, "total_tokens": 2117215 }, "thread_token_usage": { "input_tokens": 2079030, "cached_input_tokens": 2006656, "cache_write_input_tokens": 0, "output_tokens": 38185, "reasoning_output_tokens": 17757, "total_tokens": 2117215 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:40:45.736Z", "callId": "call_UWYDWnfcYYakPtiJ8EgJeFWW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:41:15.803Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1a4572\",\"wall_time_seconds\":30.000898213,\"session_id\":54551,\"original_token_count\":62,\"output\":\"step=1600/5000 loss=3.8094 ema=3.8239 pairs/s=6,184 window=27\\nstep=1800/5000 loss=3.6942 ema=3.8093 pairs/s=6,196 window=24\\nstep=2000/5000 loss=3.7559 ema=3.7820 pairs/s=6,213 window=18\\nstep=2200/5000 loss=3.7173 ema=3.7459 pairs/s=6,216 window=22\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:40:45.835Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d11830f087d28dca3cd1c78b65bc", "usage": { "input_tokens": 75350, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 75396 }, "turn_token_usage": { "input_tokens": 2154380, "cached_input_tokens": 2081536, "cache_write_input_tokens": 0, "output_tokens": 38231, "reasoning_output_tokens": 17757, "total_tokens": 2192611 }, "thread_token_usage": { "input_tokens": 2154380, "cached_input_tokens": 2081536, "cache_write_input_tokens": 0, "output_tokens": 38231, "reasoning_output_tokens": 17757, "total_tokens": 2192611 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:41:17.746Z", "callId": "call_VvcFDFU3lZ1nDIp6JfpVExn0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:41:47.787Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"461ecd\",\"wall_time_seconds\":30.00106119,\"session_id\":54551,\"original_token_count\":62,\"output\":\"step=2400/5000 loss=3.6541 ema=3.7415 pairs/s=6,222 window=23\\nstep=2600/5000 loss=3.5124 ema=3.7220 pairs/s=6,233 window=27\\nstep=2800/5000 loss=3.6653 ema=3.7097 pairs/s=6,235 window=24\\nstep=3000/5000 loss=3.5873 ema=3.6637 pairs/s=6,240 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:41:17.830Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d13c15dc87d2ad2181c436bf901e", "usage": { "input_tokens": 75569, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 75615 }, "turn_token_usage": { "input_tokens": 2229949, "cached_input_tokens": 2156672, "cache_write_input_tokens": 0, "output_tokens": 38277, "reasoning_output_tokens": 17757, "total_tokens": 2268226 }, "thread_token_usage": { "input_tokens": 2229949, "cached_input_tokens": 2156672, "cache_write_input_tokens": 0, "output_tokens": 38277, "reasoning_output_tokens": 17757, "total_tokens": 2268226 } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-25T01:41:50.963Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:41:50.963Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:41:51.558Z", "callId": "call_TUHcLs3qbMxDDGOjbZx3wilE", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:42:21.605Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"05d3b7\",\"wall_time_seconds\":30.000914621,\"session_id\":54551,\"original_token_count\":62,\"output\":\"step=3200/5000 loss=3.7184 ema=3.6514 pairs/s=6,241 window=1\\nstep=3400/5000 loss=3.6559 ema=3.6266 pairs/s=6,245 window=12\\nstep=3600/5000 loss=3.6625 ema=3.6116 pairs/s=6,254 window=13\\nstep=3800/5000 loss=3.6674 ema=3.5846 pairs/s=6,256 window=4\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:41:51.631Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d15c0bb887d29d3a2b3f8423f0c2", "usage": { "input_tokens": 75786, "cached_input_tokens": 75392, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 12, "total_tokens": 75846 }, "turn_token_usage": { "input_tokens": 2305735, "cached_input_tokens": 2232064, "cache_write_input_tokens": 0, "output_tokens": 38337, "reasoning_output_tokens": 17769, "total_tokens": 2344072 }, "thread_token_usage": { "input_tokens": 2305735, "cached_input_tokens": 2232064, "cache_write_input_tokens": 0, "output_tokens": 38337, "reasoning_output_tokens": 17769, "total_tokens": 2344072 } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-25T01:42:24.784Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:42:24.784Z", "text": "The forced-semantic run continues to improve (sampled-softmax EMA 3.90 → 3.58) and is now beyond one pass over the 947k rolling pairs. I’m letting it finish because the held-out public ranking—not training loss—will tell us where personalization peaks; all 1k-step states remain available." }, { "kind": "tool", "timestamp": "2026-09-25T01:42:25.045Z", "callId": "call_XXhLwtxYM0IhT8PVotFyAWsj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:42:55.079Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"f42522\",\"wall_time_seconds\":30.001092863,\"session_id\":54551,\"original_token_count\":62,\"output\":\"step=4000/5000 loss=3.5634 ema=3.5626 pairs/s=6,261 window=21\\nstep=4200/5000 loss=3.4908 ema=3.5289 pairs/s=6,262 window=22\\nstep=4400/5000 loss=3.3004 ema=3.5049 pairs/s=6,219 window=26\\nstep=4600/5000 loss=3.5948 ema=3.4918 pairs/s=6,224 window=11\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:42:25.114Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d17ddc3487d29ba9dc88ee44a49c", "usage": { "input_tokens": 76020, "cached_input_tokens": 75648, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 0, "total_tokens": 76142 }, "turn_token_usage": { "input_tokens": 2381755, "cached_input_tokens": 2307712, "cache_write_input_tokens": 0, "output_tokens": 38459, "reasoning_output_tokens": 17769, "total_tokens": 2420214 }, "thread_token_usage": { "input_tokens": 2381755, "cached_input_tokens": 2307712, "cache_write_input_tokens": 0, "output_tokens": 38459, "reasoning_output_tokens": 17769, "total_tokens": 2420214 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:42:57.402Z", "callId": "call_qY4PAU2vLUwTdn8AmdkNnNvl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54551,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:43:05.865Z", "content": "Script completed\nWall time 8.4 seconds\nOutput:\n\n{\"chunk_id\":\"4ccce1\",\"wall_time_seconds\":8.420510243,\"exit_code\":0,\"original_token_count\":46,\"output\":\"step=4800/5000 loss=3.5842 ema=3.4717 pairs/s=6,225 window=22\\nstep=5000/5000 loss=3.6262 ema=3.4600 pairs/s=6,231 window=11\\nsaved model /app/solution/two_tower_v2_semantic.pt (205.4s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:42:57.502Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d19f6a0c87d2ab878f8a6aff80e8", "usage": { "input_tokens": 76314, "cached_input_tokens": 75904, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 76360 }, "turn_token_usage": { "input_tokens": 2458069, "cached_input_tokens": 2383616, "cache_write_input_tokens": 0, "output_tokens": 38505, "reasoning_output_tokens": 17769, "total_tokens": 2496574 }, "thread_token_usage": { "input_tokens": 2458069, "cached_input_tokens": 2383616, "cache_write_input_tokens": 0, "output_tokens": 38505, "reasoning_output_tokens": 17769, "total_tokens": 2496574 } } }, { "kind": "context", "timestamp": "2026-09-25T01:43:05.854Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-94617430-24a1-4456-a56b-01712bfac1ac", "process_id": "54551", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v2_semantic --steps 5000 --save-every 1000 --log-every 200 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v2_semantic --steps 5000 --save-every 1000 --log-every 200 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v2_semantic\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 200,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7231 ema=4.7231 pairs/s=4,546 window=9\nstep=200/5000 loss=4.0668 ema=4.0153 pairs/s=6,376 window=16\nstep=400/5000 loss=3.8224 ema=3.9432 pairs/s=6,353 window=24\nstep=600/5000 loss=3.9083 ema=3.9055 pairs/s=6,334 window=19\nstep=800/5000 loss=3.8436 ema=3.8970 pairs/s=6,336 window=26\nstep=1000/5000 loss=3.7426 ema=3.8736 pairs/s=6,334 window=27\nstep=1200/5000 loss=3.7531 ema=3.8656 pairs/s=6,326 window=25\nstep=1400/5000 loss=3.8009 ema=3.8197 pairs/s=6,170 window=21\nstep=1600/5000 loss=3.8094 ema=3.8239 pairs/s=6,184 window=27\nstep=1800/5000 loss=3.6942 ema=3.8093 pairs/s=6,196 window=24\nstep=2000/5000 loss=3.7559 ema=3.7820 pairs/s=6,213 window=18\nstep=2200/5000 loss=3.7173 ema=3.7459 pairs/s=6,216 window=22\nstep=2400/5000 loss=3.6541 ema=3.7415 pairs/s=6,222 window=23\nstep=2600/5000 loss=3.5124 ema=3.7220 pairs/s=6,233 window=27\nstep=2800/5000 loss=3.6653 ema=3.7097 pairs/s=6,235 window=24\nstep=3000/5000 loss=3.5873 ema=3.6637 pairs/s=6,240 window=24\nstep=3200/5000 loss=3.7184 ema=3.6514 pairs/s=6,241 window=1\nstep=3400/5000 loss=3.6559 ema=3.6266 pairs/s=6,245 window=12\nstep=3600/5000 loss=3.6625 ema=3.6116 pairs/s=6,254 window=13\nstep=3800/5000 loss=3.6674 ema=3.5846 pairs/s=6,256 window=4\nstep=4000/5000 loss=3.5634 ema=3.5626 pairs/s=6,261 window=21\nstep=4200/5000 loss=3.4908 ema=3.5289 pairs/s=6,262 window=22\nstep=4400/5000 loss=3.3004 ema=3.5049 pairs/s=6,219 window=26\nstep=4600/5000 loss=3.5948 ema=3.4918 pairs/s=6,224 window=11\nstep=4800/5000 loss=3.5842 ema=3.4717 pairs/s=6,225 window=22\nstep=5000/5000 loss=3.6262 ema=3.4600 pairs/s=6,231 window=11\nsaved model /app/solution/two_tower_v2_semantic.pt (205.4s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v2_semantic\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 200,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7231 ema=4.7231 pairs/s=4,546 window=9\nstep=200/5000 loss=4.0668 ema=4.0153 pairs/s=6,376 window=16\nstep=400/5000 loss=3.8224 ema=3.9432 pairs/s=6,353 window=24\nstep=600/5000 loss=3.9083 ema=3.9055 pairs/s=6,334 window=19\nstep=800/5000 loss=3.8436 ema=3.8970 pairs/s=6,336 window=26\nstep=1000/5000 loss=3.7426 ema=3.8736 pairs/s=6,334 window=27\nstep=1200/5000 loss=3.7531 ema=3.8656 pairs/s=6,326 window=25\nstep=1400/5000 loss=3.8009 ema=3.8197 pairs/s=6,170 window=21\nstep=1600/5000 loss=3.8094 ema=3.8239 pairs/s=6,184 window=27\nstep=1800/5000 loss=3.6942 ema=3.8093 pairs/s=6,196 window=24\nstep=2000/5000 loss=3.7559 ema=3.7820 pairs/s=6,213 window=18\nstep=2200/5000 loss=3.7173 ema=3.7459 pairs/s=6,216 window=22\nstep=2400/5000 loss=3.6541 ema=3.7415 pairs/s=6,222 window=23\nstep=2600/5000 loss=3.5124 ema=3.7220 pairs/s=6,233 window=27\nstep=2800/5000 loss=3.6653 ema=3.7097 pairs/s=6,235 window=24\nstep=3000/5000 loss=3.5873 ema=3.6637 pairs/s=6,240 window=24\nstep=3200/5000 loss=3.7184 ema=3.6514 pairs/s=6,241 window=1\nstep=3400/5000 loss=3.6559 ema=3.6266 pairs/s=6,245 window=12\nstep=3600/5000 loss=3.6625 ema=3.6116 pairs/s=6,254 window=13\nstep=3800/5000 loss=3.6674 ema=3.5846 pairs/s=6,256 window=4\nstep=4000/5000 loss=3.5634 ema=3.5626 pairs/s=6,261 window=21\nstep=4200/5000 loss=3.4908 ema=3.5289 pairs/s=6,262 window=22\nstep=4400/5000 loss=3.3004 ema=3.5049 pairs/s=6,219 window=26\nstep=4600/5000 loss=3.5948 ema=3.4918 pairs/s=6,224 window=11\nstep=4800/5000 loss=3.5842 ema=3.4717 pairs/s=6,225 window=22\nstep=5000/5000 loss=3.6262 ema=3.4600 pairs/s=6,231 window=11\nsaved model /app/solution/two_tower_v2_semantic.pt (205.4s)\n", "exit_code": 0, "duration": { "secs": 212, "nanos": 405836904 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v2_semantic\",\n \"checkpoint\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 200,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7231 ema=4.7231 pairs/s=4,546 window=9\nstep=200/5000 loss=4.0668 ema=4.0153 pairs/s=6,376 window=16\nstep=400/5000 loss=3.8224 ema=3.9432 pairs/s=6,353 window=24\nstep=600/5000 loss=3.9083 ema=3.9055 pairs/s=6,334 window=19\nstep=800/5000 loss=3.8436 ema=3.8970 pairs/s=6,336 window=26\nstep=1000/5000 loss=3.7426 ema=3.8736 pairs/s=6,334 window=27\nstep=1200/5000 loss=3.7531 ema=3.8656 pairs/s=6,326 window=25\nstep=1400/5000 loss=3.8009 ema=3.8197 pairs/s=6,170 window=21\nstep=1600/5000 loss=3.8094 ema=3.8239 pairs/s=6,184 window=27\nstep=1800/5000 loss=3.6942 ema=3.8093 pairs/s=6,196 window=24\nstep=2000/5000 loss=3.7559 ema=3.7820 pairs/s=6,213 window=18\nstep=2200/5000 loss=3.7173 ema=3.7459 pairs/s=6,216 window=22\nstep=2400/5000 loss=3.6541 ema=3.7415 pairs/s=6,222 window=23\nstep=2600/5000 loss=3.5124 ema=3.7220 pairs/s=6,233 window=27\nstep=2800/5000 loss=3.6653 ema=3.7097 pairs/s=6,235 window=24\nstep=3000/5000 loss=3.5873 ema=3.6637 pairs/s=6,240 window=24\nstep=3200/5000 loss=3.7184 ema=3.6514 pairs/s=6,241 window=1\nstep=3400/5000 loss=3.6559 ema=3.6266 pairs/s=6,245 window=12\nstep=3600/5000 loss=3.6625 ema=3.6116 pairs/s=6,254 window=13\nstep=3800/5000 loss=3.6674 ema=3.5846 pairs/s=6,256 window=4\nstep=4000/5000 loss=3.5634 ema=3.5626 pairs/s=6,261 window=21\nstep=4200/5000 loss=3.4908 ema=3.5289 pairs/s=6,262 window=22\nstep=4400/5000 loss=3.3004 ema=3.5049 pairs/s=6,219 window=26\nstep=4600/5000 loss=3.5948 ema=3.4918 pairs/s=6,224 window=11\nstep=4800/5000 loss=3.5842 ema=3.4717 pairs/s=6,225 window=22\nstep=5000/5000 loss=3.6262 ema=3.4600 pairs/s=6,231 window=11\nsaved model /app/solution/two_tower_v2_semantic.pt (205.4s)\n" } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-25T01:43:07.848Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:07.848Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:14.623Z", "callId": "call_uk5xglIDSdkgJF0BTcrIIIi1", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v2_semantic.pt; test \\\"$s\\\" != 5000 && ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a0.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \\\"$ck\\\" --alpha 0 --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"step=$s $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:43:44.802Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"8681e9\",\"wall_time_seconds\":30.0011287,\"session_id\":59654,\"original_token_count\":33,\"output\":\"step=1000 \\\"quality_ndcg@10\\\": 0.005746,\\nstep=2000 \\\"quality_ndcg@10\\\": 0.003523,\\nstep=3000 \\\"quality_ndcg@10\\\": 0.003511,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:43:14.733Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d1aa254487d28ea5250b0585f7fa", "usage": { "input_tokens": 76496, "cached_input_tokens": 76160, "cache_write_input_tokens": 0, "output_tokens": 234, "reasoning_output_tokens": 28, "total_tokens": 76730 }, "turn_token_usage": { "input_tokens": 2534565, "cached_input_tokens": 2459776, "cache_write_input_tokens": 0, "output_tokens": 38739, "reasoning_output_tokens": 17797, "total_tokens": 2573304 }, "thread_token_usage": { "input_tokens": 2534565, "cached_input_tokens": 2459776, "cache_write_input_tokens": 0, "output_tokens": 38739, "reasoning_output_tokens": 17797, "total_tokens": 2573304 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-25T01:43:46.896Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:46.896Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:47.609Z", "callId": "call_pZQy9mCwJX8rijSkppa6JROq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":59654,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:43:56.043Z", "content": "Script completed\nWall time 8.4 seconds\nOutput:\n\n{\"chunk_id\":\"db328e\",\"wall_time_seconds\":8.415475248,\"exit_code\":0,\"original_token_count\":22,\"output\":\"step=4000 \\\"quality_ndcg@10\\\": 0.004555,\\nstep=5000 \\\"quality_ndcg@10\\\": 0.007663,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:43:47.693Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d1d10d9887d2903b4ae102d2c314", "usage": { "input_tokens": 76844, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 11, "total_tokens": 76903 }, "turn_token_usage": { "input_tokens": 2611409, "cached_input_tokens": 2536064, "cache_write_input_tokens": 0, "output_tokens": 38798, "reasoning_output_tokens": 17808, "total_tokens": 2650207 }, "thread_token_usage": { "input_tokens": 2611409, "cached_input_tokens": 2536064, "cache_write_input_tokens": 0, "output_tokens": 38798, "reasoning_output_tokens": 17808, "total_tokens": 2650207 } } }, { "kind": "context", "timestamp": "2026-09-25T01:43:56.033Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-615b1ff8-7ea2-4fad-aea9-b13c2de6e765", "process_id": "59654", "command": [ "/bin/sh", "-lc", "for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v2_semantic.pt; test \"$s\" != 5000 && ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a0.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 0 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"step=$s $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v2_semantic.pt; test \"$s\" != 5000 && ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a0.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 0 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"step=$s $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "step=1000 \"quality_ndcg@10\": 0.005746,\nstep=2000 \"quality_ndcg@10\": 0.003523,\nstep=3000 \"quality_ndcg@10\": 0.003511,\nstep=4000 \"quality_ndcg@10\": 0.004555,\nstep=5000 \"quality_ndcg@10\": 0.007663,\n", "stderr": "", "aggregated_output": "step=1000 \"quality_ndcg@10\": 0.005746,\nstep=2000 \"quality_ndcg@10\": 0.003523,\nstep=3000 \"quality_ndcg@10\": 0.003511,\nstep=4000 \"quality_ndcg@10\": 0.004555,\nstep=5000 \"quality_ndcg@10\": 0.007663,\n", "exit_code": 0, "duration": { "secs": 41, "nanos": 232233336 }, "formatted_output": "step=1000 \"quality_ndcg@10\": 0.005746,\nstep=2000 \"quality_ndcg@10\": 0.003523,\nstep=3000 \"quality_ndcg@10\": 0.003511,\nstep=4000 \"quality_ndcg@10\": 0.004555,\nstep=5000 \"quality_ndcg@10\": 0.007663,\n" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-25T01:43:59.339Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:59.339Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:44:01.623Z", "callId": "call_Y53q1n4okv8n4uxzDkWQ9Gqf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.5 1 2 3 4 6 8 12; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \\\"$a\\\" --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"alpha=$a $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:44:31.796Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"6bb12a\",\"wall_time_seconds\":30.000977611,\"session_id\":61702,\"original_token_count\":32,\"output\":\"alpha=0.5 \\\"quality_ndcg@10\\\": 0.030723,\\nalpha=1 \\\"quality_ndcg@10\\\": 0.030776,\\nalpha=2 \\\"quality_ndcg@10\\\": 0.030123,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:44:01.683Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d1dc62b887d2b89ccd172394f081", "usage": { "input_tokens": 76996, "cached_input_tokens": 76672, "cache_write_input_tokens": 0, "output_tokens": 246, "reasoning_output_tokens": 68, "total_tokens": 77242 }, "turn_token_usage": { "input_tokens": 2688405, "cached_input_tokens": 2612736, "cache_write_input_tokens": 0, "output_tokens": 39044, "reasoning_output_tokens": 17876, "total_tokens": 2727449 }, "thread_token_usage": { "input_tokens": 2688405, "cached_input_tokens": 2612736, "cache_write_input_tokens": 0, "output_tokens": 39044, "reasoning_output_tokens": 17876, "total_tokens": 2727449 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-25T01:44:35.435Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:44:35.435Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:44:36.161Z", "callId": "call_Hi4kBm027PyGAhUGRtQBevvw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61702,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:45:06.170Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b55ca3\",\"wall_time_seconds\":30.001430737,\"session_id\":61702,\"original_token_count\":41,\"output\":\"alpha=3 \\\"quality_ndcg@10\\\": 0.029544,\\nalpha=4 \\\"quality_ndcg@10\\\": 0.029079,\\nalpha=6 \\\"quality_ndcg@10\\\": 0.028936,\\nalpha=8 \\\"quality_ndcg@10\\\": 0.028804,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:44:36.315Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d20048c087d296d0ce6c4bfbf724", "usage": { "input_tokens": 77355, "cached_input_tokens": 76800, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 77416 }, "turn_token_usage": { "input_tokens": 2765760, "cached_input_tokens": 2689536, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 17889, "total_tokens": 2804865 }, "thread_token_usage": { "input_tokens": 2765760, "cached_input_tokens": 2689536, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 17889, "total_tokens": 2804865 } } }, { "kind": "context", "timestamp": "2026-09-25T01:45:07.500Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-88650791-9e19-4628-a0a3-352ba2f79b37", "process_id": "61702", "command": [ "/bin/sh", "-lc", "for a in 0.5 1 2 3 4 6 8 12; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.5 1 2 3 4 6 8 12; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "alpha=0.5 \"quality_ndcg@10\": 0.030723,\nalpha=1 \"quality_ndcg@10\": 0.030776,\nalpha=2 \"quality_ndcg@10\": 0.030123,\nalpha=3 \"quality_ndcg@10\": 0.029544,\nalpha=4 \"quality_ndcg@10\": 0.029079,\nalpha=6 \"quality_ndcg@10\": 0.028936,\nalpha=8 \"quality_ndcg@10\": 0.028804,\nalpha=12 \"quality_ndcg@10\": 0.028511,\n", "stderr": "", "aggregated_output": "alpha=0.5 \"quality_ndcg@10\": 0.030723,\nalpha=1 \"quality_ndcg@10\": 0.030776,\nalpha=2 \"quality_ndcg@10\": 0.030123,\nalpha=3 \"quality_ndcg@10\": 0.029544,\nalpha=4 \"quality_ndcg@10\": 0.029079,\nalpha=6 \"quality_ndcg@10\": 0.028936,\nalpha=8 \"quality_ndcg@10\": 0.028804,\nalpha=12 \"quality_ndcg@10\": 0.028511,\n", "exit_code": 0, "duration": { "secs": 65, "nanos": 706025350 }, "formatted_output": "alpha=0.5 \"quality_ndcg@10\": 0.030723,\nalpha=1 \"quality_ndcg@10\": 0.030776,\nalpha=2 \"quality_ndcg@10\": 0.030123,\nalpha=3 \"quality_ndcg@10\": 0.029544,\nalpha=4 \"quality_ndcg@10\": 0.029079,\nalpha=6 \"quality_ndcg@10\": 0.028936,\nalpha=8 \"quality_ndcg@10\": 0.028804,\nalpha=12 \"quality_ndcg@10\": 0.028511,\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:45:11.457Z", "callId": "call_old2VvOZwzN2QZo2BydDiEow", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61702,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:45:11.567Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"52089d\",\"wall_time_seconds\":0.00000225,\"exit_code\":0,\"original_token_count\":11,\"output\":\"alpha=12 \\\"quality_ndcg@10\\\": 0.028511,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:45:11.565Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d22292f087d2a6cbf9c420e940dc", "usage": { "input_tokens": 77545, "cached_input_tokens": 77184, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 77591 }, "turn_token_usage": { "input_tokens": 2843305, "cached_input_tokens": 2766720, "cache_write_input_tokens": 0, "output_tokens": 39151, "reasoning_output_tokens": 17889, "total_tokens": 2882456 }, "thread_token_usage": { "input_tokens": 2843305, "cached_input_tokens": 2766720, "cache_write_input_tokens": 0, "output_tokens": 39151, "reasoning_output_tokens": 17889, "total_tokens": 2882456 } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-25T01:45:17.342Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:45:17.342Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:45:17.575Z", "text": "The forced semantic tower now contributes real lift: with the trend coordinate restored, NDCG@10 rises to 0.030776, about 9% above the strongest non-personalized baseline. The best blend is near equal score scale (`alpha≈1`), confirming that the history tower is adding useful ordering rather than merely reproducing popularity." }, { "kind": "tool", "timestamp": "2026-09-25T01:45:25.556Z", "callId": "call_KfgBYhhrHuvjjkSFr8gySnSK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for s in 1000 2000 3000 4000; do ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a1.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \\\"$ck\\\" --alpha 1 --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"step=$s $score\\\"; done\\nfor a in 0.6 0.75 0.9 1.1 1.25 1.5; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \\\"$a\\\" --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"alpha=$a $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:45:55.727Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"7ecdfa\",\"wall_time_seconds\":30.000908735,\"session_id\":93634,\"original_token_count\":33,\"output\":\"step=1000 \\\"quality_ndcg@10\\\": 0.029291,\\nstep=2000 \\\"quality_ndcg@10\\\": 0.029752,\\nstep=3000 \\\"quality_ndcg@10\\\": 0.030155,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:45:25.648Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d227f7e887d2b5e0ce80a5ae0550", "usage": { "input_tokens": 77664, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 469, "reasoning_output_tokens": 75, "total_tokens": 78133 }, "turn_token_usage": { "input_tokens": 2920969, "cached_input_tokens": 2844032, "cache_write_input_tokens": 0, "output_tokens": 39620, "reasoning_output_tokens": 17964, "total_tokens": 2960589 }, "thread_token_usage": { "input_tokens": 2920969, "cached_input_tokens": 2844032, "cache_write_input_tokens": 0, "output_tokens": 39620, "reasoning_output_tokens": 17964, "total_tokens": 2960589 } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-25T01:45:57.953Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:45:57.953Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:45:59.040Z", "callId": "call_JOGnpBblcWzsBdlN4L8UcHCP", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":93634,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:46:29.048Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"f0f503\",\"wall_time_seconds\":30.000493248,\"session_id\":93634,\"original_token_count\":44,\"output\":\"step=4000 \\\"quality_ndcg@10\\\": 0.030666,\\nalpha=0.6 \\\"quality_ndcg@10\\\": 0.030838,\\nalpha=0.75 \\\"quality_ndcg@10\\\": 0.031123,\\nalpha=0.9 \\\"quality_ndcg@10\\\": 0.030834,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:45:59.123Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d253e7a087d2bf3ac54167790312", "usage": { "input_tokens": 78246, "cached_input_tokens": 77440, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 78300 }, "turn_token_usage": { "input_tokens": 2999215, "cached_input_tokens": 2921472, "cache_write_input_tokens": 0, "output_tokens": 39674, "reasoning_output_tokens": 17970, "total_tokens": 3038889 }, "thread_token_usage": { "input_tokens": 2999215, "cached_input_tokens": 2921472, "cache_write_input_tokens": 0, "output_tokens": 39674, "reasoning_output_tokens": 17970, "total_tokens": 3038889 } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-25T01:46:34.345Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:46:34.345Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:46:35.036Z", "callId": "call_JtT1QKQ7taIFAbMpikcbDP3y", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":93634,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:46:47.734Z", "content": "Script completed\nWall time 12.7 seconds\nOutput:\n\n{\"chunk_id\":\"03accd\",\"wall_time_seconds\":12.68398187,\"exit_code\":0,\"original_token_count\":33,\"output\":\"alpha=1.1 \\\"quality_ndcg@10\\\": 0.030689,\\nalpha=1.25 \\\"quality_ndcg@10\\\": 0.03051,\\nalpha=1.5 \\\"quality_ndcg@10\\\": 0.030509,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:46:35.122Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d2787fb887d2a19f98e981ba5aa1", "usage": { "input_tokens": 78436, "cached_input_tokens": 78080, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 78490 }, "turn_token_usage": { "input_tokens": 3077651, "cached_input_tokens": 2999552, "cache_write_input_tokens": 0, "output_tokens": 39728, "reasoning_output_tokens": 17976, "total_tokens": 3117379 }, "thread_token_usage": { "input_tokens": 3077651, "cached_input_tokens": 2999552, "cache_write_input_tokens": 0, "output_tokens": 39728, "reasoning_output_tokens": 17976, "total_tokens": 3117379 } } }, { "kind": "context", "timestamp": "2026-09-25T01:46:47.728Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fe872a8d-301c-4163-a56c-71135f2a3b33", "process_id": "93634", "command": [ "/bin/sh", "-lc", "for s in 1000 2000 3000 4000; do ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a1.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 1 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"step=$s $score\"; done\nfor a in 0.6 0.75 0.9 1.1 1.25 1.5; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for s in 1000 2000 3000 4000; do ck=/app/solution/two_tower_v2_semantic_step${s}.pt; out=/app/solution/v2_s${s}_a1.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --alpha 1 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"step=$s $score\"; done\nfor a in 0.6 0.75 0.9 1.1 1.25 1.5; do out=/app/solution/v2_s5000_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "step=1000 \"quality_ndcg@10\": 0.029291,\nstep=2000 \"quality_ndcg@10\": 0.029752,\nstep=3000 \"quality_ndcg@10\": 0.030155,\nstep=4000 \"quality_ndcg@10\": 0.030666,\nalpha=0.6 \"quality_ndcg@10\": 0.030838,\nalpha=0.75 \"quality_ndcg@10\": 0.031123,\nalpha=0.9 \"quality_ndcg@10\": 0.030834,\nalpha=1.1 \"quality_ndcg@10\": 0.030689,\nalpha=1.25 \"quality_ndcg@10\": 0.03051,\nalpha=1.5 \"quality_ndcg@10\": 0.030509,\n", "stderr": "", "aggregated_output": "step=1000 \"quality_ndcg@10\": 0.029291,\nstep=2000 \"quality_ndcg@10\": 0.029752,\nstep=3000 \"quality_ndcg@10\": 0.030155,\nstep=4000 \"quality_ndcg@10\": 0.030666,\nalpha=0.6 \"quality_ndcg@10\": 0.030838,\nalpha=0.75 \"quality_ndcg@10\": 0.031123,\nalpha=0.9 \"quality_ndcg@10\": 0.030834,\nalpha=1.1 \"quality_ndcg@10\": 0.030689,\nalpha=1.25 \"quality_ndcg@10\": 0.03051,\nalpha=1.5 \"quality_ndcg@10\": 0.030509,\n", "exit_code": 0, "duration": { "secs": 82, "nanos": 2582430 }, "formatted_output": "step=1000 \"quality_ndcg@10\": 0.029291,\nstep=2000 \"quality_ndcg@10\": 0.029752,\nstep=3000 \"quality_ndcg@10\": 0.030155,\nstep=4000 \"quality_ndcg@10\": 0.030666,\nalpha=0.6 \"quality_ndcg@10\": 0.030838,\nalpha=0.75 \"quality_ndcg@10\": 0.031123,\nalpha=0.9 \"quality_ndcg@10\": 0.030834,\nalpha=1.1 \"quality_ndcg@10\": 0.030689,\nalpha=1.25 \"quality_ndcg@10\": 0.03051,\nalpha=1.5 \"quality_ndcg@10\": 0.030509,\n" } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-25T01:47:01.633Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:01.633Z", "text": "" } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-25T01:47:09.680Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:09.680Z", "text": "" } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-25T01:47:12.407Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:12.407Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:47:14.641Z", "callId": "call_HbXTXejkDKjPC91fyRcLGnRK", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_rank.py\\n@@\\n def train_model(cache_path: Path, args: argparse.Namespace) -> Path:\\n@@\\n config = model_config(cache, args)\\n model = TwoTowerRanker(**config)\\n+ if args.init_checkpoint is not None:\\n+ initial = torch.load(args.init_checkpoint, map_location=\\\"cpu\\\", weights_only=False)\\n+ if initial[\\\"config\\\"] != config:\\n+ raise ValueError(\\\"initial checkpoint architecture does not match requested model\\\")\\n+ model.load_state_dict(initial[\\\"model\\\"])\\n+ print(f\\\"initialized from {args.init_checkpoint}\\\", flush=True)\\n@@\\n parser.add_argument(\\\"--checkpoint\\\", type=Path)\\n+ parser.add_argument(\\\"--init-checkpoint\\\", type=Path)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:47:14.704Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:47:14.650Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b0842927-06b0-4fdd-b6a1-5a7883b82930", "changes": { "/app/solution/train_and_rank.py": { "type": "update", "unified_diff": "@@ -560,2 +560,8 @@\n model = TwoTowerRanker(**config)\n+ if args.init_checkpoint is not None:\n+ initial = torch.load(args.init_checkpoint, map_location=\"cpu\", weights_only=False)\n+ if initial[\"config\"] != config:\n+ raise ValueError(\"initial checkpoint architecture does not match requested model\")\n+ model.load_state_dict(initial[\"model\"])\n+ print(f\"initialized from {args.init_checkpoint}\", flush=True)\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)\n@@ -747,2 +753,3 @@\n parser.add_argument(\"--checkpoint\", type=Path)\n+ parser.add_argument(\"--init-checkpoint\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:47:14.703Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d288fb1c87d29369f530e9dbdcf9", "usage": { "input_tokens": 78605, "cached_input_tokens": 78208, "cache_write_input_tokens": 0, "output_tokens": 1377, "reasoning_output_tokens": 1167, "total_tokens": 79982 }, "turn_token_usage": { "input_tokens": 3156256, "cached_input_tokens": 3077760, "cache_write_input_tokens": 0, "output_tokens": 41105, "reasoning_output_tokens": 19143, "total_tokens": 3197361 }, "thread_token_usage": { "input_tokens": 3156256, "cached_input_tokens": 3077760, "cache_write_input_tokens": 0, "output_tokens": 41105, "reasoning_output_tokens": 19143, "total_tokens": 3197361 } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-25T01:47:24.931Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:24.931Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:47:43.258Z", "callId": "call_Nu9jn7SPPCIRinciEXwGxo6o", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,time\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nimport pandas as pd,numpy as np\\nD='/task/data'; cutoff=24786800\\ntarget=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\\nz=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].copy(); pack=z['packed_eligible'].copy(); z.close()\\nel=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); truth=validation_truth(el,target)\\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\nx=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]\\nidx=x.item_id.map(mp).to_numpy(np.int32); ts=x.timestamp.to_numpy(np.int64); uid=x.uid.to_numpy(np.int64)\\nmetrics={}\\nfor d in [1,2,3,4,5,6,7,8,10,12,14,21,28,42,60,90]:\\n m=ts>=cutoff-d*86400\\n metrics[f'cnt{d}']=np.bincount(idx[m],minlength=n).astype(float)\\n # uniques only selected windows\\n if d in [3,7,14,28,60]:\\n tmp=pd.DataFrame({'u':uid[m],'i':idx[m]}).drop_duplicates()\\n metrics[f'uniq{d}']=np.bincount(tmp.i,minlength=n).astype(float)\\nage=(cutoff-ts)/86400\\nfor h in [1,2,3,5,7,10,14,21,30]:\\n w=np.exp(-np.log(2)*age/h)\\n metrics[f'ewm{h}']=np.bincount(idx,weights=w,minlength=n)\\n# blends/momentum\\nmetrics['blend7_28']=np.log1p(metrics['cnt7'])+.12*np.log1p(metrics['cnt28'])\\nmetrics['blend3_14']=np.log1p(metrics['cnt3'])+.2*np.log1p(metrics['cnt14'])\\nmetrics['trend7']=np.log1p(metrics['cnt7'])-.25*np.log1p(metrics['cnt28']+1)\\n# scorer\\nfor name,s in metrics.items():\\n order=np.lexsort((np.arange(n),-metrics.get('cnt28',s),-s))\\n ranked={}\\n cols=order\\n for r,uu in enumerate(u):\\n ok=((pack[r,cols//8]>>(cols%8))&1).astype(bool)\\n ranked[int(uu)]=ids[cols[ok][:100]].astype(int).tolist()\\n q=score_rankings(ranked,truth)['quality_ndcg@10']\\n print(name,q,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:48:13.424Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"a3ba0b\",\"wall_time_seconds\":30.000566451,\"session_id\":46009,\"original_token_count\":36,\"output\":\"cnt1 0.016755\\ncnt2 0.020697\\ncnt3 0.025721\\nuniq3 0.031607\\ncnt4 0.027354\\ncnt5 0.027302\\ncnt6 0.027538\\ncnt7 0.028131\\nuniq7 0.030373\\ncnt8 0.026585\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:47:43.455Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d2a31c2487d2841b3eab53ddefa8", "usage": { "input_tokens": 80006, "cached_input_tokens": 78464, "cache_write_input_tokens": 0, "output_tokens": 1040, "reasoning_output_tokens": 289, "total_tokens": 81046 }, "turn_token_usage": { "input_tokens": 3236262, "cached_input_tokens": 3156224, "cache_write_input_tokens": 0, "output_tokens": 42145, "reasoning_output_tokens": 19432, "total_tokens": 3278407 }, "thread_token_usage": { "input_tokens": 3236262, "cached_input_tokens": 3156224, "cache_write_input_tokens": 0, "output_tokens": 42145, "reasoning_output_tokens": 19432, "total_tokens": 3278407 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-25T01:48:16.029Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:48:16.029Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:48:16.765Z", "callId": "call_3Likep53ky99izq2jEXWO4tk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":46009,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:48:46.774Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b6d1b6\",\"wall_time_seconds\":30.001211622,\"session_id\":46009,\"original_token_count\":63,\"output\":\"cnt10 0.025154\\ncnt12 0.0262\\ncnt14 0.022878\\nuniq14 0.025464\\ncnt21 0.016279\\ncnt28 0.012967\\nuniq28 0.019078\\ncnt42 0.01594\\ncnt60 0.015826\\nuniq60 0.017372\\ncnt90 0.015822\\newm1 0.025121\\newm2 0.026281\\newm3 0.027438\\newm5 0.026567\\newm7 0.022218\\newm10 0.018984\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:48:16.862Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d2ddc2b087d2b236fd97fe34d93a", "usage": { "input_tokens": 81184, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 17, "total_tokens": 81249 }, "turn_token_usage": { "input_tokens": 3317446, "cached_input_tokens": 3236096, "cache_write_input_tokens": 0, "output_tokens": 42210, "reasoning_output_tokens": 19449, "total_tokens": 3359656 }, "thread_token_usage": { "input_tokens": 3317446, "cached_input_tokens": 3236096, "cache_write_input_tokens": 0, "output_tokens": 42210, "reasoning_output_tokens": 19449, "total_tokens": 3359656 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:48:48.407Z", "callId": "call_PzvcQkXhRNbu6p8mqMAVs4iD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":46009,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:48:57.799Z", "content": "Script completed\nWall time 9.4 seconds\nOutput:\n\n{\"chunk_id\":\"51eba2\",\"wall_time_seconds\":9.375090435,\"exit_code\":0,\"original_token_count\":25,\"output\":\"ewm14 0.017692\\newm21 0.016336\\newm30 0.016117\\nblend7_28 0.027947\\nblend3_14 0.026679\\ntrend7 0.027898\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:48:48.488Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d2fef23c87d2bc8bdf45f347cac4", "usage": { "input_tokens": 81450, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 81496 }, "turn_token_usage": { "input_tokens": 3398896, "cached_input_tokens": 3317120, "cache_write_input_tokens": 0, "output_tokens": 42256, "reasoning_output_tokens": 19449, "total_tokens": 3441152 }, "thread_token_usage": { "input_tokens": 3398896, "cached_input_tokens": 3317120, "cache_write_input_tokens": 0, "output_tokens": 42256, "reasoning_output_tokens": 19449, "total_tokens": 3441152 } } }, { "kind": "context", "timestamp": "2026-09-25T01:48:57.789Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4b005d44-fcbd-4b84-b31c-66572cdab2e8", "process_id": "46009", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd,numpy as np\nD='/task/data'; cutoff=24786800\ntarget=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nz=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].copy(); pack=z['packed_eligible'].copy(); z.close()\nel=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); truth=validation_truth(el,target)\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]\nidx=x.item_id.map(mp).to_numpy(np.int32); ts=x.timestamp.to_numpy(np.int64); uid=x.uid.to_numpy(np.int64)\nmetrics={}\nfor d in [1,2,3,4,5,6,7,8,10,12,14,21,28,42,60,90]:\n m=ts>=cutoff-d*86400\n metrics[f'cnt{d}']=np.bincount(idx[m],minlength=n).astype(float)\n # uniques only selected windows\n if d in [3,7,14,28,60]:\n tmp=pd.DataFrame({'u':uid[m],'i':idx[m]}).drop_duplicates()\n metrics[f'uniq{d}']=np.bincount(tmp.i,minlength=n).astype(float)\nage=(cutoff-ts)/86400\nfor h in [1,2,3,5,7,10,14,21,30]:\n w=np.exp(-np.log(2)*age/h)\n metrics[f'ewm{h}']=np.bincount(idx,weights=w,minlength=n)\n# blends/momentum\nmetrics['blend7_28']=np.log1p(metrics['cnt7'])+.12*np.log1p(metrics['cnt28'])\nmetrics['blend3_14']=np.log1p(metrics['cnt3'])+.2*np.log1p(metrics['cnt14'])\nmetrics['trend7']=np.log1p(metrics['cnt7'])-.25*np.log1p(metrics['cnt28']+1)\n# scorer\nfor name,s in metrics.items():\n order=np.lexsort((np.arange(n),-metrics.get('cnt28',s),-s))\n ranked={}\n cols=order\n for r,uu in enumerate(u):\n ok=((pack[r,cols//8]>>(cols%8))&1).astype(bool)\n ranked[int(uu)]=ids[cols[ok][:100]].astype(int).tolist()\n q=score_rankings(ranked,truth)['quality_ndcg@10']\n print(name,q,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd,numpy as np\nD='/task/data'; cutoff=24786800\ntarget=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nz=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].copy(); pack=z['packed_eligible'].copy(); z.close()\nel=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); truth=validation_truth(el,target)\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]\nidx=x.item_id.map(mp).to_numpy(np.int32); ts=x.timestamp.to_numpy(np.int64); uid=x.uid.to_numpy(np.int64)\nmetrics={}\nfor d in [1,2,3,4,5,6,7,8,10,12,14,21,28,42,60,90]:\n m=ts>=cutoff-d*86400\n metrics[f'cnt{d}']=np.bincount(idx[m],minlength=n).astype(float)\n # uniques only selected windows\n if d in [3,7,14,28,60]:\n tmp=pd.DataFrame({'u':uid[m],'i':idx[m]}).drop_duplicates()\n metrics[f'uniq{d}']=np.bincount(tmp.i,minlength=n).astype(float)\nage=(cutoff-ts)/86400\nfor h in [1,2,3,5,7,10,14,21,30]:\n w=np.exp(-np.log(2)*age/h)\n metrics[f'ewm{h}']=np.bincount(idx,weights=w,minlength=n)\n# blends/momentum\nmetrics['blend7_28']=np.log1p(metrics['cnt7'])+.12*np.log1p(metrics['cnt28'])\nmetrics['blend3_14']=np.log1p(metrics['cnt3'])+.2*np.log1p(metrics['cnt14'])\nmetrics['trend7']=np.log1p(metrics['cnt7'])-.25*np.log1p(metrics['cnt28']+1)\n# scorer\nfor name,s in metrics.items():\n order=np.lexsort((np.arange(n),-metrics.get('cnt28',s),-s))\n ranked={}\n cols=order\n for r,uu in enumerate(u):\n ok=((pack[r,cols//8]>>(cols%8))&1).astype(bool)\n ranked[int(uu)]=ids[cols[ok][:100]].astype(int).tolist()\n q=score_rankings(ranked,truth)['quality_ndcg@10']\n print(name,q,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cnt1 0.016755\ncnt2 0.020697\ncnt3 0.025721\nuniq3 0.031607\ncnt4 0.027354\ncnt5 0.027302\ncnt6 0.027538\ncnt7 0.028131\nuniq7 0.030373\ncnt8 0.026585\ncnt10 0.025154\ncnt12 0.0262\ncnt14 0.022878\nuniq14 0.025464\ncnt21 0.016279\ncnt28 0.012967\nuniq28 0.019078\ncnt42 0.01594\ncnt60 0.015826\nuniq60 0.017372\ncnt90 0.015822\newm1 0.025121\newm2 0.026281\newm3 0.027438\newm5 0.026567\newm7 0.022218\newm10 0.018984\newm14 0.017692\newm21 0.016336\newm30 0.016117\nblend7_28 0.027947\nblend3_14 0.026679\ntrend7 0.027898\n", "stderr": "", "aggregated_output": "cnt1 0.016755\ncnt2 0.020697\ncnt3 0.025721\nuniq3 0.031607\ncnt4 0.027354\ncnt5 0.027302\ncnt6 0.027538\ncnt7 0.028131\nuniq7 0.030373\ncnt8 0.026585\ncnt10 0.025154\ncnt12 0.0262\ncnt14 0.022878\nuniq14 0.025464\ncnt21 0.016279\ncnt28 0.012967\nuniq28 0.019078\ncnt42 0.01594\ncnt60 0.015826\nuniq60 0.017372\ncnt90 0.015822\newm1 0.025121\newm2 0.026281\newm3 0.027438\newm5 0.026567\newm7 0.022218\newm10 0.018984\newm14 0.017692\newm21 0.016336\newm30 0.016117\nblend7_28 0.027947\nblend3_14 0.026679\ntrend7 0.027898\n", "exit_code": 0, "duration": { "secs": 74, "nanos": 366529067 }, "formatted_output": "cnt1 0.016755\ncnt2 0.020697\ncnt3 0.025721\nuniq3 0.031607\ncnt4 0.027354\ncnt5 0.027302\ncnt6 0.027538\ncnt7 0.028131\nuniq7 0.030373\ncnt8 0.026585\ncnt10 0.025154\ncnt12 0.0262\ncnt14 0.022878\nuniq14 0.025464\ncnt21 0.016279\ncnt28 0.012967\nuniq28 0.019078\ncnt42 0.01594\ncnt60 0.015826\nuniq60 0.017372\ncnt90 0.015822\newm1 0.025121\newm2 0.026281\newm3 0.027438\newm5 0.026567\newm7 0.022218\newm10 0.018984\newm14 0.017692\newm21 0.016336\newm30 0.016117\nblend7_28 0.027947\nblend3_14 0.026679\ntrend7 0.027898\n" } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-25T01:49:16.186Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:49:16.186Z", "text": "" } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-25T01:49:17.481Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:49:17.481Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:49:17.972Z", "text": "A second diagnostic found a stronger production-safe prior: the number of distinct organic listeners in the last three days scores 0.031607 by itself, versus 0.028131 for raw seven-day play count. This is not label leakage—it is computed solely from pre-cutoff history—and it reduces domination by repeat-heavy tracks. I’m replacing the request-time trend coordinate with this distinct-listener signal and recalibrating it with the learned taste score." }, { "kind": "tool", "timestamp": "2026-09-25T01:49:19.821Z", "callId": "call_kjJ6FC6aCY7uy7BV3yCCUcN4", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_rank.py\\n@@\\n def rank_model(\\n@@\\n alpha: float,\\n threads: int,\\n+ trend_override: Path | None = None,\\n ) -> None:\\n@@\\n model, _ = load_model(checkpoint)\\n@@\\n item_ids = torch.arange(len(cache[\\\"catalog\\\"]), dtype=torch.long)\\n+ request_trend = tensors[\\\"trend\\\"][inference_ci]\\n+ if trend_override is not None:\\n+ overridden = np.load(trend_override, allow_pickle=False)\\n+ if overridden.shape != (len(cache[\\\"catalog\\\"]),):\\n+ raise ValueError(\\\"trend override has the wrong catalog shape\\\")\\n+ request_trend = torch.from_numpy(overridden.astype(np.float32, copy=False))\\n with torch.inference_mode():\\n item_vec = model.encode_item(\\n item_ids,\\n tensors[\\\"item_features\\\"][inference_ci],\\n- tensors[\\\"trend\\\"][inference_ci],\\n+ request_trend,\\n alpha,\\n )\\n@@\\n parser.add_argument(\\\"--checkpoint\\\", type=Path)\\n parser.add_argument(\\\"--init-checkpoint\\\", type=Path)\\n+ parser.add_argument(\\\"--trend-override\\\", type=Path)\\n@@\\n- rank_model(cache, checkpoint, output, args.alpha, args.threads)\\n+ rank_model(\\n+ cache,\\n+ checkpoint,\\n+ output,\\n+ args.alpha,\\n+ args.threads,\\n+ args.trend_override,\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:49:19.935Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:49:19.830Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a7a13d1e-c91f-4fa4-8a98-dacf02238839", "changes": { "/app/solution/train_and_rank.py": { "type": "update", "unified_diff": "@@ -675,2 +675,3 @@\n threads: int,\n+ trend_override: Path | None = None,\n ) -> None:\n@@ -685,2 +686,8 @@\n item_ids = torch.arange(len(cache[\"catalog\"]), dtype=torch.long)\n+ request_trend = tensors[\"trend\"][inference_ci]\n+ if trend_override is not None:\n+ overridden = np.load(trend_override, allow_pickle=False)\n+ if overridden.shape != (len(cache[\"catalog\"]),):\n+ raise ValueError(\"trend override has the wrong catalog shape\")\n+ request_trend = torch.from_numpy(overridden.astype(np.float32, copy=False))\n with torch.inference_mode():\n@@ -689,3 +696,3 @@\n tensors[\"item_features\"][inference_ci],\n- tensors[\"trend\"][inference_ci],\n+ request_trend,\n alpha,\n@@ -754,2 +761,3 @@\n parser.add_argument(\"--init-checkpoint\", type=Path)\n+ parser.add_argument(\"--trend-override\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n@@ -791,3 +799,10 @@\n )\n- rank_model(cache, checkpoint, output, args.alpha, args.threads)\n+ rank_model(\n+ cache,\n+ checkpoint,\n+ output,\n+ args.alpha,\n+ args.threads,\n+ args.trend_override,\n+ )\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:49:19.933Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d309fc1887d2bb7286da8ddf0bb6", "usage": { "input_tokens": 81606, "cached_input_tokens": 81280, "cache_write_input_tokens": 0, "output_tokens": 1118, "reasoning_output_tokens": 624, "total_tokens": 82724 }, "turn_token_usage": { "input_tokens": 3480502, "cached_input_tokens": 3398400, "cache_write_input_tokens": 0, "output_tokens": 43374, "reasoning_output_tokens": 20073, "total_tokens": 3523876 }, "thread_token_usage": { "input_tokens": 3480502, "cached_input_tokens": 3398400, "cache_write_input_tokens": 0, "output_tokens": 43374, "reasoning_output_tokens": 20073, "total_tokens": 3523876 } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-25T01:49:32.665Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:49:32.665Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:49:50.865Z", "callId": "call_trruumgqyNQACWuWoIcAVlHW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nD=Path('/task/data'); cutoff=24786800\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\\ndef cnt(d,uniq=False):\\n y=x[x.timestamp>=cutoff-d*86400]\\n if uniq:y=y.drop_duplicates(['uid','i'])\\n return np.bincount(y.i,minlength=n).astype(float)\\nu={d:cnt(d,True) for d in [1,2,3,4,5,7,10,14,28]}; c={d:cnt(d,False) for d in [3,7,28]}\\nwith np.load(D/'validation_eligible_candidates.npz') as z: users=z['uids'].copy(); pack=z['packed_eligible'].copy()\\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\ndef eval_order(name,order):\\n ranked={}\\n for r,uid in enumerate(users):\\n ok=((pack[r,order//8]>>(order%8))&1).astype(bool); ranked[int(uid)]=ids[order[ok][:100]].astype(int).tolist()\\n print(name,score_rankings(ranked,truth)['quality_ndcg@10'])\\n# lex variants\\nsecondaries={'u1':u[1],'u2':u[2],'u4':u[4],'u5':u[5],'u7':u[7],'u14':u[14],'u28':u[28],'c3':c[3],'c7':c[7],'c28':c[28],'catalog':-np.arange(n)}\\nfor name,s in secondaries.items(): eval_order('u3_then_'+name,np.lexsort((np.arange(n),-s,-u[3])))\\nfor b in [.02,.05,.1,.2,.3,.5,1.0]: eval_order('u3_plus_'+str(b)+'u7',np.lexsort((np.arange(n),-(u[3]+b*u[7]))))\\nfor b in [.05,.1,.2,.5]: eval_order('logblend'+str(b),np.lexsort((np.arange(n),-(np.log1p(u[3])+b*np.log1p(u[7])))))\\n# Save several normalized rank-coordinate overrides.\\nfor name,order in {\\n 'unique3_c28':np.lexsort((np.arange(n),-c[28],-u[3])),\\n 'unique3_u7':np.lexsort((np.arange(n),-u[7],-u[3])),\\n 'unique3p05u7':np.lexsort((np.arange(n),-(u[3]+.05*u[7]))),\\n}.items():\\n rank=np.empty(n,np.int32); rank[order]=np.arange(n); score=-np.log1p(rank.astype(np.float32)); score=(score-score.mean())/(score.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',score.astype(np.float32))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:50:21.034Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"bff420\",\"wall_time_seconds\":30.001079668,\"session_id\":58291,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:49:51.002Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d321b0f087d280aaefb3bbb793da", "usage": { "input_tokens": 82748, "cached_input_tokens": 81408, "cache_write_input_tokens": 0, "output_tokens": 1170, "reasoning_output_tokens": 293, "total_tokens": 83918 }, "turn_token_usage": { "input_tokens": 3563250, "cached_input_tokens": 3479808, "cache_write_input_tokens": 0, "output_tokens": 44544, "reasoning_output_tokens": 20366, "total_tokens": 3607794 }, "thread_token_usage": { "input_tokens": 3563250, "cached_input_tokens": 3479808, "cache_write_input_tokens": 0, "output_tokens": 44544, "reasoning_output_tokens": 20366, "total_tokens": 3607794 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-25T01:50:22.877Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:50:22.877Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:50:25.448Z", "callId": "call_Q3keuUaGwQ9sP698dg3zb6B9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":58291,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:50:42.210Z", "content": "Script completed\nWall time 16.8 seconds\nOutput:\n\n{\"chunk_id\":\"a52d9a\",\"wall_time_seconds\":16.746894577,\"exit_code\":0,\"original_token_count\":119,\"output\":\"u3_then_u1 0.031612\\nu3_then_u2 0.031632\\nu3_then_u4 0.031592\\nu3_then_u5 0.03155\\nu3_then_u7 0.031541\\nu3_then_u14 0.031594\\nu3_then_u28 0.031587\\nu3_then_c3 0.031526\\nu3_then_c7 0.031541\\nu3_then_c28 0.031607\\nu3_then_catalog 0.031654\\nu3_plus_0.02u7 0.031546\\nu3_plus_0.05u7 0.031461\\nu3_plus_0.1u7 0.031541\\nu3_plus_0.2u7 0.031494\\nu3_plus_0.3u7 0.03144\\nu3_plus_0.5u7 0.031463\\nu3_plus_1.0u7 0.030646\\nlogblend0.05 0.031545\\nlogblend0.1 0.031477\\nlogblend0.2 0.031537\\nlogblend0.5 0.031472\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:50:25.683Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d35d394487d290209488084747a5", "usage": { "input_tokens": 83975, "cached_input_tokens": 82560, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 11, "total_tokens": 84034 }, "turn_token_usage": { "input_tokens": 3647225, "cached_input_tokens": 3562368, "cache_write_input_tokens": 0, "output_tokens": 44603, "reasoning_output_tokens": 20377, "total_tokens": 3691828 }, "thread_token_usage": { "input_tokens": 3647225, "cached_input_tokens": 3562368, "cache_write_input_tokens": 0, "output_tokens": 44603, "reasoning_output_tokens": 20377, "total_tokens": 3691828 } } }, { "kind": "context", "timestamp": "2026-09-25T01:50:42.203Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da5adb84-734e-49a8-9e91-e9e34632546f", "process_id": "58291", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nD=Path('/task/data'); cutoff=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\ndef cnt(d,uniq=False):\n y=x[x.timestamp>=cutoff-d*86400]\n if uniq:y=y.drop_duplicates(['uid','i'])\n return np.bincount(y.i,minlength=n).astype(float)\nu={d:cnt(d,True) for d in [1,2,3,4,5,7,10,14,28]}; c={d:cnt(d,False) for d in [3,7,28]}\nwith np.load(D/'validation_eligible_candidates.npz') as z: users=z['uids'].copy(); pack=z['packed_eligible'].copy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\ndef eval_order(name,order):\n ranked={}\n for r,uid in enumerate(users):\n ok=((pack[r,order//8]>>(order%8))&1).astype(bool); ranked[int(uid)]=ids[order[ok][:100]].astype(int).tolist()\n print(name,score_rankings(ranked,truth)['quality_ndcg@10'])\n# lex variants\nsecondaries={'u1':u[1],'u2':u[2],'u4':u[4],'u5':u[5],'u7':u[7],'u14':u[14],'u28':u[28],'c3':c[3],'c7':c[7],'c28':c[28],'catalog':-np.arange(n)}\nfor name,s in secondaries.items(): eval_order('u3_then_'+name,np.lexsort((np.arange(n),-s,-u[3])))\nfor b in [.02,.05,.1,.2,.3,.5,1.0]: eval_order('u3_plus_'+str(b)+'u7',np.lexsort((np.arange(n),-(u[3]+b*u[7]))))\nfor b in [.05,.1,.2,.5]: eval_order('logblend'+str(b),np.lexsort((np.arange(n),-(np.log1p(u[3])+b*np.log1p(u[7])))))\n# Save several normalized rank-coordinate overrides.\nfor name,order in {\n 'unique3_c28':np.lexsort((np.arange(n),-c[28],-u[3])),\n 'unique3_u7':np.lexsort((np.arange(n),-u[7],-u[3])),\n 'unique3p05u7':np.lexsort((np.arange(n),-(u[3]+.05*u[7]))),\n}.items():\n rank=np.empty(n,np.int32); rank[order]=np.arange(n); score=-np.log1p(rank.astype(np.float32)); score=(score-score.mean())/(score.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',score.astype(np.float32))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nD=Path('/task/data'); cutoff=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\ndef cnt(d,uniq=False):\n y=x[x.timestamp>=cutoff-d*86400]\n if uniq:y=y.drop_duplicates(['uid','i'])\n return np.bincount(y.i,minlength=n).astype(float)\nu={d:cnt(d,True) for d in [1,2,3,4,5,7,10,14,28]}; c={d:cnt(d,False) for d in [3,7,28]}\nwith np.load(D/'validation_eligible_candidates.npz') as z: users=z['uids'].copy(); pack=z['packed_eligible'].copy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\ndef eval_order(name,order):\n ranked={}\n for r,uid in enumerate(users):\n ok=((pack[r,order//8]>>(order%8))&1).astype(bool); ranked[int(uid)]=ids[order[ok][:100]].astype(int).tolist()\n print(name,score_rankings(ranked,truth)['quality_ndcg@10'])\n# lex variants\nsecondaries={'u1':u[1],'u2':u[2],'u4':u[4],'u5':u[5],'u7':u[7],'u14':u[14],'u28':u[28],'c3':c[3],'c7':c[7],'c28':c[28],'catalog':-np.arange(n)}\nfor name,s in secondaries.items(): eval_order('u3_then_'+name,np.lexsort((np.arange(n),-s,-u[3])))\nfor b in [.02,.05,.1,.2,.3,.5,1.0]: eval_order('u3_plus_'+str(b)+'u7',np.lexsort((np.arange(n),-(u[3]+b*u[7]))))\nfor b in [.05,.1,.2,.5]: eval_order('logblend'+str(b),np.lexsort((np.arange(n),-(np.log1p(u[3])+b*np.log1p(u[7])))))\n# Save several normalized rank-coordinate overrides.\nfor name,order in {\n 'unique3_c28':np.lexsort((np.arange(n),-c[28],-u[3])),\n 'unique3_u7':np.lexsort((np.arange(n),-u[7],-u[3])),\n 'unique3p05u7':np.lexsort((np.arange(n),-(u[3]+.05*u[7]))),\n}.items():\n rank=np.empty(n,np.int32); rank[order]=np.arange(n); score=-np.log1p(rank.astype(np.float32)); score=(score-score.mean())/(score.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',score.astype(np.float32))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "u3_then_u1 0.031612\nu3_then_u2 0.031632\nu3_then_u4 0.031592\nu3_then_u5 0.03155\nu3_then_u7 0.031541\nu3_then_u14 0.031594\nu3_then_u28 0.031587\nu3_then_c3 0.031526\nu3_then_c7 0.031541\nu3_then_c28 0.031607\nu3_then_catalog 0.031654\nu3_plus_0.02u7 0.031546\nu3_plus_0.05u7 0.031461\nu3_plus_0.1u7 0.031541\nu3_plus_0.2u7 0.031494\nu3_plus_0.3u7 0.03144\nu3_plus_0.5u7 0.031463\nu3_plus_1.0u7 0.030646\nlogblend0.05 0.031545\nlogblend0.1 0.031477\nlogblend0.2 0.031537\nlogblend0.5 0.031472\n", "stderr": "", "aggregated_output": "u3_then_u1 0.031612\nu3_then_u2 0.031632\nu3_then_u4 0.031592\nu3_then_u5 0.03155\nu3_then_u7 0.031541\nu3_then_u14 0.031594\nu3_then_u28 0.031587\nu3_then_c3 0.031526\nu3_then_c7 0.031541\nu3_then_c28 0.031607\nu3_then_catalog 0.031654\nu3_plus_0.02u7 0.031546\nu3_plus_0.05u7 0.031461\nu3_plus_0.1u7 0.031541\nu3_plus_0.2u7 0.031494\nu3_plus_0.3u7 0.03144\nu3_plus_0.5u7 0.031463\nu3_plus_1.0u7 0.030646\nlogblend0.05 0.031545\nlogblend0.1 0.031477\nlogblend0.2 0.031537\nlogblend0.5 0.031472\n", "exit_code": 0, "duration": { "secs": 51, "nanos": 170268777 }, "formatted_output": "u3_then_u1 0.031612\nu3_then_u2 0.031632\nu3_then_u4 0.031592\nu3_then_u5 0.03155\nu3_then_u7 0.031541\nu3_then_u14 0.031594\nu3_then_u28 0.031587\nu3_then_c3 0.031526\nu3_then_c7 0.031541\nu3_then_c28 0.031607\nu3_then_catalog 0.031654\nu3_plus_0.02u7 0.031546\nu3_plus_0.05u7 0.031461\nu3_plus_0.1u7 0.031541\nu3_plus_0.2u7 0.031494\nu3_plus_0.3u7 0.03144\nu3_plus_0.5u7 0.031463\nu3_plus_1.0u7 0.030646\nlogblend0.05 0.031545\nlogblend0.1 0.031477\nlogblend0.2 0.031537\nlogblend0.5 0.031472\n" } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-25T01:50:49.266Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:50:49.266Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:50:52.721Z", "callId": "call_csonS8nDTpKVd839ZChIg6R4", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.25 0.5 0.75 1 1.5 2 3 5 8; do out=/app/solution/v2_unique3_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --trend-override /app/solution/trend_unique3_c28_validation.npy --alpha \\\"$a\\\" --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"alpha=$a $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:51:22.888Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"92c7b6\",\"wall_time_seconds\":30.001214839,\"session_id\":70921,\"original_token_count\":33,\"output\":\"alpha=0.25 \\\"quality_ndcg@10\\\": 0.024174,\\nalpha=0.5 \\\"quality_ndcg@10\\\": 0.033636,\\nalpha=0.75 \\\"quality_ndcg@10\\\": 0.035259,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:50:52.804Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d3726b0087d28d391e13bb70b011", "usage": { "input_tokens": 84360, "cached_input_tokens": 83840, "cache_write_input_tokens": 0, "output_tokens": 277, "reasoning_output_tokens": 76, "total_tokens": 84637 }, "turn_token_usage": { "input_tokens": 3731585, "cached_input_tokens": 3646208, "cache_write_input_tokens": 0, "output_tokens": 44880, "reasoning_output_tokens": 20453, "total_tokens": 3776465 }, "thread_token_usage": { "input_tokens": 3731585, "cached_input_tokens": 3646208, "cache_write_input_tokens": 0, "output_tokens": 44880, "reasoning_output_tokens": 20453, "total_tokens": 3776465 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-25T01:51:24.077Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:51:24.077Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:51:24.808Z", "callId": "call_KpwQiP9e0Jkx5IrlH0t3lJdF", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":70921,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:51:54.818Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"4e579b\",\"wall_time_seconds\":30.002198423,\"session_id\":70921,\"original_token_count\":42,\"output\":\"alpha=1 \\\"quality_ndcg@10\\\": 0.035604,\\nalpha=1.5 \\\"quality_ndcg@10\\\": 0.034809,\\nalpha=2 \\\"quality_ndcg@10\\\": 0.034337,\\nalpha=3 \\\"quality_ndcg@10\\\": 0.033684,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:51:24.903Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d39b11a487d2b26fb008233486ef", "usage": { "input_tokens": 84755, "cached_input_tokens": 84224, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 8, "total_tokens": 84811 }, "turn_token_usage": { "input_tokens": 3816340, "cached_input_tokens": 3730432, "cache_write_input_tokens": 0, "output_tokens": 44936, "reasoning_output_tokens": 20461, "total_tokens": 3861276 }, "thread_token_usage": { "input_tokens": 3816340, "cached_input_tokens": 3730432, "cache_write_input_tokens": 0, "output_tokens": 44936, "reasoning_output_tokens": 20461, "total_tokens": 3861276 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:52:01.572Z", "callId": "call_7j9sTZEI6RsxY8MAPPteggS9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":70921,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:52:06.700Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\n{\"chunk_id\":\"9fd8cf\",\"wall_time_seconds\":5.111535661,\"exit_code\":0,\"original_token_count\":21,\"output\":\"alpha=5 \\\"quality_ndcg@10\\\": 0.033026,\\nalpha=8 \\\"quality_ndcg@10\\\": 0.032329,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:52:01.731Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d3bde30087d2bcd0dd675cae88ef", "usage": { "input_tokens": 84942, "cached_input_tokens": 84608, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 84988 }, "turn_token_usage": { "input_tokens": 3901282, "cached_input_tokens": 3815040, "cache_write_input_tokens": 0, "output_tokens": 44982, "reasoning_output_tokens": 20461, "total_tokens": 3946264 }, "thread_token_usage": { "input_tokens": 3901282, "cached_input_tokens": 3815040, "cache_write_input_tokens": 0, "output_tokens": 44982, "reasoning_output_tokens": 20461, "total_tokens": 3946264 } } }, { "kind": "context", "timestamp": "2026-09-25T01:52:06.692Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-98d1c42d-e1ff-4373-bd22-e6f4e4b0335d", "process_id": "70921", "command": [ "/bin/sh", "-lc", "for a in 0.25 0.5 0.75 1 1.5 2 3 5 8; do out=/app/solution/v2_unique3_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --trend-override /app/solution/trend_unique3_c28_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.25 0.5 0.75 1 1.5 2 3 5 8; do out=/app/solution/v2_unique3_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v2_semantic.pt --trend-override /app/solution/trend_unique3_c28_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "alpha=0.25 \"quality_ndcg@10\": 0.024174,\nalpha=0.5 \"quality_ndcg@10\": 0.033636,\nalpha=0.75 \"quality_ndcg@10\": 0.035259,\nalpha=1 \"quality_ndcg@10\": 0.035604,\nalpha=1.5 \"quality_ndcg@10\": 0.034809,\nalpha=2 \"quality_ndcg@10\": 0.034337,\nalpha=3 \"quality_ndcg@10\": 0.033684,\nalpha=5 \"quality_ndcg@10\": 0.033026,\nalpha=8 \"quality_ndcg@10\": 0.032329,\n", "stderr": "", "aggregated_output": "alpha=0.25 \"quality_ndcg@10\": 0.024174,\nalpha=0.5 \"quality_ndcg@10\": 0.033636,\nalpha=0.75 \"quality_ndcg@10\": 0.035259,\nalpha=1 \"quality_ndcg@10\": 0.035604,\nalpha=1.5 \"quality_ndcg@10\": 0.034809,\nalpha=2 \"quality_ndcg@10\": 0.034337,\nalpha=3 \"quality_ndcg@10\": 0.033684,\nalpha=5 \"quality_ndcg@10\": 0.033026,\nalpha=8 \"quality_ndcg@10\": 0.032329,\n", "exit_code": 0, "duration": { "secs": 73, "nanos": 805845020 }, "formatted_output": "alpha=0.25 \"quality_ndcg@10\": 0.024174,\nalpha=0.5 \"quality_ndcg@10\": 0.033636,\nalpha=0.75 \"quality_ndcg@10\": 0.035259,\nalpha=1 \"quality_ndcg@10\": 0.035604,\nalpha=1.5 \"quality_ndcg@10\": 0.034809,\nalpha=2 \"quality_ndcg@10\": 0.034337,\nalpha=3 \"quality_ndcg@10\": 0.033684,\nalpha=5 \"quality_ndcg@10\": 0.033026,\nalpha=8 \"quality_ndcg@10\": 0.032329,\n" } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-25T01:52:17.808Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:52:17.808Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:52:19.298Z", "text": "Using distinct-listener trend plus the same neural checkpoint raises public NDCG@10 to 0.035604—about 27% above raw popularity and 13% above the improved trend-only baseline. I’m preserving that ranking as the current best while testing only low-risk calibration variants and additional semantic training." }, { "kind": "tool", "timestamp": "2026-09-25T01:52:28.596Z", "callId": "call_Jfv23D35pYTgkFX2S4gNDfTK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data'; cutoff=24786800\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.timestamp>=cutoff-7*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\\ndef uniq(d):\\n y=x[x.timestamp>=cutoff-d*86400].drop_duplicates(['uid','i']); return np.bincount(y.i,minlength=n).astype(np.float32)\\nu3,u7=uniq(3),uniq(7)\\nprint('u3 quantiles',np.percentile(u3,[0,25,50,75,90,95,99,99.5,99.9,100])); print('top',np.sort(u3)[-20:])\\norder=np.lexsort((np.arange(n),-u7,-u3)); rank=np.empty(n,np.int32); rank[order]=np.arange(n)\\nraws={\\n 'u3_log':np.log1p(u3)+1e-4*np.log1p(u7),\\n 'u3_sqrt':np.sqrt(u3)+1e-4*np.sqrt(u7),\\n 'u3_raw':u3+1e-4*u7,\\n 'u3_ranklog':-np.log1p(rank.astype(np.float32)),\\n 'u3_rankroot':-np.sqrt(rank.astype(np.float32)),\\n 'u3_ranklinear':-rank.astype(np.float32),\\n 'u3_logblend01':np.log1p(u3)+.1*np.log1p(u7),\\n}\\nfor name,v in raws.items():\\n v=(v-v.mean())/(v.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',v.astype(np.float32))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:52:35.331Z", "content": "Script completed\nWall time 6.7 seconds\nOutput:\n\nu3 quantiles [ 0. 0. 1. 3. 6. 10. 25. 36. 83.001\n 204. ]\ntop [ 92. 94. 94. 103. 110. 112. 116. 118. 124. 125. 131. 134. 135. 136.\n 137. 150. 151. 160. 179. 204.]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:52:28.712Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d3c6f60c87d2a57896675fac2f50", "usage": { "input_tokens": 85080, "cached_input_tokens": 84736, "cache_write_input_tokens": 0, "output_tokens": 1109, "reasoning_output_tokens": 498, "total_tokens": 86189 }, "turn_token_usage": { "input_tokens": 3986362, "cached_input_tokens": 3899776, "cache_write_input_tokens": 0, "output_tokens": 46091, "reasoning_output_tokens": 20959, "total_tokens": 4032453 }, "thread_token_usage": { "input_tokens": 3986362, "cached_input_tokens": 3899776, "cache_write_input_tokens": 0, "output_tokens": 46091, "reasoning_output_tokens": 20959, "total_tokens": 4032453 } } }, { "kind": "context", "timestamp": "2026-09-25T01:52:35.320Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dab925ee-c7db-4871-9d18-b725087cd3cc", "process_id": "50897", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cutoff=24786800\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.timestamp>=cutoff-7*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\ndef uniq(d):\n y=x[x.timestamp>=cutoff-d*86400].drop_duplicates(['uid','i']); return np.bincount(y.i,minlength=n).astype(np.float32)\nu3,u7=uniq(3),uniq(7)\nprint('u3 quantiles',np.percentile(u3,[0,25,50,75,90,95,99,99.5,99.9,100])); print('top',np.sort(u3)[-20:])\norder=np.lexsort((np.arange(n),-u7,-u3)); rank=np.empty(n,np.int32); rank[order]=np.arange(n)\nraws={\n 'u3_log':np.log1p(u3)+1e-4*np.log1p(u7),\n 'u3_sqrt':np.sqrt(u3)+1e-4*np.sqrt(u7),\n 'u3_raw':u3+1e-4*u7,\n 'u3_ranklog':-np.log1p(rank.astype(np.float32)),\n 'u3_rankroot':-np.sqrt(rank.astype(np.float32)),\n 'u3_ranklinear':-rank.astype(np.float32),\n 'u3_logblend01':np.log1p(u3)+.1*np.log1p(u7),\n}\nfor name,v in raws.items():\n v=(v-v.mean())/(v.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',v.astype(np.float32))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cutoff=24786800\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); mp={int(v):i for i,v in enumerate(ids)}\nx=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[(x.timestamp>=cutoff-7*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)&x.item_id.isin(set(ids))]; x['i']=x.item_id.map(mp).astype(np.int32)\ndef uniq(d):\n y=x[x.timestamp>=cutoff-d*86400].drop_duplicates(['uid','i']); return np.bincount(y.i,minlength=n).astype(np.float32)\nu3,u7=uniq(3),uniq(7)\nprint('u3 quantiles',np.percentile(u3,[0,25,50,75,90,95,99,99.5,99.9,100])); print('top',np.sort(u3)[-20:])\norder=np.lexsort((np.arange(n),-u7,-u3)); rank=np.empty(n,np.int32); rank[order]=np.arange(n)\nraws={\n 'u3_log':np.log1p(u3)+1e-4*np.log1p(u7),\n 'u3_sqrt':np.sqrt(u3)+1e-4*np.sqrt(u7),\n 'u3_raw':u3+1e-4*u7,\n 'u3_ranklog':-np.log1p(rank.astype(np.float32)),\n 'u3_rankroot':-np.sqrt(rank.astype(np.float32)),\n 'u3_ranklinear':-rank.astype(np.float32),\n 'u3_logblend01':np.log1p(u3)+.1*np.log1p(u7),\n}\nfor name,v in raws.items():\n v=(v-v.mean())/(v.std()+1e-6); np.save('/app/solution/trend_'+name+'_validation.npy',v.astype(np.float32))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "u3 quantiles [ 0. 0. 1. 3. 6. 10. 25. 36. 83.001\n 204. ]\ntop [ 92. 94. 94. 103. 110. 112. 116. 118. 124. 125. 131. 134. 135. 136.\n 137. 150. 151. 160. 179. 204.]\n", "stderr": "", "aggregated_output": "u3 quantiles [ 0. 0. 1. 3. 6. 10. 25. 36. 83.001\n 204. ]\ntop [ 92. 94. 94. 103. 110. 112. 116. 118. 124. 125. 131. 134. 135. 136.\n 137. 150. 151. 160. 179. 204.]\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 560018009 }, "formatted_output": "u3 quantiles [ 0. 0. 1. 3. 6. 10. 25. 36. 83.001\n 204. ]\ntop [ 92. 94. 94. 103. 110. 112. 116. 118. 124. 125. 131. 134. 135. 136.\n 137. 150. 151. 160. 179. 204.]\n" } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-25T01:52:45.515Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:52:45.515Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:52:58.251Z", "callId": "call_j0pn9IntuMCjtEDZBCo7Y5CI", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path[:0]=['/app/solution','/task']\\nfrom pathlib import Path\\nimport numpy as np,torch,pandas as pd\\nfrom train_and_rank import load_cache,cache_tensors,load_model\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data'); O=Path('/app/solution'); torch.set_num_threads(8)\\nc=load_cache(O/'cache_validation_w28.npz'); t=cache_tensors(c); m,_=load_model(O/'two_tower_v2_semantic.pt'); nusers=len(c['users']); ci=len(c['cutoffs']); pids=ci*nusers+c['target_ui'].astype(np.int64)\\nwith torch.inference_mode():\\n ids=torch.arange(len(c['catalog'])); ivec=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.0)\\n us=[]\\n for s in range(0,len(pids),512):\\n p=torch.from_numpy(pids[s:s+512]); us.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\\n uvec=torch.cat(us)\\nwith np.load(D/'validation_eligible_candidates.npz') as z: pack=z['packed_eligible'].copy(); uids=z['uids'].copy()\\nnames=['u3_log','u3_sqrt','u3_raw','u3_ranklog','u3_rankroot','u3_ranklinear','u3_logblend01','unique3_c28']\\nalphas=[.4,.55,.7,.85,1.,1.2,1.5]\\ntrends={n:torch.from_numpy(np.load(O/f'trend_{n}_validation.npy')) for n in names}\\ntops={(n,a):np.empty((len(uids),10),np.int32) for n in names for a in alphas}\\nwith torch.inference_mode():\\n for s in range(0,len(uids),256):\\n e=min(s+256,len(uids)); base=uvec[s:e]@ivec.T\\n mask=np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool); mt=torch.from_numpy(mask)\\n for n in names:\\n tr=trends[n]\\n for a in alphas:\\n score=base+a*tr.unsqueeze(0); score.masked_fill_(~mt,-torch.inf); tops[(n,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\\n print('chunk',e,flush=True)\\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\nres=[]\\nfor key,top in tops.items():\\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)}\\n q=score_rankings(ranked,truth)['quality_ndcg@10']; res.append((q,*key))\\nfor row in sorted(res,reverse=True):print(row)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:53:22.522Z", "content": "Script completed\nWall time 24.3 seconds\nOutput:\n\n{\"chunk_id\":\"70c458\",\"wall_time_seconds\":24.081821716,\"exit_code\":0,\"original_token_count\":511,\"output\":\"chunk 256\\nchunk 512\\nchunk 768\\nchunk 1024\\nchunk 1280\\nchunk 1536\\nchunk 1792\\nchunk 2048\\nchunk 2304\\nchunk 2560\\nchunk 2816\\nchunk 3072\\nchunk 3328\\nchunk 3584\\nchunk 3840\\nchunk 4096\\nchunk 4352\\nchunk 4608\\nchunk 4864\\nchunk 5120\\nchunk 5376\\nchunk 5632\\nchunk 5888\\nchunk 6144\\nchunk 6400\\nchunk 6656\\nchunk 6912\\nchunk 7168\\nchunk 7424\\nchunk 7680\\nchunk 7936\\nchunk 8192\\nchunk 8448\\nchunk 8456\\n(0.036339, 'u3_sqrt', 0.7)\\n(0.035863, 'u3_sqrt', 0.55)\\n(0.035849, 'u3_sqrt', 0.85)\\n(0.035632, 'u3_sqrt', 1.0)\\n(0.035627, 'u3_ranklog', 0.85)\\n(0.035604, 'unique3_c28', 1.0)\\n(0.035563, 'unique3_c28', 0.85)\\n(0.035463, 'u3_ranklog', 1.2)\\n(0.035445, 'u3_ranklog', 1.0)\\n(0.035424, 'unique3_c28', 1.2)\\n(0.035306, 'unique3_c28', 0.7)\\n(0.035225, 'u3_sqrt', 1.2)\\n(0.035155, 'u3_ranklog', 0.7)\\n(0.035059, 'u3_logblend01', 1.5)\\n(0.035056, 'u3_log', 1.5)\\n(0.034831, 'u3_sqrt', 1.5)\\n(0.034831, 'u3_ranklog', 1.5)\\n(0.034809, 'unique3_c28', 1.5)\\n(0.034677, 'u3_raw', 0.4)\\n(0.034214, 'u3_sqrt', 0.4)\\n(0.034073, 'u3_log', 1.2)\\n(0.034047, 'u3_ranklog', 0.55)\\n(0.034044, 'unique3_c28', 0.55)\\n(0.034023, 'u3_logblend01', 1.2)\\n(0.033644, 'u3_raw', 0.55)\\n(0.033468, 'u3_raw', 0.7)\\n(0.03288, 'u3_log', 1.0)\\n(0.032832, 'u3_raw', 0.85)\\n(0.032545, 'u3_raw', 1.0)\\n(0.032435, 'u3_logblend01', 1.0)\\n(0.032377, 'u3_raw', 1.2)\\n(0.031994, 'u3_raw', 1.5)\\n(0.031814, 'u3_log', 0.85)\\n(0.031447, 'u3_logblend01', 0.85)\\n(0.030591, 'unique3_c28', 0.4)\\n(0.030469, 'u3_ranklog', 0.4)\\n(0.029475, 'u3_logblend01', 0.7)\\n(0.029387, 'u3_log', 0.7)\\n(0.026039, 'u3_logblend01', 0.55)\\n(0.025958, 'u3_log', 0.55)\\n(0.021888, 'u3_log', 0.4)\\n(0.021603, 'u3_logblend01', 0.4)\\n(0.019523, 'u3_rankroot', 1.5)\\n(0.018277, 'u3_rankroot', 1.2)\\n(0.017013, 'u3_rankroot', 1.0)\\n(0.015676, 'u3_rankroot', 0.85)\\n(0.014813, 'u3_rankroot', 0.7)\\n(0.013734, 'u3_rankroot', 0.55)\\n(0.012581, 'u3_rankroot', 0.4)\\n(0.012561, 'u3_ranklinear', 1.5)\\n(0.01193, 'u3_ranklinear', 1.2)\\n(0.011547, 'u3_ranklinear', 1.0)\\n(0.011269, 'u3_ranklinear', 0.85)\\n(0.010976, 'u3_ranklinear', 0.7)\\n(0.010696, 'u3_ranklinear', 0.55)\\n(0.010003, 'u3_ranklinear', 0.4)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:52:58.407Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d3e39cd487d291374eb8638b5dbc", "usage": { "input_tokens": 86324, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 1177, "reasoning_output_tokens": 418, "total_tokens": 87501 }, "turn_token_usage": { "input_tokens": 4072686, "cached_input_tokens": 3984640, "cache_write_input_tokens": 0, "output_tokens": 47268, "reasoning_output_tokens": 21377, "total_tokens": 4119954 }, "thread_token_usage": { "input_tokens": 4072686, "cached_input_tokens": 3984640, "cache_write_input_tokens": 0, "output_tokens": 47268, "reasoning_output_tokens": 21377, "total_tokens": 4119954 } } }, { "kind": "context", "timestamp": "2026-09-25T01:53:22.511Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5a524016-222a-4077-9ab8-9915fce6d48c", "process_id": "7460", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); O=Path('/app/solution'); torch.set_num_threads(8)\nc=load_cache(O/'cache_validation_w28.npz'); t=cache_tensors(c); m,_=load_model(O/'two_tower_v2_semantic.pt'); nusers=len(c['users']); ci=len(c['cutoffs']); pids=ci*nusers+c['target_ui'].astype(np.int64)\nwith torch.inference_mode():\n ids=torch.arange(len(c['catalog'])); ivec=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.0)\n us=[]\n for s in range(0,len(pids),512):\n p=torch.from_numpy(pids[s:s+512]); us.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\n uvec=torch.cat(us)\nwith np.load(D/'validation_eligible_candidates.npz') as z: pack=z['packed_eligible'].copy(); uids=z['uids'].copy()\nnames=['u3_log','u3_sqrt','u3_raw','u3_ranklog','u3_rankroot','u3_ranklinear','u3_logblend01','unique3_c28']\nalphas=[.4,.55,.7,.85,1.,1.2,1.5]\ntrends={n:torch.from_numpy(np.load(O/f'trend_{n}_validation.npy')) for n in names}\ntops={(n,a):np.empty((len(uids),10),np.int32) for n in names for a in alphas}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids)); base=uvec[s:e]@ivec.T\n mask=np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool); mt=torch.from_numpy(mask)\n for n in names:\n tr=trends[n]\n for a in alphas:\n score=base+a*tr.unsqueeze(0); score.masked_fill_(~mt,-torch.inf); tops[(n,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\n print('chunk',e,flush=True)\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nres=[]\nfor key,top in tops.items():\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)}\n q=score_rankings(ranked,truth)['quality_ndcg@10']; res.append((q,*key))\nfor row in sorted(res,reverse=True):print(row)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); O=Path('/app/solution'); torch.set_num_threads(8)\nc=load_cache(O/'cache_validation_w28.npz'); t=cache_tensors(c); m,_=load_model(O/'two_tower_v2_semantic.pt'); nusers=len(c['users']); ci=len(c['cutoffs']); pids=ci*nusers+c['target_ui'].astype(np.int64)\nwith torch.inference_mode():\n ids=torch.arange(len(c['catalog'])); ivec=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.0)\n us=[]\n for s in range(0,len(pids),512):\n p=torch.from_numpy(pids[s:s+512]); us.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\n uvec=torch.cat(us)\nwith np.load(D/'validation_eligible_candidates.npz') as z: pack=z['packed_eligible'].copy(); uids=z['uids'].copy()\nnames=['u3_log','u3_sqrt','u3_raw','u3_ranklog','u3_rankroot','u3_ranklinear','u3_logblend01','unique3_c28']\nalphas=[.4,.55,.7,.85,1.,1.2,1.5]\ntrends={n:torch.from_numpy(np.load(O/f'trend_{n}_validation.npy')) for n in names}\ntops={(n,a):np.empty((len(uids),10),np.int32) for n in names for a in alphas}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids)); base=uvec[s:e]@ivec.T\n mask=np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool); mt=torch.from_numpy(mask)\n for n in names:\n tr=trends[n]\n for a in alphas:\n score=base+a*tr.unsqueeze(0); score.masked_fill_(~mt,-torch.inf); tops[(n,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\n print('chunk',e,flush=True)\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nres=[]\nfor key,top in tops.items():\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)}\n q=score_rankings(ranked,truth)['quality_ndcg@10']; res.append((q,*key))\nfor row in sorted(res,reverse=True):print(row)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chunk 256\nchunk 512\nchunk 768\nchunk 1024\nchunk 1280\nchunk 1536\nchunk 1792\nchunk 2048\nchunk 2304\nchunk 2560\nchunk 2816\nchunk 3072\nchunk 3328\nchunk 3584\nchunk 3840\nchunk 4096\nchunk 4352\nchunk 4608\nchunk 4864\nchunk 5120\nchunk 5376\nchunk 5632\nchunk 5888\nchunk 6144\nchunk 6400\nchunk 6656\nchunk 6912\nchunk 7168\nchunk 7424\nchunk 7680\nchunk 7936\nchunk 8192\nchunk 8448\nchunk 8456\n(0.036339, 'u3_sqrt', 0.7)\n(0.035863, 'u3_sqrt', 0.55)\n(0.035849, 'u3_sqrt', 0.85)\n(0.035632, 'u3_sqrt', 1.0)\n(0.035627, 'u3_ranklog', 0.85)\n(0.035604, 'unique3_c28', 1.0)\n(0.035563, 'unique3_c28', 0.85)\n(0.035463, 'u3_ranklog', 1.2)\n(0.035445, 'u3_ranklog', 1.0)\n(0.035424, 'unique3_c28', 1.2)\n(0.035306, 'unique3_c28', 0.7)\n(0.035225, 'u3_sqrt', 1.2)\n(0.035155, 'u3_ranklog', 0.7)\n(0.035059, 'u3_logblend01', 1.5)\n(0.035056, 'u3_log', 1.5)\n(0.034831, 'u3_sqrt', 1.5)\n(0.034831, 'u3_ranklog', 1.5)\n(0.034809, 'unique3_c28', 1.5)\n(0.034677, 'u3_raw', 0.4)\n(0.034214, 'u3_sqrt', 0.4)\n(0.034073, 'u3_log', 1.2)\n(0.034047, 'u3_ranklog', 0.55)\n(0.034044, 'unique3_c28', 0.55)\n(0.034023, 'u3_logblend01', 1.2)\n(0.033644, 'u3_raw', 0.55)\n(0.033468, 'u3_raw', 0.7)\n(0.03288, 'u3_log', 1.0)\n(0.032832, 'u3_raw', 0.85)\n(0.032545, 'u3_raw', 1.0)\n(0.032435, 'u3_logblend01', 1.0)\n(0.032377, 'u3_raw', 1.2)\n(0.031994, 'u3_raw', 1.5)\n(0.031814, 'u3_log', 0.85)\n(0.031447, 'u3_logblend01', 0.85)\n(0.030591, 'unique3_c28', 0.4)\n(0.030469, 'u3_ranklog', 0.4)\n(0.029475, 'u3_logblend01', 0.7)\n(0.029387, 'u3_log', 0.7)\n(0.026039, 'u3_logblend01', 0.55)\n(0.025958, 'u3_log', 0.55)\n(0.021888, 'u3_log', 0.4)\n(0.021603, 'u3_logblend01', 0.4)\n(0.019523, 'u3_rankroot', 1.5)\n(0.018277, 'u3_rankroot', 1.2)\n(0.017013, 'u3_rankroot', 1.0)\n(0.015676, 'u3_rankroot', 0.85)\n(0.014813, 'u3_rankroot', 0.7)\n(0.013734, 'u3_rankroot', 0.55)\n(0.012581, 'u3_rankroot', 0.4)\n(0.012561, 'u3_ranklinear', 1.5)\n(0.01193, 'u3_ranklinear', 1.2)\n(0.011547, 'u3_ranklinear', 1.0)\n(0.011269, 'u3_ranklinear', 0.85)\n(0.010976, 'u3_ranklinear', 0.7)\n(0.010696, 'u3_ranklinear', 0.55)\n(0.010003, 'u3_ranklinear', 0.4)\n", "stderr": "", "aggregated_output": "chunk 256\nchunk 512\nchunk 768\nchunk 1024\nchunk 1280\nchunk 1536\nchunk 1792\nchunk 2048\nchunk 2304\nchunk 2560\nchunk 2816\nchunk 3072\nchunk 3328\nchunk 3584\nchunk 3840\nchunk 4096\nchunk 4352\nchunk 4608\nchunk 4864\nchunk 5120\nchunk 5376\nchunk 5632\nchunk 5888\nchunk 6144\nchunk 6400\nchunk 6656\nchunk 6912\nchunk 7168\nchunk 7424\nchunk 7680\nchunk 7936\nchunk 8192\nchunk 8448\nchunk 8456\n(0.036339, 'u3_sqrt', 0.7)\n(0.035863, 'u3_sqrt', 0.55)\n(0.035849, 'u3_sqrt', 0.85)\n(0.035632, 'u3_sqrt', 1.0)\n(0.035627, 'u3_ranklog', 0.85)\n(0.035604, 'unique3_c28', 1.0)\n(0.035563, 'unique3_c28', 0.85)\n(0.035463, 'u3_ranklog', 1.2)\n(0.035445, 'u3_ranklog', 1.0)\n(0.035424, 'unique3_c28', 1.2)\n(0.035306, 'unique3_c28', 0.7)\n(0.035225, 'u3_sqrt', 1.2)\n(0.035155, 'u3_ranklog', 0.7)\n(0.035059, 'u3_logblend01', 1.5)\n(0.035056, 'u3_log', 1.5)\n(0.034831, 'u3_sqrt', 1.5)\n(0.034831, 'u3_ranklog', 1.5)\n(0.034809, 'unique3_c28', 1.5)\n(0.034677, 'u3_raw', 0.4)\n(0.034214, 'u3_sqrt', 0.4)\n(0.034073, 'u3_log', 1.2)\n(0.034047, 'u3_ranklog', 0.55)\n(0.034044, 'unique3_c28', 0.55)\n(0.034023, 'u3_logblend01', 1.2)\n(0.033644, 'u3_raw', 0.55)\n(0.033468, 'u3_raw', 0.7)\n(0.03288, 'u3_log', 1.0)\n(0.032832, 'u3_raw', 0.85)\n(0.032545, 'u3_raw', 1.0)\n(0.032435, 'u3_logblend01', 1.0)\n(0.032377, 'u3_raw', 1.2)\n(0.031994, 'u3_raw', 1.5)\n(0.031814, 'u3_log', 0.85)\n(0.031447, 'u3_logblend01', 0.85)\n(0.030591, 'unique3_c28', 0.4)\n(0.030469, 'u3_ranklog', 0.4)\n(0.029475, 'u3_logblend01', 0.7)\n(0.029387, 'u3_log', 0.7)\n(0.026039, 'u3_logblend01', 0.55)\n(0.025958, 'u3_log', 0.55)\n(0.021888, 'u3_log', 0.4)\n(0.021603, 'u3_logblend01', 0.4)\n(0.019523, 'u3_rankroot', 1.5)\n(0.018277, 'u3_rankroot', 1.2)\n(0.017013, 'u3_rankroot', 1.0)\n(0.015676, 'u3_rankroot', 0.85)\n(0.014813, 'u3_rankroot', 0.7)\n(0.013734, 'u3_rankroot', 0.55)\n(0.012581, 'u3_rankroot', 0.4)\n(0.012561, 'u3_ranklinear', 1.5)\n(0.01193, 'u3_ranklinear', 1.2)\n(0.011547, 'u3_ranklinear', 1.0)\n(0.011269, 'u3_ranklinear', 0.85)\n(0.010976, 'u3_ranklinear', 0.7)\n(0.010696, 'u3_ranklinear', 0.55)\n(0.010003, 'u3_ranklinear', 0.4)\n", "exit_code": 0, "duration": { "secs": 24, "nanos": 81810095 }, "formatted_output": "chunk 256\nchunk 512\nchunk 768\nchunk 1024\nchunk 1280\nchunk 1536\nchunk 1792\nchunk 2048\nchunk 2304\nchunk 2560\nchunk 2816\nchunk 3072\nchunk 3328\nchunk 3584\nchunk 3840\nchunk 4096\nchunk 4352\nchunk 4608\nchunk 4864\nchunk 5120\nchunk 5376\nchunk 5632\nchunk 5888\nchunk 6144\nchunk 6400\nchunk 6656\nchunk 6912\nchunk 7168\nchunk 7424\nchunk 7680\nchunk 7936\nchunk 8192\nchunk 8448\nchunk 8456\n(0.036339, 'u3_sqrt', 0.7)\n(0.035863, 'u3_sqrt', 0.55)\n(0.035849, 'u3_sqrt', 0.85)\n(0.035632, 'u3_sqrt', 1.0)\n(0.035627, 'u3_ranklog', 0.85)\n(0.035604, 'unique3_c28', 1.0)\n(0.035563, 'unique3_c28', 0.85)\n(0.035463, 'u3_ranklog', 1.2)\n(0.035445, 'u3_ranklog', 1.0)\n(0.035424, 'unique3_c28', 1.2)\n(0.035306, 'unique3_c28', 0.7)\n(0.035225, 'u3_sqrt', 1.2)\n(0.035155, 'u3_ranklog', 0.7)\n(0.035059, 'u3_logblend01', 1.5)\n(0.035056, 'u3_log', 1.5)\n(0.034831, 'u3_sqrt', 1.5)\n(0.034831, 'u3_ranklog', 1.5)\n(0.034809, 'unique3_c28', 1.5)\n(0.034677, 'u3_raw', 0.4)\n(0.034214, 'u3_sqrt', 0.4)\n(0.034073, 'u3_log', 1.2)\n(0.034047, 'u3_ranklog', 0.55)\n(0.034044, 'unique3_c28', 0.55)\n(0.034023, 'u3_logblend01', 1.2)\n(0.033644, 'u3_raw', 0.55)\n(0.033468, 'u3_raw', 0.7)\n(0.03288, 'u3_log', 1.0)\n(0.032832, 'u3_raw', 0.85)\n(0.032545, 'u3_raw', 1.0)\n(0.032435, 'u3_logblend01', 1.0)\n(0.032377, 'u3_raw', 1.2)\n(0.031994, 'u3_raw', 1.5)\n(0.031814, 'u3_log', 0.85)\n(0.031447, 'u3_logblend01', 0.85)\n(0.030591, 'unique3_c28', 0.4)\n(0.030469, 'u3_ranklog', 0.4)\n(0.029475, 'u3_logblend01', 0.7)\n(0.029387, 'u3_log', 0.7)\n(0.026039, 'u3_logblend01', 0.55)\n(0.025958, 'u3_log', 0.55)\n(0.021888, 'u3_log', 0.4)\n(0.021603, 'u3_logblend01', 0.4)\n(0.019523, 'u3_rankroot', 1.5)\n(0.018277, 'u3_rankroot', 1.2)\n(0.017013, 'u3_rankroot', 1.0)\n(0.015676, 'u3_rankroot', 0.85)\n(0.014813, 'u3_rankroot', 0.7)\n(0.013734, 'u3_rankroot', 0.55)\n(0.012581, 'u3_rankroot', 0.4)\n(0.012561, 'u3_ranklinear', 1.5)\n(0.01193, 'u3_ranklinear', 1.2)\n(0.011547, 'u3_ranklinear', 1.0)\n(0.011269, 'u3_ranklinear', 0.85)\n(0.010976, 'u3_ranklinear', 0.7)\n(0.010696, 'u3_ranklinear', 0.55)\n(0.010003, 'u3_ranklinear', 0.4)\n" } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-25T01:53:28.233Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:53:28.233Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:53:30.188Z", "callId": "call_sv6k6Dq5tnLWR2eOAWadw0xJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v3_resume --init-checkpoint /app/solution/two_tower_v2_semantic.pt --steps 5000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:54:00.365Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"af1b3b\",\"wall_time_seconds\":30.001587116,\"session_id\":29481,\"original_token_count\":221,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_v3_resume\\\",\\n \\\"checkpoint\\\": null,\\n \\\"init_checkpoint\\\": \\\"/app/solution/two_tower_v2_semantic.pt\\\",\\n \\\"trend_override\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 5000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.001,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 250,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\ninitialized from /app/solution/two_tower_v2_semantic.pt\\nstep=1/5000 loss=3.5610 ema=3.5610 pairs/s=4,509 window=9\\nstep=250/5000 loss=3.5463 ema=3.3672 pairs/s=6,333 window=7\\nstep=500/5000 loss=3.3479 ema=3.3680 pairs/s=6,366 window=13\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:53:30.279Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d412f95c87d28b0db47336718de8", "usage": { "input_tokens": 88724, "cached_input_tokens": 86144, "cache_write_input_tokens": 0, "output_tokens": 214, "reasoning_output_tokens": 62, "total_tokens": 88938 }, "turn_token_usage": { "input_tokens": 4161410, "cached_input_tokens": 4070784, "cache_write_input_tokens": 0, "output_tokens": 47482, "reasoning_output_tokens": 21439, "total_tokens": 4208892 }, "thread_token_usage": { "input_tokens": 4161410, "cached_input_tokens": 4070784, "cache_write_input_tokens": 0, "output_tokens": 47482, "reasoning_output_tokens": 21439, "total_tokens": 4208892 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-25T01:54:03.343Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:54:03.343Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:54:04.109Z", "callId": "call_gGSxTGzkPXYgXNxw2Hqug4WC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:54:34.154Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"13a8c5\",\"wall_time_seconds\":30.000968183,\"session_id\":29481,\"original_token_count\":47,\"output\":\"step=750/5000 loss=3.1020 ema=3.3690 pairs/s=6,063 window=25\\nstep=1000/5000 loss=3.0459 ema=3.3116 pairs/s=6,119 window=27\\nstep=1250/5000 loss=3.3504 ema=3.3141 pairs/s=6,160 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:54:04.236Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d438b7d887d2a202c0f36a5f405c", "usage": { "input_tokens": 89369, "cached_input_tokens": 88576, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 14, "total_tokens": 89431 }, "turn_token_usage": { "input_tokens": 4250779, "cached_input_tokens": 4159360, "cache_write_input_tokens": 0, "output_tokens": 47544, "reasoning_output_tokens": 21453, "total_tokens": 4298323 }, "thread_token_usage": { "input_tokens": 4250779, "cached_input_tokens": 4159360, "cache_write_input_tokens": 0, "output_tokens": 47544, "reasoning_output_tokens": 21453, "total_tokens": 4298323 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-25T01:54:35.534Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:54:35.534Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:54:37.029Z", "text": "The best calibration is now 0.036339, using a square-root transform of three-day distinct organic listeners. I’ve preserved that candidate. Continued semantic training from the 5k-step state is also converging cleanly at a lower learning rate; I’ll compare its 6k–10k effective checkpoints against the preserved best before selecting the final refit duration." }, { "kind": "tool", "timestamp": "2026-09-25T01:54:37.767Z", "callId": "call_nFN3GeQcqBq2c62D3Bnghih4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:55:07.803Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"4b2e2d\",\"wall_time_seconds\":30.000738818,\"session_id\":29481,\"original_token_count\":47,\"output\":\"step=1500/5000 loss=3.2465 ema=3.2748 pairs/s=6,185 window=24\\nstep=1750/5000 loss=3.2834 ema=3.2581 pairs/s=6,211 window=21\\nstep=2000/5000 loss=3.1584 ema=3.2493 pairs/s=6,223 window=18\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:54:37.830Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d45a669087d2b50d71a4e951e972", "usage": { "input_tokens": 89575, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 156, "reasoning_output_tokens": 26, "total_tokens": 89731 }, "turn_token_usage": { "input_tokens": 4340354, "cached_input_tokens": 4248576, "cache_write_input_tokens": 0, "output_tokens": 47700, "reasoning_output_tokens": 21479, "total_tokens": 4388054 }, "thread_token_usage": { "input_tokens": 4340354, "cached_input_tokens": 4248576, "cache_write_input_tokens": 0, "output_tokens": 47700, "reasoning_output_tokens": 21479, "total_tokens": 4388054 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:55:13.000Z", "callId": "call_THNERscQGb4ysPxsQ9jPcKnU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:55:43.049Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"69214b\",\"wall_time_seconds\":30.000958149,\"session_id\":29481,\"original_token_count\":62,\"output\":\"step=2250/5000 loss=3.2157 ema=3.2417 pairs/s=6,126 window=13\\nstep=2500/5000 loss=3.2003 ema=3.2276 pairs/s=6,146 window=23\\nstep=2750/5000 loss=3.3978 ema=3.1925 pairs/s=6,157 window=1\\nstep=3000/5000 loss=3.0397 ema=3.1753 pairs/s=6,171 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:55:13.219Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d47efcb087d291b531802130d56f", "usage": { "input_tokens": 89877, "cached_input_tokens": 89344, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 89923 }, "turn_token_usage": { "input_tokens": 4430231, "cached_input_tokens": 4337920, "cache_write_input_tokens": 0, "output_tokens": 47746, "reasoning_output_tokens": 21479, "total_tokens": 4477977 }, "thread_token_usage": { "input_tokens": 4430231, "cached_input_tokens": 4337920, "cache_write_input_tokens": 0, "output_tokens": 47746, "reasoning_output_tokens": 21479, "total_tokens": 4477977 } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-25T01:55:45.140Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:55:45.140Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:55:45.863Z", "callId": "call_CsKUJFNJXaiRFp9TuAOX1PHz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:56:15.904Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d4c5a8\",\"wall_time_seconds\":30.000944222,\"session_id\":29481,\"original_token_count\":47,\"output\":\"step=3250/5000 loss=3.1133 ema=3.1851 pairs/s=6,181 window=21\\nstep=3500/5000 loss=3.3952 ema=3.1511 pairs/s=6,187 window=7\\nstep=3750/5000 loss=3.0666 ema=3.1426 pairs/s=6,195 window=22\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:55:45.993Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d49f5f5087d29095f979e4b66988", "usage": { "input_tokens": 90095, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 90149 }, "turn_token_usage": { "input_tokens": 4520326, "cached_input_tokens": 4427648, "cache_write_input_tokens": 0, "output_tokens": 47800, "reasoning_output_tokens": 21485, "total_tokens": 4568126 }, "thread_token_usage": { "input_tokens": 4520326, "cached_input_tokens": 4427648, "cache_write_input_tokens": 0, "output_tokens": 47800, "reasoning_output_tokens": 21485, "total_tokens": 4568126 } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-25T01:56:17.433Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:17.433Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:18.141Z", "callId": "call_3vjQJ4bwpFtStfhtQRzygOvb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:56:48.190Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"7caaca\",\"wall_time_seconds\":30.000399608,\"session_id\":29481,\"original_token_count\":47,\"output\":\"step=4000/5000 loss=3.0549 ema=3.1147 pairs/s=6,195 window=21\\nstep=4250/5000 loss=3.2816 ema=3.0986 pairs/s=6,206 window=12\\nstep=4500/5000 loss=3.0663 ema=3.1061 pairs/s=6,210 window=16\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:18.246Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d4c0248887d2a668f8d6fef5b7c4", "usage": { "input_tokens": 90295, "cached_input_tokens": 89856, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 90356 }, "turn_token_usage": { "input_tokens": 4610621, "cached_input_tokens": 4517504, "cache_write_input_tokens": 0, "output_tokens": 47861, "reasoning_output_tokens": 21498, "total_tokens": 4658482 }, "thread_token_usage": { "input_tokens": 4610621, "cached_input_tokens": 4517504, "cache_write_input_tokens": 0, "output_tokens": 47861, "reasoning_output_tokens": 21498, "total_tokens": 4658482 } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-25T01:56:50.846Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:56:50.846Z", "text": "The resumed run has reduced the contrastive EMA further, from 3.46 to about 3.10. Training loss alone can overstate ranking gains, so I’m still treating 0.036339 as the incumbent and will only replace it if a later checkpoint beats it under the exact eligibility-aware evaluator." }, { "kind": "tool", "timestamp": "2026-09-25T01:56:51.546Z", "callId": "call_rQgSNQKBnK6bQEvY4gjzkcS8", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":29481,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:57:03.596Z", "content": "Script completed\nWall time 12.0 seconds\nOutput:\n\n{\"chunk_id\":\"54df9f\",\"wall_time_seconds\":12.013292391,\"exit_code\":0,\"original_token_count\":46,\"output\":\"step=4750/5000 loss=2.9666 ema=3.0617 pairs/s=6,217 window=16\\nstep=5000/5000 loss=3.3771 ema=3.0973 pairs/s=6,206 window=11\\nsaved model /app/solution/two_tower_v3_resume.pt (206.3s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:51.672Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d4e075cc87d2871128ee411cbdf8", "usage": { "input_tokens": 90499, "cached_input_tokens": 90112, "cache_write_input_tokens": 0, "output_tokens": 116, "reasoning_output_tokens": 0, "total_tokens": 90615 }, "turn_token_usage": { "input_tokens": 4701120, "cached_input_tokens": 4607616, "cache_write_input_tokens": 0, "output_tokens": 47977, "reasoning_output_tokens": 21498, "total_tokens": 4749097 }, "thread_token_usage": { "input_tokens": 4701120, "cached_input_tokens": 4607616, "cache_write_input_tokens": 0, "output_tokens": 47977, "reasoning_output_tokens": 21498, "total_tokens": 4749097 } } }, { "kind": "context", "timestamp": "2026-09-25T01:57:03.588Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3a91f43f-ccee-44da-b99e-0cc8258d4fd6", "process_id": "29481", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v3_resume --init-checkpoint /app/solution/two_tower_v2_semantic.pt --steps 5000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_v3_resume --init-checkpoint /app/solution/two_tower_v2_semantic.pt --steps 5000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v3_resume\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_v2_semantic.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_v2_semantic.pt\nstep=1/5000 loss=3.5610 ema=3.5610 pairs/s=4,509 window=9\nstep=250/5000 loss=3.5463 ema=3.3672 pairs/s=6,333 window=7\nstep=500/5000 loss=3.3479 ema=3.3680 pairs/s=6,366 window=13\nstep=750/5000 loss=3.1020 ema=3.3690 pairs/s=6,063 window=25\nstep=1000/5000 loss=3.0459 ema=3.3116 pairs/s=6,119 window=27\nstep=1250/5000 loss=3.3504 ema=3.3141 pairs/s=6,160 window=24\nstep=1500/5000 loss=3.2465 ema=3.2748 pairs/s=6,185 window=24\nstep=1750/5000 loss=3.2834 ema=3.2581 pairs/s=6,211 window=21\nstep=2000/5000 loss=3.1584 ema=3.2493 pairs/s=6,223 window=18\nstep=2250/5000 loss=3.2157 ema=3.2417 pairs/s=6,126 window=13\nstep=2500/5000 loss=3.2003 ema=3.2276 pairs/s=6,146 window=23\nstep=2750/5000 loss=3.3978 ema=3.1925 pairs/s=6,157 window=1\nstep=3000/5000 loss=3.0397 ema=3.1753 pairs/s=6,171 window=24\nstep=3250/5000 loss=3.1133 ema=3.1851 pairs/s=6,181 window=21\nstep=3500/5000 loss=3.3952 ema=3.1511 pairs/s=6,187 window=7\nstep=3750/5000 loss=3.0666 ema=3.1426 pairs/s=6,195 window=22\nstep=4000/5000 loss=3.0549 ema=3.1147 pairs/s=6,195 window=21\nstep=4250/5000 loss=3.2816 ema=3.0986 pairs/s=6,206 window=12\nstep=4500/5000 loss=3.0663 ema=3.1061 pairs/s=6,210 window=16\nstep=4750/5000 loss=2.9666 ema=3.0617 pairs/s=6,217 window=16\nstep=5000/5000 loss=3.3771 ema=3.0973 pairs/s=6,206 window=11\nsaved model /app/solution/two_tower_v3_resume.pt (206.3s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v3_resume\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_v2_semantic.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_v2_semantic.pt\nstep=1/5000 loss=3.5610 ema=3.5610 pairs/s=4,509 window=9\nstep=250/5000 loss=3.5463 ema=3.3672 pairs/s=6,333 window=7\nstep=500/5000 loss=3.3479 ema=3.3680 pairs/s=6,366 window=13\nstep=750/5000 loss=3.1020 ema=3.3690 pairs/s=6,063 window=25\nstep=1000/5000 loss=3.0459 ema=3.3116 pairs/s=6,119 window=27\nstep=1250/5000 loss=3.3504 ema=3.3141 pairs/s=6,160 window=24\nstep=1500/5000 loss=3.2465 ema=3.2748 pairs/s=6,185 window=24\nstep=1750/5000 loss=3.2834 ema=3.2581 pairs/s=6,211 window=21\nstep=2000/5000 loss=3.1584 ema=3.2493 pairs/s=6,223 window=18\nstep=2250/5000 loss=3.2157 ema=3.2417 pairs/s=6,126 window=13\nstep=2500/5000 loss=3.2003 ema=3.2276 pairs/s=6,146 window=23\nstep=2750/5000 loss=3.3978 ema=3.1925 pairs/s=6,157 window=1\nstep=3000/5000 loss=3.0397 ema=3.1753 pairs/s=6,171 window=24\nstep=3250/5000 loss=3.1133 ema=3.1851 pairs/s=6,181 window=21\nstep=3500/5000 loss=3.3952 ema=3.1511 pairs/s=6,187 window=7\nstep=3750/5000 loss=3.0666 ema=3.1426 pairs/s=6,195 window=22\nstep=4000/5000 loss=3.0549 ema=3.1147 pairs/s=6,195 window=21\nstep=4250/5000 loss=3.2816 ema=3.0986 pairs/s=6,206 window=12\nstep=4500/5000 loss=3.0663 ema=3.1061 pairs/s=6,210 window=16\nstep=4750/5000 loss=2.9666 ema=3.0617 pairs/s=6,217 window=16\nstep=5000/5000 loss=3.3771 ema=3.0973 pairs/s=6,206 window=11\nsaved model /app/solution/two_tower_v3_resume.pt (206.3s)\n", "exit_code": 0, "duration": { "secs": 213, "nanos": 233262630 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_v3_resume\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_v2_semantic.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_v2_semantic.pt\nstep=1/5000 loss=3.5610 ema=3.5610 pairs/s=4,509 window=9\nstep=250/5000 loss=3.5463 ema=3.3672 pairs/s=6,333 window=7\nstep=500/5000 loss=3.3479 ema=3.3680 pairs/s=6,366 window=13\nstep=750/5000 loss=3.1020 ema=3.3690 pairs/s=6,063 window=25\nstep=1000/5000 loss=3.0459 ema=3.3116 pairs/s=6,119 window=27\nstep=1250/5000 loss=3.3504 ema=3.3141 pairs/s=6,160 window=24\nstep=1500/5000 loss=3.2465 ema=3.2748 pairs/s=6,185 window=24\nstep=1750/5000 loss=3.2834 ema=3.2581 pairs/s=6,211 window=21\nstep=2000/5000 loss=3.1584 ema=3.2493 pairs/s=6,223 window=18\nstep=2250/5000 loss=3.2157 ema=3.2417 pairs/s=6,126 window=13\nstep=2500/5000 loss=3.2003 ema=3.2276 pairs/s=6,146 window=23\nstep=2750/5000 loss=3.3978 ema=3.1925 pairs/s=6,157 window=1\nstep=3000/5000 loss=3.0397 ema=3.1753 pairs/s=6,171 window=24\nstep=3250/5000 loss=3.1133 ema=3.1851 pairs/s=6,181 window=21\nstep=3500/5000 loss=3.3952 ema=3.1511 pairs/s=6,187 window=7\nstep=3750/5000 loss=3.0666 ema=3.1426 pairs/s=6,195 window=22\nstep=4000/5000 loss=3.0549 ema=3.1147 pairs/s=6,195 window=21\nstep=4250/5000 loss=3.2816 ema=3.0986 pairs/s=6,206 window=12\nstep=4500/5000 loss=3.0663 ema=3.1061 pairs/s=6,210 window=16\nstep=4750/5000 loss=2.9666 ema=3.0617 pairs/s=6,217 window=16\nstep=5000/5000 loss=3.3771 ema=3.0973 pairs/s=6,206 window=11\nsaved model /app/solution/two_tower_v3_resume.pt (206.3s)\n" } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-25T01:57:05.490Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:05.490Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:57:13.917Z", "callId": "call_6noWkwI8UWdb01w9A4uelwWl", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v3_resume.pt; test \\\"$s\\\" != 5000 && ck=/app/solution/two_tower_v3_resume_step${s}.pt; out=/app/solution/v3_s${s}_sqrt_a07.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \\\"$ck\\\" --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.7 --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"resume_step=$s $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:57:44.085Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"5d2d39\",\"wall_time_seconds\":30.001694076,\"session_id\":78394,\"original_token_count\":38,\"output\":\"resume_step=1000 \\\"quality_ndcg@10\\\": 0.03723,\\nresume_step=2000 \\\"quality_ndcg@10\\\": 0.036498,\\nresume_step=3000 \\\"quality_ndcg@10\\\": 0.036444,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:57:14.029Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d4efc30887d2a3d6457092b14f6c", "usage": { "input_tokens": 90750, "cached_input_tokens": 90368, "cache_write_input_tokens": 0, "output_tokens": 259, "reasoning_output_tokens": 35, "total_tokens": 91009 }, "turn_token_usage": { "input_tokens": 4791870, "cached_input_tokens": 4697984, "cache_write_input_tokens": 0, "output_tokens": 48236, "reasoning_output_tokens": 21533, "total_tokens": 4840106 }, "thread_token_usage": { "input_tokens": 4791870, "cached_input_tokens": 4697984, "cache_write_input_tokens": 0, "output_tokens": 48236, "reasoning_output_tokens": 21533, "total_tokens": 4840106 } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-25T01:57:45.740Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:45.740Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:57:51.600Z", "callId": "call_pUdgjJYXGZ8vN9ap36NerDmd", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":78394,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:57:56.061Z", "content": "Script completed\nWall time 4.5 seconds\nOutput:\n\n{\"chunk_id\":\"157bc4\",\"wall_time_seconds\":4.445508346,\"exit_code\":0,\"original_token_count\":25,\"output\":\"resume_step=4000 \\\"quality_ndcg@10\\\": 0.036323,\\nresume_step=5000 \\\"quality_ndcg@10\\\": 0.035927,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:57:51.854Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5186d8c87d294a2e0523c878552", "usage": { "input_tokens": 91127, "cached_input_tokens": 90624, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 15, "total_tokens": 91190 }, "turn_token_usage": { "input_tokens": 4882997, "cached_input_tokens": 4788608, "cache_write_input_tokens": 0, "output_tokens": 48299, "reasoning_output_tokens": 21548, "total_tokens": 4931296 }, "thread_token_usage": { "input_tokens": 4882997, "cached_input_tokens": 4788608, "cache_write_input_tokens": 0, "output_tokens": 48299, "reasoning_output_tokens": 21548, "total_tokens": 4931296 } } }, { "kind": "context", "timestamp": "2026-09-25T01:57:56.054Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74b9f09f-b004-4d2b-acda-f8cda996bbb8", "process_id": "78394", "command": [ "/bin/sh", "-lc", "for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v3_resume.pt; test \"$s\" != 5000 && ck=/app/solution/two_tower_v3_resume_step${s}.pt; out=/app/solution/v3_s${s}_sqrt_a07.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.7 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"resume_step=$s $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for s in 1000 2000 3000 4000 5000; do ck=/app/solution/two_tower_v3_resume.pt; test \"$s\" != 5000 && ck=/app/solution/two_tower_v3_resume_step${s}.pt; out=/app/solution/v3_s${s}_sqrt_a07.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint \"$ck\" --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.7 --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"resume_step=$s $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resume_step=1000 \"quality_ndcg@10\": 0.03723,\nresume_step=2000 \"quality_ndcg@10\": 0.036498,\nresume_step=3000 \"quality_ndcg@10\": 0.036444,\nresume_step=4000 \"quality_ndcg@10\": 0.036323,\nresume_step=5000 \"quality_ndcg@10\": 0.035927,\n", "stderr": "", "aggregated_output": "resume_step=1000 \"quality_ndcg@10\": 0.03723,\nresume_step=2000 \"quality_ndcg@10\": 0.036498,\nresume_step=3000 \"quality_ndcg@10\": 0.036444,\nresume_step=4000 \"quality_ndcg@10\": 0.036323,\nresume_step=5000 \"quality_ndcg@10\": 0.035927,\n", "exit_code": 0, "duration": { "secs": 41, "nanos": 970438878 }, "formatted_output": "resume_step=1000 \"quality_ndcg@10\": 0.03723,\nresume_step=2000 \"quality_ndcg@10\": 0.036498,\nresume_step=3000 \"quality_ndcg@10\": 0.036444,\nresume_step=4000 \"quality_ndcg@10\": 0.036323,\nresume_step=5000 \"quality_ndcg@10\": 0.035927,\n" } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-25T01:57:58.218Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:58.218Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:58:02.027Z", "callId": "call_JV4fTqTp2Lk6p0qetynGrEKT", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.4 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.0; do out=/app/solution/v3_s1000_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \\\"$a\\\" --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"alpha=$a $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:58:32.194Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"3b4057\",\"wall_time_seconds\":30.001439234,\"session_id\":1836,\"original_token_count\":33,\"output\":\"alpha=0.4 \\\"quality_ndcg@10\\\": 0.033972,\\nalpha=0.5 \\\"quality_ndcg@10\\\": 0.035861,\\nalpha=0.6 \\\"quality_ndcg@10\\\": 0.036751,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:58:02.076Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5243da887d293bd4c9ff2a0d1fe", "usage": { "input_tokens": 91285, "cached_input_tokens": 91008, "cache_write_input_tokens": 0, "output_tokens": 264, "reasoning_output_tokens": 48, "total_tokens": 91549 }, "turn_token_usage": { "input_tokens": 4974282, "cached_input_tokens": 4879616, "cache_write_input_tokens": 0, "output_tokens": 48563, "reasoning_output_tokens": 21596, "total_tokens": 5022845 }, "thread_token_usage": { "input_tokens": 4974282, "cached_input_tokens": 4879616, "cache_write_input_tokens": 0, "output_tokens": 48563, "reasoning_output_tokens": 21596, "total_tokens": 5022845 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:58:36.336Z", "callId": "call_HqpPYEtJog8g8uSQ3VFX3iXm", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":1836,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:59:06.345Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"625efc\",\"wall_time_seconds\":30.000975644,\"session_id\":1836,\"original_token_count\":44,\"output\":\"alpha=0.65 \\\"quality_ndcg@10\\\": 0.036941,\\nalpha=0.7 \\\"quality_ndcg@10\\\": 0.03723,\\nalpha=0.75 \\\"quality_ndcg@10\\\": 0.037048,\\nalpha=0.8 \\\"quality_ndcg@10\\\": 0.037024,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:58:36.426Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5488bc887d28c5cfb08472c306f", "usage": { "input_tokens": 91666, "cached_input_tokens": 91136, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 91712 }, "turn_token_usage": { "input_tokens": 5065948, "cached_input_tokens": 4970752, "cache_write_input_tokens": 0, "output_tokens": 48609, "reasoning_output_tokens": 21596, "total_tokens": 5114557 }, "thread_token_usage": { "input_tokens": 5065948, "cached_input_tokens": 4970752, "cache_write_input_tokens": 0, "output_tokens": 48609, "reasoning_output_tokens": 21596, "total_tokens": 5114557 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:59:08.699Z", "callId": "call_3qarsi5tcp3x9ksIQKG2sm2f", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":1836,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:59:16.838Z", "content": "Script completed\nWall time 8.1 seconds\nOutput:\n\n{\"chunk_id\":\"6b8ee8\",\"wall_time_seconds\":8.124925132,\"exit_code\":0,\"original_token_count\":22,\"output\":\"alpha=0.9 \\\"quality_ndcg@10\\\": 0.036939,\\nalpha=1.0 \\\"quality_ndcg@10\\\": 0.036938,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:59:08.784Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d56ab2bc87d2bcbc5873e43a4195", "usage": { "input_tokens": 91848, "cached_input_tokens": 91520, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 91894 }, "turn_token_usage": { "input_tokens": 5157796, "cached_input_tokens": 5062272, "cache_write_input_tokens": 0, "output_tokens": 48655, "reasoning_output_tokens": 21596, "total_tokens": 5206451 }, "thread_token_usage": { "input_tokens": 5157796, "cached_input_tokens": 5062272, "cache_write_input_tokens": 0, "output_tokens": 48655, "reasoning_output_tokens": 21596, "total_tokens": 5206451 } } }, { "kind": "context", "timestamp": "2026-09-25T01:59:16.831Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2176d5ca-8d25-4e01-afe4-1cfa2101067b", "process_id": "1836", "command": [ "/bin/sh", "-lc", "for a in 0.4 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.0; do out=/app/solution/v3_s1000_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.4 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.0; do out=/app/solution/v3_s1000_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "alpha=0.4 \"quality_ndcg@10\": 0.033972,\nalpha=0.5 \"quality_ndcg@10\": 0.035861,\nalpha=0.6 \"quality_ndcg@10\": 0.036751,\nalpha=0.65 \"quality_ndcg@10\": 0.036941,\nalpha=0.7 \"quality_ndcg@10\": 0.03723,\nalpha=0.75 \"quality_ndcg@10\": 0.037048,\nalpha=0.8 \"quality_ndcg@10\": 0.037024,\nalpha=0.9 \"quality_ndcg@10\": 0.036939,\nalpha=1.0 \"quality_ndcg@10\": 0.036938,\n", "stderr": "", "aggregated_output": "alpha=0.4 \"quality_ndcg@10\": 0.033972,\nalpha=0.5 \"quality_ndcg@10\": 0.035861,\nalpha=0.6 \"quality_ndcg@10\": 0.036751,\nalpha=0.65 \"quality_ndcg@10\": 0.036941,\nalpha=0.7 \"quality_ndcg@10\": 0.03723,\nalpha=0.75 \"quality_ndcg@10\": 0.037048,\nalpha=0.8 \"quality_ndcg@10\": 0.037024,\nalpha=0.9 \"quality_ndcg@10\": 0.036939,\nalpha=1.0 \"quality_ndcg@10\": 0.036938,\n", "exit_code": 0, "duration": { "secs": 74, "nanos": 639144089 }, "formatted_output": "alpha=0.4 \"quality_ndcg@10\": 0.033972,\nalpha=0.5 \"quality_ndcg@10\": 0.035861,\nalpha=0.6 \"quality_ndcg@10\": 0.036751,\nalpha=0.65 \"quality_ndcg@10\": 0.036941,\nalpha=0.7 \"quality_ndcg@10\": 0.03723,\nalpha=0.75 \"quality_ndcg@10\": 0.037048,\nalpha=0.8 \"quality_ndcg@10\": 0.037024,\nalpha=0.9 \"quality_ndcg@10\": 0.036939,\nalpha=1.0 \"quality_ndcg@10\": 0.036938,\n" } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-25T01:59:28.795Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:28.795Z", "text": "" } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-25T01:59:40.295Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:40.295Z", "text": "" } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-25T01:59:42.033Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:42.033Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:59:43.651Z", "text": "The semantic tower peaks at an effective 6,000 steps; the exact public score is now 0.037230. Further optimization lowers training loss but slightly hurts the temporal backtest, so I’ve fixed 6k as the refit duration. I’m running one independent seed to check stability and whether a small two-head concatenation improves quality enough to justify its extra request-time cost." }, { "kind": "tool", "timestamp": "2026-09-25T01:59:44.886Z", "callId": "call_Ra7tvPeOzSTMNDTZX7TrlD2o", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:00:15.057Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"fb022b\",\"wall_time_seconds\":30.001625013,\"session_id\":54301,\"original_token_count\":183,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_seed2_base\\\",\\n \\\"checkpoint\\\": null,\\n \\\"init_checkpoint\\\": null,\\n \\\"trend_override\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 5000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 481516,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 500,\\n \\\"save_every\\\": 5000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\nstep=1/5000 loss=4.7146 ema=4.7146 pairs/s=4,475 window=17\\nstep=500/5000 loss=3.9490 ema=3.9417 pairs/s=5,863 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:59:45.001Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d575086087d2953ba37a88389ba6", "usage": { "input_tokens": 91991, "cached_input_tokens": 91648, "cache_write_input_tokens": 0, "output_tokens": 1349, "reasoning_output_tokens": 1123, "total_tokens": 93340 }, "turn_token_usage": { "input_tokens": 5249787, "cached_input_tokens": 5153920, "cache_write_input_tokens": 0, "output_tokens": 50004, "reasoning_output_tokens": 22719, "total_tokens": 5299791 }, "thread_token_usage": { "input_tokens": 5249787, "cached_input_tokens": 5153920, "cache_write_input_tokens": 0, "output_tokens": 50004, "reasoning_output_tokens": 22719, "total_tokens": 5299791 } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-25T02:00:17.716Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:00:17.716Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:00:18.412Z", "callId": "call_9pwn7mglBlDkhpTeZN8AMc9K", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:00:48.459Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"c4d5a3\",\"wall_time_seconds\":30.000956343,\"session_id\":54301,\"original_token_count\":16,\"output\":\"step=1000/5000 loss=3.7995 ema=3.8797 pairs/s=6,067 window=27\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:00:18.555Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5af726087d2832b022d1e4b27c3", "usage": { "input_tokens": 93711, "cached_input_tokens": 91776, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 93765 }, "turn_token_usage": { "input_tokens": 5343498, "cached_input_tokens": 5245696, "cache_write_input_tokens": 0, "output_tokens": 50058, "reasoning_output_tokens": 22725, "total_tokens": 5393556 }, "thread_token_usage": { "input_tokens": 5343498, "cached_input_tokens": 5245696, "cache_write_input_tokens": 0, "output_tokens": 50058, "reasoning_output_tokens": 22725, "total_tokens": 5393556 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:00:51.288Z", "callId": "call_UAh7PrY30bNSat4c5BLGD0QM", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:01:21.314Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e865be\",\"wall_time_seconds\":30.000913596,\"session_id\":54301,\"original_token_count\":31,\"output\":\"step=1500/5000 loss=3.8227 ema=3.8313 pairs/s=6,151 window=22\\nstep=2000/5000 loss=3.6189 ema=3.7674 pairs/s=6,189 window=27\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:00:51.392Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5d0afa087d2938f1cc91102f370", "usage": { "input_tokens": 93853, "cached_input_tokens": 93568, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 93899 }, "turn_token_usage": { "input_tokens": 5437351, "cached_input_tokens": 5339264, "cache_write_input_tokens": 0, "output_tokens": 50104, "reasoning_output_tokens": 22725, "total_tokens": 5487455 }, "thread_token_usage": { "input_tokens": 5437351, "cached_input_tokens": 5339264, "cache_write_input_tokens": 0, "output_tokens": 50104, "reasoning_output_tokens": 22725, "total_tokens": 5487455 } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-25T02:01:24.107Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:01:24.107Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:01:25.321Z", "text": "The second seed is tracking the first run’s learning curve closely, which is a good stability sign. I’ll keep the single-head model unless averaging the two produces a material public gain; the stricter latency objective favors the smaller 65-dimensional embedding when quality is effectively tied." }, { "kind": "tool", "timestamp": "2026-09-25T02:01:26.073Z", "callId": "call_MjO61OGNLfkq2LITi68ZJC3J", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:01:56.108Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"44d3f2\",\"wall_time_seconds\":30.001309469,\"session_id\":54301,\"original_token_count\":16,\"output\":\"step=2500/5000 loss=3.7241 ema=3.7285 pairs/s=6,213 window=8\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:01:26.179Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d5f1a04887d2b945f7cf1352e714", "usage": { "input_tokens": 94013, "cached_input_tokens": 93696, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 11, "total_tokens": 94134 }, "turn_token_usage": { "input_tokens": 5531364, "cached_input_tokens": 5432960, "cache_write_input_tokens": 0, "output_tokens": 50225, "reasoning_output_tokens": 22736, "total_tokens": 5581589 }, "thread_token_usage": { "input_tokens": 5531364, "cached_input_tokens": 5432960, "cache_write_input_tokens": 0, "output_tokens": 50225, "reasoning_output_tokens": 22736, "total_tokens": 5581589 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:01:59.249Z", "callId": "call_GVunp8oWtunWPPNbSjlD2j4y", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:02:29.279Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"bdb18d\",\"wall_time_seconds\":30.00092896,\"session_id\":54301,\"original_token_count\":31,\"output\":\"step=3000/5000 loss=3.7449 ema=3.6760 pairs/s=6,156 window=4\\nstep=3500/5000 loss=3.5919 ema=3.6282 pairs/s=6,183 window=26\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:01:59.356Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d614792887d29fe5228f039d16d0", "usage": { "input_tokens": 94221, "cached_input_tokens": 93824, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 94267 }, "turn_token_usage": { "input_tokens": 5625585, "cached_input_tokens": 5526784, "cache_write_input_tokens": 0, "output_tokens": 50271, "reasoning_output_tokens": 22736, "total_tokens": 5675856 }, "thread_token_usage": { "input_tokens": 5625585, "cached_input_tokens": 5526784, "cache_write_input_tokens": 0, "output_tokens": 50271, "reasoning_output_tokens": 22736, "total_tokens": 5675856 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:02:32.969Z", "callId": "call_Nj2jaelpKiSPydjNbzDwkhwW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:03:03.017Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b4e9cd\",\"wall_time_seconds\":30.000916393,\"session_id\":54301,\"original_token_count\":31,\"output\":\"step=4000/5000 loss=3.4845 ema=3.5464 pairs/s=6,205 window=23\\nstep=4500/5000 loss=3.5082 ema=3.5165 pairs/s=6,217 window=18\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:02:33.111Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d6358b5c87d29c802886fb1d5320", "usage": { "input_tokens": 94382, "cached_input_tokens": 94080, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 94428 }, "turn_token_usage": { "input_tokens": 5719967, "cached_input_tokens": 5620864, "cache_write_input_tokens": 0, "output_tokens": 50317, "reasoning_output_tokens": 22736, "total_tokens": 5770284 }, "thread_token_usage": { "input_tokens": 5719967, "cached_input_tokens": 5620864, "cache_write_input_tokens": 0, "output_tokens": 50317, "reasoning_output_tokens": 22736, "total_tokens": 5770284 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:03:05.131Z", "callId": "call_Hy8WH8zllGgIwXaClbuCvzSw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":54301,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:03:17.526Z", "content": "Script completed\nWall time 12.4 seconds\nOutput:\n\n{\"chunk_id\":\"7a5b2c\",\"wall_time_seconds\":12.356580406,\"exit_code\":0,\"original_token_count\":31,\"output\":\"step=5000/5000 loss=3.4540 ema=3.4395 pairs/s=6,228 window=10\\nsaved model /app/solution/two_tower_seed2_base.pt (205.5s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:03:05.281Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d657486087d2a5d9d039c7bad537", "usage": { "input_tokens": 94544, "cached_input_tokens": 94208, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 94590 }, "turn_token_usage": { "input_tokens": 5814511, "cached_input_tokens": 5715072, "cache_write_input_tokens": 0, "output_tokens": 50363, "reasoning_output_tokens": 22736, "total_tokens": 5864874 }, "thread_token_usage": { "input_tokens": 5814511, "cached_input_tokens": 5715072, "cache_write_input_tokens": 0, "output_tokens": 50363, "reasoning_output_tokens": 22736, "total_tokens": 5864874 } } }, { "kind": "context", "timestamp": "2026-09-25T02:03:17.525Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-908a91c2-9925-47b5-815c-2995d625e09b", "process_id": "54301", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_base\",\n \"checkpoint\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7146 ema=4.7146 pairs/s=4,475 window=17\nstep=500/5000 loss=3.9490 ema=3.9417 pairs/s=5,863 window=24\nstep=1000/5000 loss=3.7995 ema=3.8797 pairs/s=6,067 window=27\nstep=1500/5000 loss=3.8227 ema=3.8313 pairs/s=6,151 window=22\nstep=2000/5000 loss=3.6189 ema=3.7674 pairs/s=6,189 window=27\nstep=2500/5000 loss=3.7241 ema=3.7285 pairs/s=6,213 window=8\nstep=3000/5000 loss=3.7449 ema=3.6760 pairs/s=6,156 window=4\nstep=3500/5000 loss=3.5919 ema=3.6282 pairs/s=6,183 window=26\nstep=4000/5000 loss=3.4845 ema=3.5464 pairs/s=6,205 window=23\nstep=4500/5000 loss=3.5082 ema=3.5165 pairs/s=6,217 window=18\nstep=5000/5000 loss=3.4540 ema=3.4395 pairs/s=6,228 window=10\nsaved model /app/solution/two_tower_seed2_base.pt (205.5s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_base\",\n \"checkpoint\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7146 ema=4.7146 pairs/s=4,475 window=17\nstep=500/5000 loss=3.9490 ema=3.9417 pairs/s=5,863 window=24\nstep=1000/5000 loss=3.7995 ema=3.8797 pairs/s=6,067 window=27\nstep=1500/5000 loss=3.8227 ema=3.8313 pairs/s=6,151 window=22\nstep=2000/5000 loss=3.6189 ema=3.7674 pairs/s=6,189 window=27\nstep=2500/5000 loss=3.7241 ema=3.7285 pairs/s=6,213 window=8\nstep=3000/5000 loss=3.7449 ema=3.6760 pairs/s=6,156 window=4\nstep=3500/5000 loss=3.5919 ema=3.6282 pairs/s=6,183 window=26\nstep=4000/5000 loss=3.4845 ema=3.5464 pairs/s=6,205 window=23\nstep=4500/5000 loss=3.5082 ema=3.5165 pairs/s=6,217 window=18\nstep=5000/5000 loss=3.4540 ema=3.4395 pairs/s=6,228 window=10\nsaved model /app/solution/two_tower_seed2_base.pt (205.5s)\n", "exit_code": 0, "duration": { "secs": 212, "nanos": 473374103 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_base\",\n \"checkpoint\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nstep=1/5000 loss=4.7146 ema=4.7146 pairs/s=4,475 window=17\nstep=500/5000 loss=3.9490 ema=3.9417 pairs/s=5,863 window=24\nstep=1000/5000 loss=3.7995 ema=3.8797 pairs/s=6,067 window=27\nstep=1500/5000 loss=3.8227 ema=3.8313 pairs/s=6,151 window=22\nstep=2000/5000 loss=3.6189 ema=3.7674 pairs/s=6,189 window=27\nstep=2500/5000 loss=3.7241 ema=3.7285 pairs/s=6,213 window=8\nstep=3000/5000 loss=3.7449 ema=3.6760 pairs/s=6,156 window=4\nstep=3500/5000 loss=3.5919 ema=3.6282 pairs/s=6,183 window=26\nstep=4000/5000 loss=3.4845 ema=3.5464 pairs/s=6,205 window=23\nstep=4500/5000 loss=3.5082 ema=3.5165 pairs/s=6,217 window=18\nstep=5000/5000 loss=3.4540 ema=3.4395 pairs/s=6,228 window=10\nsaved model /app/solution/two_tower_seed2_base.pt (205.5s)\n" } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-25T02:03:20.811Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:03:20.811Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:03:23.464Z", "callId": "call_iqI5oiHsty9hWciEp3KlnyTl", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_final --init-checkpoint /app/solution/two_tower_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:03:53.636Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"b8273b\",\"wall_time_seconds\":30.001116841,\"session_id\":82585,\"original_token_count\":221,\"output\":\"{\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_seed2_final\\\",\\n \\\"checkpoint\\\": null,\\n \\\"init_checkpoint\\\": \\\"/app/solution/two_tower_seed2_base.pt\\\",\\n \\\"trend_override\\\": null,\\n \\\"output\\\": null,\\n \\\"steps\\\": 1000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.001,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"seed\\\": 481516,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 250,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_validation_w28.npz\\ninitialized from /app/solution/two_tower_seed2_base.pt\\nstep=1/1000 loss=3.5019 ema=3.5019 pairs/s=4,579 window=17\\nstep=250/1000 loss=3.3310 ema=3.3631 pairs/s=6,382 window=26\\nstep=500/1000 loss=3.3240 ema=3.3641 pairs/s=5,948 window=24\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:03:23.591Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d665b53087d2adc7684cf3e16465", "usage": { "input_tokens": 94698, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 170, "reasoning_output_tokens": 14, "total_tokens": 94868 }, "turn_token_usage": { "input_tokens": 5909209, "cached_input_tokens": 5809408, "cache_write_input_tokens": 0, "output_tokens": 50533, "reasoning_output_tokens": 22750, "total_tokens": 5959742 }, "thread_token_usage": { "input_tokens": 5909209, "cached_input_tokens": 5809408, "cache_write_input_tokens": 0, "output_tokens": 50533, "reasoning_output_tokens": 22750, "total_tokens": 5959742 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:03:55.529Z", "callId": "call_Sumqs4IFxLItblSBncykYcdw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":82585,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:04:12.210Z", "content": "Script completed\nWall time 16.7 seconds\nOutput:\n\n{\"chunk_id\":\"e579ad\",\"wall_time_seconds\":16.65312175,\"exit_code\":0,\"original_token_count\":46,\"output\":\"step=750/1000 loss=3.3292 ema=3.3562 pairs/s=6,091 window=15\\nstep=1000/1000 loss=3.1920 ema=3.3200 pairs/s=6,143 window=27\\nsaved model /app/solution/two_tower_seed2_final.pt (41.7s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:03:55.645Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d689eae887d2be83f9a1bb031b73", "usage": { "input_tokens": 95295, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 95341 }, "turn_token_usage": { "input_tokens": 6004504, "cached_input_tokens": 5903872, "cache_write_input_tokens": 0, "output_tokens": 50579, "reasoning_output_tokens": 22750, "total_tokens": 6055083 }, "thread_token_usage": { "input_tokens": 6004504, "cached_input_tokens": 5903872, "cache_write_input_tokens": 0, "output_tokens": 50579, "reasoning_output_tokens": 22750, "total_tokens": 6055083 } } }, { "kind": "context", "timestamp": "2026-09-25T02:04:12.208Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-020555d1-f370-44e1-b335-76e78f52c8ac", "process_id": "82585", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_final --init-checkpoint /app/solution/two_tower_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode train --windows 28 --name two_tower_seed2_final --init-checkpoint /app/solution/two_tower_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_final\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_seed2_base.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_seed2_base.pt\nstep=1/1000 loss=3.5019 ema=3.5019 pairs/s=4,579 window=17\nstep=250/1000 loss=3.3310 ema=3.3631 pairs/s=6,382 window=26\nstep=500/1000 loss=3.3240 ema=3.3641 pairs/s=5,948 window=24\nstep=750/1000 loss=3.3292 ema=3.3562 pairs/s=6,091 window=15\nstep=1000/1000 loss=3.1920 ema=3.3200 pairs/s=6,143 window=27\nsaved model /app/solution/two_tower_seed2_final.pt (41.7s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_final\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_seed2_base.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_seed2_base.pt\nstep=1/1000 loss=3.5019 ema=3.5019 pairs/s=4,579 window=17\nstep=250/1000 loss=3.3310 ema=3.3631 pairs/s=6,382 window=26\nstep=500/1000 loss=3.3240 ema=3.3641 pairs/s=5,948 window=24\nstep=750/1000 loss=3.3292 ema=3.3562 pairs/s=6,091 window=15\nstep=1000/1000 loss=3.1920 ema=3.3200 pairs/s=6,143 window=27\nsaved model /app/solution/two_tower_seed2_final.pt (41.7s)\n", "exit_code": 0, "duration": { "secs": 48, "nanos": 577928303 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_seed2_final\",\n \"checkpoint\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_seed2_base.pt\",\n \"trend_override\": null,\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_validation_w28.npz\ninitialized from /app/solution/two_tower_seed2_base.pt\nstep=1/1000 loss=3.5019 ema=3.5019 pairs/s=4,579 window=17\nstep=250/1000 loss=3.3310 ema=3.3631 pairs/s=6,382 window=26\nstep=500/1000 loss=3.3240 ema=3.3641 pairs/s=5,948 window=24\nstep=750/1000 loss=3.3292 ema=3.3562 pairs/s=6,091 window=15\nstep=1000/1000 loss=3.1920 ema=3.3200 pairs/s=6,143 window=27\nsaved model /app/solution/two_tower_seed2_final.pt (41.7s)\n" } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-25T02:04:13.594Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:04:13.594Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:04:17.015Z", "callId": "call_cGFAEc29nzLoGKWkVeQKhJS2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.55 0.65 0.7 0.75 0.85 1.0; do out=/app/solution/seed2_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_seed2_final.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \\\"$a\\\" --output \\\"$out\\\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \\\"$out\\\" | grep quality_ndcg); echo \\\"alpha=$a $score\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:04:47.183Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"6789ce\",\"wall_time_seconds\":30.00118701,\"session_id\":33822,\"original_token_count\":33,\"output\":\"alpha=0.55 \\\"quality_ndcg@10\\\": 0.035122,\\nalpha=0.65 \\\"quality_ndcg@10\\\": 0.03602,\\nalpha=0.7 \\\"quality_ndcg@10\\\": 0.036368,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:04:17.119Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d69c5fbc87d298ed30f0ea850fa9", "usage": { "input_tokens": 95474, "cached_input_tokens": 95104, "cache_write_input_tokens": 0, "output_tokens": 218, "reasoning_output_tokens": 19, "total_tokens": 95692 }, "turn_token_usage": { "input_tokens": 6099978, "cached_input_tokens": 5998976, "cache_write_input_tokens": 0, "output_tokens": 50797, "reasoning_output_tokens": 22769, "total_tokens": 6150775 }, "thread_token_usage": { "input_tokens": 6099978, "cached_input_tokens": 5998976, "cache_write_input_tokens": 0, "output_tokens": 50797, "reasoning_output_tokens": 22769, "total_tokens": 6150775 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:04:49.007Z", "callId": "call_T5sLXF5jdXVG4I7ZRKhfsDgq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33822,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:05:06.787Z", "content": "Script completed\nWall time 17.8 seconds\nOutput:\n\n{\"chunk_id\":\"09eba4\",\"wall_time_seconds\":17.770536758,\"exit_code\":0,\"original_token_count\":33,\"output\":\"alpha=0.75 \\\"quality_ndcg@10\\\": 0.036611,\\nalpha=0.85 \\\"quality_ndcg@10\\\": 0.036597,\\nalpha=1.0 \\\"quality_ndcg@10\\\": 0.03656,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:04:49.143Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d6bf5fbc87d29eaf8b8f3d32964d", "usage": { "input_tokens": 95808, "cached_input_tokens": 95232, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 95854 }, "turn_token_usage": { "input_tokens": 6195786, "cached_input_tokens": 6094208, "cache_write_input_tokens": 0, "output_tokens": 50843, "reasoning_output_tokens": 22769, "total_tokens": 6246629 }, "thread_token_usage": { "input_tokens": 6195786, "cached_input_tokens": 6094208, "cache_write_input_tokens": 0, "output_tokens": 50843, "reasoning_output_tokens": 22769, "total_tokens": 6246629 } } }, { "kind": "context", "timestamp": "2026-09-25T02:05:06.785Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-43aed4eb-d286-4b9b-bc07-e4297d861b91", "process_id": "33822", "command": [ "/bin/sh", "-lc", "for a in 0.55 0.65 0.7 0.75 0.85 1.0; do out=/app/solution/seed2_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_seed2_final.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.55 0.65 0.7 0.75 0.85 1.0; do out=/app/solution/seed2_sqrt_a${a}.parquet; /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_seed2_final.pt --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha \"$a\" --output \"$out\" >/dev/null; score=$(/opt/task-venv/bin/python /task/evaluate_public.py \"$out\" | grep quality_ndcg); echo \"alpha=$a $score\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "alpha=0.55 \"quality_ndcg@10\": 0.035122,\nalpha=0.65 \"quality_ndcg@10\": 0.03602,\nalpha=0.7 \"quality_ndcg@10\": 0.036368,\nalpha=0.75 \"quality_ndcg@10\": 0.036611,\nalpha=0.85 \"quality_ndcg@10\": 0.036597,\nalpha=1.0 \"quality_ndcg@10\": 0.03656,\n", "stderr": "", "aggregated_output": "alpha=0.55 \"quality_ndcg@10\": 0.035122,\nalpha=0.65 \"quality_ndcg@10\": 0.03602,\nalpha=0.7 \"quality_ndcg@10\": 0.036368,\nalpha=0.75 \"quality_ndcg@10\": 0.036611,\nalpha=0.85 \"quality_ndcg@10\": 0.036597,\nalpha=1.0 \"quality_ndcg@10\": 0.03656,\n", "exit_code": 0, "duration": { "secs": 49, "nanos": 603705295 }, "formatted_output": "alpha=0.55 \"quality_ndcg@10\": 0.035122,\nalpha=0.65 \"quality_ndcg@10\": 0.03602,\nalpha=0.7 \"quality_ndcg@10\": 0.036368,\nalpha=0.75 \"quality_ndcg@10\": 0.036611,\nalpha=0.85 \"quality_ndcg@10\": 0.036597,\nalpha=1.0 \"quality_ndcg@10\": 0.03656,\n" } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-25T02:05:13.439Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:05:13.439Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:05:27.817Z", "callId": "call_HzyFeqCeUJVrnLrNx9SQeS5q", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path[:0]=['/app/solution','/task']\\nfrom pathlib import Path\\nimport numpy as np,torch,pandas as pd\\nfrom train_and_rank import load_cache,cache_tensors,load_model\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8)\\nc=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c); nusers=len(c['users']);ci=len(c['cutoffs']);pids=ci*nusers+c['target_ui'].astype(np.int64)\\ndef enc(path):\\n m,_=load_model(path)\\n with torch.inference_mode():\\n ids=torch.arange(len(c['catalog'])); iv=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.)\\n uv=[]\\n for s in range(0,len(pids),512):\\n p=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\\n return torch.cat(uv),iv\\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt')\\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\\ntr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy')); ws=[0,.2,.35,.5,.65,.8,1.]; alphas=[.55,.65,.7,.75,.8,.9,1.]\\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in alphas}\\nwith torch.inference_mode():\\n for s in range(0,len(uids),256):\\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\\n for w in ws:\\n base=w*b1+(1-w)*b2\\n for a in alphas:\\n score=base+a*tr;score.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\nres=[]\\nfor key,top in tops.items():\\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)};res.append((score_rankings(ranked,truth)['quality_ndcg@10'],*key))\\nfor x in sorted(res,reverse=True)[:25]:print(x)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:05:51.707Z", "content": "Script completed\nWall time 23.9 seconds\nOutput:\n\n(0.038324, 0.5, 0.55)\n(0.038305, 0.35, 0.7)\n(0.038265, 0.35, 0.75)\n(0.038258, 0.35, 0.65)\n(0.038227, 0.5, 0.7)\n(0.038178, 0.5, 0.65)\n(0.037982, 0.65, 0.55)\n(0.037923, 0.65, 0.65)\n(0.037805, 0.35, 0.55)\n(0.037796, 0.5, 0.75)\n(0.037776, 0.8, 0.7)\n(0.037757, 0.35, 0.8)\n(0.037729, 0.8, 0.65)\n(0.037718, 0.65, 0.7)\n(0.037702, 0.2, 0.8)\n(0.037652, 0.5, 0.9)\n(0.037632, 0.35, 0.9)\n(0.037629, 0.2, 0.75)\n(0.037583, 0.65, 0.9)\n(0.037569, 0.65, 1.0)\n(0.037543, 0.8, 0.55)\n(0.037543, 0.5, 0.8)\n(0.037539, 0.2, 0.7)\n(0.037535, 0.65, 0.75)\n(0.037514, 0.8, 0.75)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:05:27.913Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d6d2f63087d28f26b59a17bd3c9d", "usage": { "input_tokens": 95969, "cached_input_tokens": 95616, "cache_write_input_tokens": 0, "output_tokens": 895, "reasoning_output_tokens": 142, "total_tokens": 96864 }, "turn_token_usage": { "input_tokens": 6291755, "cached_input_tokens": 6189824, "cache_write_input_tokens": 0, "output_tokens": 51738, "reasoning_output_tokens": 22911, "total_tokens": 6343493 }, "thread_token_usage": { "input_tokens": 6291755, "cached_input_tokens": 6189824, "cache_write_input_tokens": 0, "output_tokens": 51738, "reasoning_output_tokens": 22911, "total_tokens": 6343493 } } }, { "kind": "context", "timestamp": "2026-09-25T02:05:51.702Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-46b49781-8414-43f7-9b38-b7aa26b3fd64", "process_id": "97242", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8)\nc=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c); nusers=len(c['users']);ci=len(c['cutoffs']);pids=ci*nusers+c['target_ui'].astype(np.int64)\ndef enc(path):\n m,_=load_model(path)\n with torch.inference_mode():\n ids=torch.arange(len(c['catalog'])); iv=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.)\n uv=[]\n for s in range(0,len(pids),512):\n p=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\n return torch.cat(uv),iv\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt')\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\ntr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy')); ws=[0,.2,.35,.5,.65,.8,1.]; alphas=[.55,.65,.7,.75,.8,.9,1.]\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in alphas}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\n for w in ws:\n base=w*b1+(1-w)*b2\n for a in alphas:\n score=base+a*tr;score.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nres=[]\nfor key,top in tops.items():\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)};res.append((score_rankings(ranked,truth)['quality_ndcg@10'],*key))\nfor x in sorted(res,reverse=True)[:25]:print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8)\nc=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c); nusers=len(c['users']);ci=len(c['cutoffs']);pids=ci*nusers+c['target_ui'].astype(np.int64)\ndef enc(path):\n m,_=load_model(path)\n with torch.inference_mode():\n ids=torch.arange(len(c['catalog'])); iv=m.encode_item(ids,t['item_features'][ci],t['trend'][ci],0.)\n uv=[]\n for s in range(0,len(pids),512):\n p=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][p],t['organic'][p],t['liked'][p],t['disliked'][p],t['profile_stats'][p]))\n return torch.cat(uv),iv\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt')\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\ntr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy')); ws=[0,.2,.35,.5,.65,.8,1.]; alphas=[.55,.65,.7,.75,.8,.9,1.]\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in alphas}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\n for w in ws:\n base=w*b1+(1-w)*b2\n for a in alphas:\n score=base+a*tr;score.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(score,10,dim=1).indices.numpy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nres=[]\nfor key,top in tops.items():\n ranked={int(u):c['catalog'][top[r]].astype(int).tolist() for r,u in enumerate(uids)};res.append((score_rankings(ranked,truth)['quality_ndcg@10'],*key))\nfor x in sorted(res,reverse=True)[:25]:print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.038324, 0.5, 0.55)\n(0.038305, 0.35, 0.7)\n(0.038265, 0.35, 0.75)\n(0.038258, 0.35, 0.65)\n(0.038227, 0.5, 0.7)\n(0.038178, 0.5, 0.65)\n(0.037982, 0.65, 0.55)\n(0.037923, 0.65, 0.65)\n(0.037805, 0.35, 0.55)\n(0.037796, 0.5, 0.75)\n(0.037776, 0.8, 0.7)\n(0.037757, 0.35, 0.8)\n(0.037729, 0.8, 0.65)\n(0.037718, 0.65, 0.7)\n(0.037702, 0.2, 0.8)\n(0.037652, 0.5, 0.9)\n(0.037632, 0.35, 0.9)\n(0.037629, 0.2, 0.75)\n(0.037583, 0.65, 0.9)\n(0.037569, 0.65, 1.0)\n(0.037543, 0.8, 0.55)\n(0.037543, 0.5, 0.8)\n(0.037539, 0.2, 0.7)\n(0.037535, 0.65, 0.75)\n(0.037514, 0.8, 0.75)\n", "stderr": "", "aggregated_output": "(0.038324, 0.5, 0.55)\n(0.038305, 0.35, 0.7)\n(0.038265, 0.35, 0.75)\n(0.038258, 0.35, 0.65)\n(0.038227, 0.5, 0.7)\n(0.038178, 0.5, 0.65)\n(0.037982, 0.65, 0.55)\n(0.037923, 0.65, 0.65)\n(0.037805, 0.35, 0.55)\n(0.037796, 0.5, 0.75)\n(0.037776, 0.8, 0.7)\n(0.037757, 0.35, 0.8)\n(0.037729, 0.8, 0.65)\n(0.037718, 0.65, 0.7)\n(0.037702, 0.2, 0.8)\n(0.037652, 0.5, 0.9)\n(0.037632, 0.35, 0.9)\n(0.037629, 0.2, 0.75)\n(0.037583, 0.65, 0.9)\n(0.037569, 0.65, 1.0)\n(0.037543, 0.8, 0.55)\n(0.037543, 0.5, 0.8)\n(0.037539, 0.2, 0.7)\n(0.037535, 0.65, 0.75)\n(0.037514, 0.8, 0.75)\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 720960873 }, "formatted_output": "(0.038324, 0.5, 0.55)\n(0.038305, 0.35, 0.7)\n(0.038265, 0.35, 0.75)\n(0.038258, 0.35, 0.65)\n(0.038227, 0.5, 0.7)\n(0.038178, 0.5, 0.65)\n(0.037982, 0.65, 0.55)\n(0.037923, 0.65, 0.65)\n(0.037805, 0.35, 0.55)\n(0.037796, 0.5, 0.75)\n(0.037776, 0.8, 0.7)\n(0.037757, 0.35, 0.8)\n(0.037729, 0.8, 0.65)\n(0.037718, 0.65, 0.7)\n(0.037702, 0.2, 0.8)\n(0.037652, 0.5, 0.9)\n(0.037632, 0.35, 0.9)\n(0.037629, 0.2, 0.75)\n(0.037583, 0.65, 0.9)\n(0.037569, 0.65, 1.0)\n(0.037543, 0.8, 0.55)\n(0.037543, 0.5, 0.8)\n(0.037539, 0.2, 0.7)\n(0.037535, 0.65, 0.75)\n(0.037514, 0.8, 0.75)\n" } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-25T02:05:56.070Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:05:56.070Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:05:59.101Z", "text": "The second seed is slightly weaker alone (0.036611), but its errors are complementary. Concatenating the two 64-D heads into one 129-D retrieval vector raises NDCG@10 to 0.038324. That is a material gain while remaining a single maximum-inner-product query; candidate vectors are still fully offline, and request-time work is just two small history heads." }, { "kind": "tool", "timestamp": "2026-09-25T02:06:11.280Z", "callId": "call_Rzx2iGaRF6erMDMTmvhuLfOx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\n# Focused ensemble calibration; loads both towers once.\\nimport sys\\nsys.path[:0]=['/app/solution','/task']\\nfrom pathlib import Path\\nimport numpy as np,torch,pandas as pd\\nfrom train_and_rank import load_cache,cache_tensors,load_model\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8);c=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c);ci=len(c['cutoffs']);nu=len(c['users']);pids=ci*nu+c['target_ui'].astype(np.int64)\\ndef enc(p):\\n m,_=load_model(p)\\n with torch.inference_mode():\\n ii=torch.arange(len(c['catalog']));iv=m.encode_item(ii,t['item_features'][ci],t['trend'][ci],0);uv=[]\\n for s in range(0,len(pids),512):\\n q=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][q],t['organic'][q],t['liked'][q],t['disliked'][q],t['profile_stats'][q]))\\n return torch.cat(uv),iv\\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt');tr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy'))\\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\\nws=[.38,.42,.46,.48,.5,.52,.54,.58,.62];aa=[.42,.46,.5,.52,.54,.56,.58,.6,.62]\\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in aa}\\nwith torch.inference_mode():\\n for s in range(0,len(uids),256):\\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\\n for w in ws:\\n b=w*b1+(1-w)*b2\\n for a in aa:\\n q=b+a*tr;q.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(q,10,dim=1).indices.numpy()\\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)));res=[]\\nfor k,v in tops.items():\\n r={int(u):c['catalog'][v[j]].astype(int).tolist() for j,u in enumerate(uids)};res.append((score_rankings(r,truth)['quality_ndcg@10'],*k))\\nfor x in sorted(res,reverse=True)[:20]:print(x)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:06:41.446Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:06:11.361Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d6ffe3d087d299dcd97673bda6c9", "usage": { "input_tokens": 97287, "cached_input_tokens": 95744, "cache_write_input_tokens": 0, "output_tokens": 942, "reasoning_output_tokens": 96, "total_tokens": 98229 }, "turn_token_usage": { "input_tokens": 6389042, "cached_input_tokens": 6285568, "cache_write_input_tokens": 0, "output_tokens": 52680, "reasoning_output_tokens": 23007, "total_tokens": 6441722 }, "thread_token_usage": { "input_tokens": 6389042, "cached_input_tokens": 6285568, "cache_write_input_tokens": 0, "output_tokens": 52680, "reasoning_output_tokens": 23007, "total_tokens": 6441722 } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-25T02:06:47.155Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:06:47.155Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T02:06:47.594Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0a3cd651-86a7-48eb-a172-9e45c1362dd5", "process_id": "11169", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\n# Focused ensemble calibration; loads both towers once.\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8);c=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c);ci=len(c['cutoffs']);nu=len(c['users']);pids=ci*nu+c['target_ui'].astype(np.int64)\ndef enc(p):\n m,_=load_model(p)\n with torch.inference_mode():\n ii=torch.arange(len(c['catalog']));iv=m.encode_item(ii,t['item_features'][ci],t['trend'][ci],0);uv=[]\n for s in range(0,len(pids),512):\n q=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][q],t['organic'][q],t['liked'][q],t['disliked'][q],t['profile_stats'][q]))\n return torch.cat(uv),iv\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt');tr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\nws=[.38,.42,.46,.48,.5,.52,.54,.58,.62];aa=[.42,.46,.5,.52,.54,.56,.58,.6,.62]\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in aa}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\n for w in ws:\n b=w*b1+(1-w)*b2\n for a in aa:\n q=b+a*tr;q.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(q,10,dim=1).indices.numpy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)));res=[]\nfor k,v in tops.items():\n r={int(u):c['catalog'][v[j]].astype(int).tolist() for j,u in enumerate(uids)};res.append((score_rankings(r,truth)['quality_ndcg@10'],*k))\nfor x in sorted(res,reverse=True)[:20]:print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\n# Focused ensemble calibration; loads both towers once.\nimport sys\nsys.path[:0]=['/app/solution','/task']\nfrom pathlib import Path\nimport numpy as np,torch,pandas as pd\nfrom train_and_rank import load_cache,cache_tensors,load_model\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data');O=Path('/app/solution');torch.set_num_threads(8);c=load_cache(O/'cache_validation_w28.npz');t=cache_tensors(c);ci=len(c['cutoffs']);nu=len(c['users']);pids=ci*nu+c['target_ui'].astype(np.int64)\ndef enc(p):\n m,_=load_model(p)\n with torch.inference_mode():\n ii=torch.arange(len(c['catalog']));iv=m.encode_item(ii,t['item_features'][ci],t['trend'][ci],0);uv=[]\n for s in range(0,len(pids),512):\n q=torch.from_numpy(pids[s:s+512]);uv.append(m.encode_user(t['recent'][q],t['organic'][q],t['liked'][q],t['disliked'][q],t['profile_stats'][q]))\n return torch.cat(uv),iv\nu1,i1=enc(O/'two_tower_v3_resume_step1000.pt');u2,i2=enc(O/'two_tower_seed2_final.pt');tr=torch.from_numpy(np.load(O/'trend_u3_sqrt_validation.npy'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:pack=z['packed_eligible'].copy();uids=z['uids'].copy()\nws=[.38,.42,.46,.48,.5,.52,.54,.58,.62];aa=[.42,.46,.5,.52,.54,.56,.58,.6,.62]\ntops={(w,a):np.empty((len(uids),10),np.int32) for w in ws for a in aa}\nwith torch.inference_mode():\n for s in range(0,len(uids),256):\n e=min(s+256,len(uids));b1=u1[s:e]@i1.T;b2=u2[s:e]@i2.T;mask=torch.from_numpy(np.unpackbits(pack[s:e],axis=1,count=len(c['catalog']),bitorder='little').astype(bool))\n for w in ws:\n b=w*b1+(1-w)*b2\n for a in aa:\n q=b+a*tr;q.masked_fill_(~mask,-torch.inf);tops[(w,a)][s:e]=torch.topk(q,10,dim=1).indices.numpy()\ntruth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)));res=[]\nfor k,v in tops.items():\n r={int(u):c['catalog'][v[j]].astype(int).tolist() for j,u in enumerate(uids)};res.append((score_rankings(r,truth)['quality_ndcg@10'],*k))\nfor x in sorted(res,reverse=True)[:20]:print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.038439, 0.52, 0.6)\n(0.038426, 0.54, 0.58)\n(0.03841, 0.46, 0.62)\n(0.038384, 0.58, 0.54)\n(0.038373, 0.48, 0.58)\n(0.038364, 0.54, 0.54)\n(0.038363, 0.5, 0.56)\n(0.038361, 0.52, 0.58)\n(0.038359, 0.48, 0.62)\n(0.038353, 0.5, 0.6)\n(0.038335, 0.42, 0.62)\n(0.038319, 0.48, 0.6)\n(0.038319, 0.46, 0.58)\n(0.038286, 0.46, 0.6)\n(0.038281, 0.52, 0.56)\n(0.038281, 0.48, 0.56)\n(0.03827, 0.54, 0.6)\n(0.038265, 0.52, 0.54)\n(0.038258, 0.42, 0.6)\n(0.038254, 0.42, 0.58)\n", "stderr": "", "aggregated_output": "(0.038439, 0.52, 0.6)\n(0.038426, 0.54, 0.58)\n(0.03841, 0.46, 0.62)\n(0.038384, 0.58, 0.54)\n(0.038373, 0.48, 0.58)\n(0.038364, 0.54, 0.54)\n(0.038363, 0.5, 0.56)\n(0.038361, 0.52, 0.58)\n(0.038359, 0.48, 0.62)\n(0.038353, 0.5, 0.6)\n(0.038335, 0.42, 0.62)\n(0.038319, 0.48, 0.6)\n(0.038319, 0.46, 0.58)\n(0.038286, 0.46, 0.6)\n(0.038281, 0.52, 0.56)\n(0.038281, 0.48, 0.56)\n(0.03827, 0.54, 0.6)\n(0.038265, 0.52, 0.54)\n(0.038258, 0.42, 0.6)\n(0.038254, 0.42, 0.58)\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 150461403 }, "formatted_output": "(0.038439, 0.52, 0.6)\n(0.038426, 0.54, 0.58)\n(0.03841, 0.46, 0.62)\n(0.038384, 0.58, 0.54)\n(0.038373, 0.48, 0.58)\n(0.038364, 0.54, 0.54)\n(0.038363, 0.5, 0.56)\n(0.038361, 0.52, 0.58)\n(0.038359, 0.48, 0.62)\n(0.038353, 0.5, 0.6)\n(0.038335, 0.42, 0.62)\n(0.038319, 0.48, 0.6)\n(0.038319, 0.46, 0.58)\n(0.038286, 0.46, 0.6)\n(0.038281, 0.52, 0.56)\n(0.038281, 0.48, 0.56)\n(0.03827, 0.54, 0.6)\n(0.038265, 0.52, 0.54)\n(0.038258, 0.42, 0.6)\n(0.038254, 0.42, 0.58)\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T02:06:48.034Z", "callId": "call_nnxdAEFUFr0vyZhaL55raTuG", "name": "exec", "input": { "code": "let out=[]; for (const n of ALL_TOOLS) if (n.name.includes(\"list\")||n.description?.includes(\"session\")) out.push(n); text(out);\n" }, "result": { "timestamp": "2026-09-25T02:06:48.157Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[{\"name\":\"exec_command\",\"description\":\"Runs a command in a PTY, returning output or a session ID for ongoing interaction.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { exec_command(args: {\\n // Shell command to execute.\\n cmd: string;\\n // User-facing approval question for `require_escalated`; omit otherwise.\\n justification?: string;\\n // True runs the shell with -l/-i semantics; false disables them. Defaults to true.\\n login?: boolean;\\n // Output token budget. Defaults to 10000 tokens; larger requests may be capped by policy.\\n max_output_tokens?: number;\\n // Reusable approval prefix for `cmd`, only with `sandbox_permissions: \\\"require_escalated\\\"`; for example [\\\"git\\\", \\\"pull\\\"].\\n prefix_rule?: Array;\\n // Per-command sandbox override. Defaults to `use_default`; use `require_escalated` for unsandboxed execution.\\n sandbox_permissions?: \\\"use_default\\\" | \\\"require_escalated\\\";\\n // Shell binary to launch. Defaults to the user's default shell.\\n shell?: string;\\n // True allocates a PTY for the command; false or omitted uses plain pipes.\\n tty?: boolean;\\n // Working directory for the command. Defaults to the turn cwd.\\n workdir?: string;\\n // Wait before yielding output. Defaults to 10000 ms; effective range is 250-30000 ms.\\n yield_time_ms?: number;\\n}): Promise<{\\n // Chunk identifier included when the response reports one.\\n chunk_id?: string;\\n // Process exit code when the command finished during this call.\\n exit_code?: number;\\n // Approximate token count before output truncation.\\n original_token_count?: number;\\n // Command output text, possibly truncated.\\n output: string;\\n // Session identifier to pass to write_stdin when the process is still running.\\n session_id?: number;\\n // Elapsed wall time spent waiting for output in seconds.\\n wall_time_seconds: number;\\n}>; };\\n```\"},{\"name\":\"list_mcp_resource_templates\",\"description\":\"Lists resource templates provided by MCP servers. Parameterized resource templates allow servers to share data that takes parameters and provides context to language models, such as files, database schemas, or application-specific information. Prefer resource templates over web search when possible.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { list_mcp_resource_templates(args: {\\n // Opaque cursor from a previous list_mcp_resource_templates call; omit for the first page.\\n cursor?: string;\\n // MCP server name. Omit to list resource templates from every configured server.\\n server?: string;\\n}): Promise; };\\n```\"},{\"name\":\"list_mcp_resources\",\"description\":\"Lists resources provided by MCP servers. Resources allow servers to share data that provides context to language models, such as files, database schemas, or application-specific information. Prefer resources over web search when possible.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { list_mcp_resources(args: {\\n // Opaque cursor from a previous list_mcp_resources call; omit for the first page.\\n cursor?: string;\\n // MCP server name. Omit to list resources from every configured server.\\n server?: string;\\n}): Promise; };\\n```\"},{\"name\":\"mcp__codex_apps__codex_document_control_execute_document_command\",\"description\":\"Use Codex Document Control to find connected document sessions, inspect the tools supported by a selected session, and execute one supported tool against that session. Call `list_document_sessions` first to choose the intended connected session, call `get_document_tool_schemas` before constructing tool arguments, then call `execute_document_command` with a caller-stable `idempotency_key`. Use this only for connected Codex document control; do not use it for general spreadsheet, presentation, or document tasks without a connected document session.\\n\\nExecute one supported surface-specific tool against a connected Codex document session. First call `list_document_sessions` to choose the intended `executor_session_id` and `supported_tools[].name`, then call `get_document_tool_schemas` for the selected `surface`, that `supported_tools[].name` as `tool_name`, and `version` before constructing `args`. `idempotency_key` must be a caller-stable key that you reuse verbatim only when retrying the same logical document-control command; use a new key for a different command.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__codex_document_control_execute_document_command(args: {\\n // JSON object of arguments matching the selected tool's `input_schema` from `get_document_tool_schemas`.\\n args: { [key: string]: unknown; };\\n // Exact `executor_session_id` copied from the selected Codex document session returned by `list_document_sessions`.\\n executor_session_id: string;\\n // Caller-stable idempotency key for this logical document-control command. Reuse the exact same key only for retries of the same command.\\n idempotency_key: string;\\n // Exact selected session `supported_tools[].name` copied from `list_document_sessions`.\\n tool_name: string;\\n}): Promise; };\\n```\"},{\"name\":\"mcp__codex_apps__codex_document_control_get_document_tool_schemas\",\"description\":\"Use Codex Document Control to find connected document sessions, inspect the tools supported by a selected session, and execute one supported tool against that session. Call `list_document_sessions` first to choose the intended connected session, call `get_document_tool_schemas` before constructing tool arguments, then call `execute_document_command` with a caller-stable `idempotency_key`. Use this only for connected Codex document control; do not use it for general spreadsheet, presentation, or document tasks without a connected document session.\\n\\nFetch the concrete input schemas for tools supported by a selected Codex document session before constructing `execute_document_command.args`. First call `list_document_sessions`, then pass the exact `surface`, selected `supported_tools[].name` as `tool_name`, and `version` values from that session's `supported_tools` records.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__codex_document_control_get_document_tool_schemas(args: {\\n // Exact tool schema lookup keys from Codex document session discovery, keyed by `surface`, `supported_tools[].name` passed as `tool_name`, and `version`.\\n items: Array<{\\n // Document surface. Use `excel` for Excel workbooks, `powerpoint` for PowerPoint presentations, `word` for Word documents, or `sheets` for Google Sheets spreadsheets.\\n surface: \\\"excel\\\" | \\\"powerpoint\\\" | \\\"sheets\\\" | \\\"word\\\";\\n // Exact `supported_tools[].name` copied from the selected session.\\n tool_name: string;\\n // Exact `supported_tools[].version` copied from the selected session.\\n version: string;\\n}>;\\n}): Promise; };\\n```\"},{\"name\":\"mcp__codex_apps__codex_document_control_list_document_sessions\",\"description\":\"Use Codex Document Control to find connected document sessions, inspect the tools supported by a selected session, and execute one supported tool against that session. Call `list_document_sessions` first to choose the intended connected session, call `get_document_tool_schemas` before constructing tool arguments, then call `execute_document_command` with a caller-stable `idempotency_key`. Use this only for connected Codex document control; do not use it for general spreadsheet, presentation, or document tasks without a connected document session.\\n\\nList the user's currently connected Codex document sessions and the surface-specific tools each session supports. Call this before executing a document-control command so you can choose the intended `executor_session_id` and `supported_tools[].name`.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__codex_document_control_list_document_sessions(args: {\\n // Optional document surface filter. Use `excel` for Excel workbooks, `powerpoint` for PowerPoint presentations, `word` for Word documents, or `sheets` for Google Sheets spreadsheets. Omit to list connected Codex document sessions across all supported surfaces.\\n surface?: \\\"excel\\\" | \\\"powerpoint\\\" | \\\"sheets\\\" | \\\"word\\\" | null;\\n}): Promise; };\\n```\"},{\"name\":\"mcp__codex_apps__sites_list_custom_domains\",\"description\":\"Use Sites to build or modify websites, including landing pages, portfolios, dashboards, portals, trackers, hubs, and internal tools. Use Sites skills for local implementation, source preparation, and artifact packaging. Use this connector for site creation, runtime environment variables, versions, production deployments, and access controls. Read .openai/hosting.json before creating a site and reuse its project_id when present. Treat Sites IDs and cursors as opaque: copy them exactly from .openai/hosting.json or Sites responses as applicable, and never invent, reformat, derive, or substitute them. Never call create_site more than once for the same local site. Push the exact source state before saving a version. commit_sha must identify that pushed state, and any archive must be built from it. Deploy only saved versions; every Sites deployment URL is production. Inspect deployment status when the initial result is non-terminal or the user asks for progress. Publish after creating or editing a site by default, including on subsequent turns, unless the user explicitly requested local-only work, a saved version without deployment, or no publishing. New sites start private. Preserve the site's current audience unless the user explicitly requests a different audience. Use the private operation for known owner-private sites and let it enforce owner-only access. Runtime tool approvals and backend access checks still apply without a separate conversational deployment confirmation.\\n\\nList custom domains attached to a site.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__sites_list_custom_domains(args: {\\n // Exact opaque site project ID. Copy it verbatim from .openai/hosting.json's project_id or the id field returned by create_site, list_sites, or get_site, or the server-returned site_metadata.project_id on a Library Site result. Keep the same selected workspace. Never invent, modify, or substitute another identifier.\\n project_id: string;\\n}): Promise;\\n // CNAME target to use for custom subdomains.\\n cname_target: string | null;\\n created_at: string;\\n hostname: string;\\n id: string;\\n last_error: string | null;\\n project_id: string;\\n provider_status: string | null;\\n ssl_status: string | null;\\n status: \\\"pending\\\" | \\\"active\\\" | \\\"failed\\\";\\n updated_at: string;\\n validation_records: Array<{ name?: string | null; record_type?: string | null; value?: string | null; }>;\\n worker_name: string;\\n}>; }>>; };\\n```\"},{\"name\":\"mcp__codex_apps__sites_list_site_versions\",\"description\":\"Use Sites to build or modify websites, including landing pages, portfolios, dashboards, portals, trackers, hubs, and internal tools. Use Sites skills for local implementation, source preparation, and artifact packaging. Use this connector for site creation, runtime environment variables, versions, production deployments, and access controls. Read .openai/hosting.json before creating a site and reuse its project_id when present. Treat Sites IDs and cursors as opaque: copy them exactly from .openai/hosting.json or Sites responses as applicable, and never invent, reformat, derive, or substitute them. Never call create_site more than once for the same local site. Push the exact source state before saving a version. commit_sha must identify that pushed state, and any archive must be built from it. Deploy only saved versions; every Sites deployment URL is production. Inspect deployment status when the initial result is non-terminal or the user asks for progress. Publish after creating or editing a site by default, including on subsequent turns, unless the user explicitly requested local-only work, a saved version without deployment, or no publishing. New sites start private. Preserve the site's current audience unless the user explicitly requests a different audience. Use the private operation for known owner-private sites and let it enforce owner-only access. Runtime tool approvals and backend access checks still apply without a separate conversational deployment confirmation.\\n\\nList saved site versions in newest-first order for history, deployment, or rollback selection. Defaults to 20 versions; limit must be an integer from 1 to 50. For more versions, reuse the returned cursor with the same project_id; stop when cursor is null. A saved version is not necessarily deployed to production.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__sites_list_site_versions(args: {\\n // Cursor returned by a previous list_site_versions call.\\n cursor?: string | null;\\n // Maximum number of site versions to return.\\n limit?: number;\\n // Exact opaque site project ID. Copy it verbatim from .openai/hosting.json's project_id or the id field returned by create_site, list_sites, or get_site, or the server-returned site_metadata.project_id on a Library Site result. Keep the same selected workspace. Never invent, modify, or substitute another identifier.\\n project_id: string;\\n}): Promise;\\n}>>; };\\n```\"},{\"name\":\"mcp__codex_apps__sites_list_sites\",\"description\":\"Use Sites to build or modify websites, including landing pages, portfolios, dashboards, portals, trackers, hubs, and internal tools. Use Sites skills for local implementation, source preparation, and artifact packaging. Use this connector for site creation, runtime environment variables, versions, production deployments, and access controls. Read .openai/hosting.json before creating a site and reuse its project_id when present. Treat Sites IDs and cursors as opaque: copy them exactly from .openai/hosting.json or Sites responses as applicable, and never invent, reformat, derive, or substitute them. Never call create_site more than once for the same local site. Push the exact source state before saving a version. commit_sha must identify that pushed state, and any archive must be built from it. Deploy only saved versions; every Sites deployment URL is production. Inspect deployment status when the initial result is non-terminal or the user asks for progress. Publish after creating or editing a site by default, including on subsequent turns, unless the user explicitly requested local-only work, a saved version without deployment, or no publishing. New sites start private. Preserve the site's current audience unless the user explicitly requests a different audience. Use the private operation for known owner-private sites and let it enforce owner-only access. Runtime tool approvals and backend access checks still apply without a separate conversational deployment confirmation.\\n\\nList Sites you own in the selected account, including personal accounts. Defaults to 20 Sites; limit must be an integer from 1 to 50. For more results, call list_sites again with the returned cursor and the same role and include_editable values. Use role=editor for shared editable Sites; use search_sites for broader workspace discovery. If .openai/hosting.json has project_id, reuse it without listing. Otherwise use a returned item's id unchanged as project_id; never derive or replace it from a title or slug.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { mcp__codex_apps__sites_list_sites(args: {\\n // Cursor returned by a previous list_sites call.\\n cursor?: string | null;\\n // Legacy option to include editable sites when no role is specified.\\n include_editable?: boolean;\\n // Maximum number of sites to return.\\n limit?: number;\\n // Return only sites where the current user has this role.\\n role?: \\\"owner\\\" | \\\"editor\\\" | null;\\n}): Promise;\\n // Accepted project editors in the current workspace.\\n allowed_editors?: Array<{\\n // Stable row identifier. This is an account user ID for a workspace user and an external visitor grant ID when is_external is true.\\n account_user_id: string;\\n // Email address for the allowed user, when available.\\n email?: string | null;\\n // True when this email is authorized as an external visitor rather than through workspace membership.\\n is_external?: boolean | null;\\n // Display name for the allowed user, when available.\\n name?: string | null;\\n // Project sharing role when supplied by the current access response.\\n role?: \\\"owner\\\" | \\\"editor\\\" | \\\"viewer\\\" | null;\\n}>;\\n // Group details resolved from allowed workspace and tenant group IDs.\\n allowed_groups: Array<{\\n // Group ID to use in an Appgen access policy.\\n id: string;\\n // Group display name.\\n name: string;\\n // Site sharing role when supplied by the current access response.\\n role?: \\\"viewer\\\" | \\\"editor\\\" | null;\\n // Total number of members in the group.\\n size: number;\\n}>;\\n // Tenant group ID allowlist for the app.\\n allowed_tenant_group_ids: Array;\\n // Allowed workspace users and email-bound external visitors. External visitors use their grant ID as account_user_id and set is_external.\\n allowed_users: Array<{\\n // Stable row identifier. This is an account user ID for a workspace user and an external visitor grant ID when is_external is true.\\n account_user_id: string;\\n // Email address for the allowed user, when available.\\n email?: string | null;\\n // True when this email is authorized as an external visitor rather than through workspace membership.\\n is_external?: boolean | null;\\n // Display name for the allowed user, when available.\\n name?: string | null;\\n // Project sharing role when supplied by the current access response.\\n role?: \\\"owner\\\" | \\\"editor\\\" | \\\"viewer\\\" | null;\\n}>;\\n // Workspace group ID allowlist for the app.\\n allowed_workspace_group_ids: Array;\\n // Number of email-bound external visitors allowed to view the site.\\n external_visitor_count?: number;\\n // Appgen project ID\\n project_id: string;\\n // Monotonic access policy revision.\\n revision: number;\\n // Access policy update timestamp.\\n updated_at: string;\\n} | null;\\n attached_page_id?: string | null;\\n auth_client_id: string | null;\\n // Access modes the current user may set. Omitted when the capability is unavailable.\\n available_access_modes?: Array<\\\"public\\\" | \\\"workspace_all\\\" | \\\"custom\\\"> | null;\\n created_at: string;\\n current_live_url: string | null;\\n current_preview_url: string | null;\\n // The authenticated user's role on this Sites project.\\n current_user_role?: \\\"owner\\\" | \\\"editor\\\" | null;\\n description: string | null;\\n disabled_by?: \\\"workspace_admin\\\" | \\\"openai\\\" | null;\\n // Generated Site origin for the current project and workspace route. Use it for absolute Site URLs needed before publication; it does not mean the Site is live. The source repository's remote_url is a Git endpoint, not the Site origin.\\n expected_url?: string | null;\\n // Opaque site project ID. Pass this exact value as project_id.\\n id: string;\\n latest_edit_context?: { chatgpt_conversation_id?: string | null; codex_thread_id?: string | null; } | null;\\n latest_version_number: number;\\n screenshot_url: string | null;\\n slug: string;\\n // Short-lived source repository write credential when requested.\\n source_repository_credential?: {\\n // AppGen AppRepository id.\\n app_repository_id: string;\\n // Git authentication mode for the token.\\n auth_mode: string;\\n // Default branch the client should push.\\n branch: string;\\n // Source repository provider.\\n provider: string;\\n // Whether this response confirms an accepted automatic private publication window. If true, push before publish_on_push_expires_at and check the matching version's deployment_id and deployment status; do not separately save/deploy. If false, Site creation and write-credential callers must use the existing explicit publishing flow. False does not cancel an earlier window: reconcile any existing deployment before retrying publication.\\n publish_on_push_accepted?: boolean;\\n // Until this timestamp, the owner has authorized private publication of pushes to this branch. Null neither authorizes nor cancels a window. After expiry, opt in again through create_source_repository_write_credential.\\n publish_on_push_expires_at?: string | null;\\n // Git remote URL without embedded credentials.\\n remote_url: string;\\n // Provider repository name bound to the AppGen project.\\n repository: string;\\n // Short-lived repo-scoped Git token.\\n token: string;\\n // Token expiration timestamp when provided.\\n token_expires_at: string;\\n} | null;\\n status: \\\"active\\\" | \\\"suspended\\\" | \\\"deleting\\\";\\n title: string;\\n updated_at: string;\\n}>;\\n}>>; };\\n```\"},{\"name\":\"write_stdin\",\"description\":\"Writes characters to an existing unified exec session and returns recent output.\\n\\nexec tool declaration:\\n```ts\\ndeclare const tools: { write_stdin(args: {\\n // Bytes to write to stdin. Defaults to empty, which polls without writing.\\n chars?: string;\\n // Output token budget. Defaults to 10000 tokens; larger requests may be capped by policy.\\n max_output_tokens?: number;\\n // Identifier of the running unified exec session.\\n session_id: number;\\n // Wait before yielding output. Non-empty writes default to 250 ms and cap at 30000 ms; empty polls wait 5000-300000 ms by default.\\n yield_time_ms?: number;\\n}): Promise<{\\n // Chunk identifier included when the response reports one.\\n chunk_id?: string;\\n // Process exit code when the command finished during this call.\\n exit_code?: number;\\n // Approximate token count before output truncation.\\n original_token_count?: number;\\n // Command output text, possibly truncated.\\n output: string;\\n // Session identifier to pass to write_stdin when the process is still running.\\n session_id?: number;\\n // Elapsed wall time spent waiting for output in seconds.\\n wall_time_seconds: number;\\n}>; };\\n```\"}]", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:06:48.156Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d7319efc87d2840472772840bce1", "usage": { "input_tokens": 98252, "cached_input_tokens": 97152, "cache_write_input_tokens": 0, "output_tokens": 214, "reasoning_output_tokens": 166, "total_tokens": 98466 }, "turn_token_usage": { "input_tokens": 6487294, "cached_input_tokens": 6382720, "cache_write_input_tokens": 0, "output_tokens": 52894, "reasoning_output_tokens": 23173, "total_tokens": 6540188 }, "thread_token_usage": { "input_tokens": 6487294, "cached_input_tokens": 6382720, "cache_write_input_tokens": 0, "output_tokens": 52894, "reasoning_output_tokens": 23173, "total_tokens": 6540188 } } } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-25T02:06:51.361Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:06:51.361Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:06:55.581Z", "callId": "call_vDT6LhWaS8lKIl6Mf4JBnbGy", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /proc/[0-9]*/cmdline; do x=$(tr '\\\\000' ' ' < \\\"$f\\\" 2>/dev/null); case \\\"$x\\\" in *Focused*ensemble*) echo \\\"$f RUNNING\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:06:55.709Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/proc/13466/cmdline RUNNING\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:06:55.625Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8f90e608-f3ad-493f-92fd-1271b39d9554", "process_id": "20014", "command": [ "/bin/sh", "-lc", "for f in /proc/[0-9]*/cmdline; do x=$(tr '\\000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *Focused*ensemble*) echo \"$f RUNNING\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /proc/[0-9]*/cmdline; do x=$(tr '\\000' ' ' < \"$f\" 2>/dev/null); case \"$x\" in *Focused*ensemble*) echo \"$f RUNNING\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/13466/cmdline RUNNING\n", "stderr": "", "aggregated_output": "/proc/13466/cmdline RUNNING\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3260 }, "formatted_output": "/proc/13466/cmdline RUNNING\n" } } }, { "kind": "context", "timestamp": "2026-09-25T02:06:55.708Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d738790487d2b73221c450f34b9f", "usage": { "input_tokens": 103809, "cached_input_tokens": 98048, "cache_write_input_tokens": 0, "output_tokens": 182, "reasoning_output_tokens": 81, "total_tokens": 103991 }, "turn_token_usage": { "input_tokens": 6591103, "cached_input_tokens": 6480768, "cache_write_input_tokens": 0, "output_tokens": 53076, "reasoning_output_tokens": 23254, "total_tokens": 6644179 }, "thread_token_usage": { "input_tokens": 6591103, "cached_input_tokens": 6480768, "cache_write_input_tokens": 0, "output_tokens": 53076, "reasoning_output_tokens": 23254, "total_tokens": 6644179 } } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-25T02:07:00.603Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:07:00.603Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:07:02.174Z", "callId": "call_IbSfUY9JWLufcL7D6cLq7eZY", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in 13466 13467; do if test -e /proc/$p/stat; then awk -v p=$p '{print p,$3,$14,$15,$24}' /proc/$p/stat; cat /proc/$p/wchan; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:07:02.268Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:07:02.210Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-41f3acf7-ffd9-4bc8-9ee3-ead3a9b513f4", "process_id": "17301", "command": [ "/bin/sh", "-lc", "for p in 13466 13467; do if test -e /proc/$p/stat; then awk -v p=$p '{print p,$3,$14,$15,$24}' /proc/$p/stat; cat /proc/$p/wchan; fi; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in 13466 13467; do if test -e /proc/$p/stat; then awk -v p=$p '{print p,$3,$14,$15,$24}' /proc/$p/stat; cat /proc/$p/wchan; fi; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3380 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T02:07:02.266Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d74016f487d2a2f0e27e9d445810", "usage": { "input_tokens": 104024, "cached_input_tokens": 103680, "cache_write_input_tokens": 0, "output_tokens": 173, "reasoning_output_tokens": 69, "total_tokens": 104197 }, "turn_token_usage": { "input_tokens": 6695127, "cached_input_tokens": 6584448, "cache_write_input_tokens": 0, "output_tokens": 53249, "reasoning_output_tokens": 23323, "total_tokens": 6748376 }, "thread_token_usage": { "input_tokens": 6695127, "cached_input_tokens": 6584448, "cache_write_input_tokens": 0, "output_tokens": 53249, "reasoning_output_tokens": 23323, "total_tokens": 6748376 } } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-25T02:07:10.491Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:07:10.491Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:07:21.867Z", "callId": "call_A7JO3oRf2n2ea5zVWDUYWhIt", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_rank.py\\n@@\\n def rank_model(\\n cache_path: Path,\\n checkpoint: Path,\\n+ checkpoint2: Path | None,\\n output: Path,\\n alpha: float,\\n+ ensemble_weight: float,\\n threads: int,\\n trend_override: Path | None = None,\\n ) -> None:\\n@@\\n- model, _ = load_model(checkpoint)\\n+ model, _ = load_model(checkpoint)\\n+ model2 = load_model(checkpoint2)[0] if checkpoint2 is not None else None\\n@@\\n with torch.inference_mode():\\n item_vec = model.encode_item(\\n@@\\n alpha,\\n )\\n+ if model2 is not None:\\n+ item_vec2 = model2.encode_item(\\n+ item_ids,\\n+ tensors[\\\"item_features\\\"][inference_ci],\\n+ request_trend,\\n+ 0.0,\\n+ )\\n+ # Concatenation makes the ensemble itself one exact MIPS vector:\\n+ # dot([sqrt(w)u1,sqrt(1-w)u2,1],\\n+ # [sqrt(w)i1,sqrt(1-w)i2,alpha*trend]).\\n+ w1 = math.sqrt(ensemble_weight)\\n+ w2 = math.sqrt(1.0 - ensemble_weight)\\n+ item_vec = torch.cat(\\n+ [\\n+ item_vec[:, :-1] * w1,\\n+ item_vec2[:, :-1] * w2,\\n+ (request_trend * alpha).unsqueeze(1),\\n+ ],\\n+ dim=1,\\n+ )\\n@@\\n user_vec = model.encode_user(\\n@@\\n tensors[\\\"profile_stats\\\"][pid_t],\\n )\\n+ if model2 is not None:\\n+ user_vec2 = model2.encode_user(\\n+ tensors[\\\"recent\\\"][pid_t],\\n+ tensors[\\\"organic\\\"][pid_t],\\n+ tensors[\\\"liked\\\"][pid_t],\\n+ tensors[\\\"disliked\\\"][pid_t],\\n+ tensors[\\\"profile_stats\\\"][pid_t],\\n+ )\\n+ user_vec = torch.cat(\\n+ [\\n+ user_vec[:, :-1] * w1,\\n+ user_vec2[:, :-1] * w2,\\n+ torch.ones_like(user_vec[:, :1]),\\n+ ],\\n+ dim=1,\\n+ )\\n@@\\n parser.add_argument(\\\"--checkpoint\\\", type=Path)\\n+ parser.add_argument(\\\"--checkpoint2\\\", type=Path)\\n@@\\n parser.add_argument(\\\"--alpha\\\", type=float, default=1.0)\\n+ parser.add_argument(\\\"--ensemble-weight\\\", type=float, default=0.5)\\n@@\\n cache,\\n checkpoint,\\n+ args.checkpoint2,\\n output,\\n args.alpha,\\n+ args.ensemble_weight,\\n args.threads,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T02:07:21.946Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:07:21.878Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-49bda518-f5f7-458a-9102-4852fb047952", "changes": { "/app/solution/train_and_rank.py": { "type": "update", "unified_diff": "@@ -672,4 +672,6 @@\n checkpoint: Path,\n+ checkpoint2: Path | None,\n output: Path,\n alpha: float,\n+ ensemble_weight: float,\n threads: int,\n@@ -681,2 +683,3 @@\n model, _ = load_model(checkpoint)\n+ model2 = load_model(checkpoint2)[0] if checkpoint2 is not None else None\n nusers = len(cache[\"users\"])\n@@ -699,2 +702,22 @@\n )\n+ if model2 is not None:\n+ item_vec2 = model2.encode_item(\n+ item_ids,\n+ tensors[\"item_features\"][inference_ci],\n+ request_trend,\n+ 0.0,\n+ )\n+ # Concatenation makes the ensemble itself one exact MIPS vector:\n+ # dot([sqrt(w)u1,sqrt(1-w)u2,1],\n+ # [sqrt(w)i1,sqrt(1-w)i2,alpha*trend]).\n+ w1 = math.sqrt(ensemble_weight)\n+ w2 = math.sqrt(1.0 - ensemble_weight)\n+ item_vec = torch.cat(\n+ [\n+ item_vec[:, :-1] * w1,\n+ item_vec2[:, :-1] * w2,\n+ (request_trend * alpha).unsqueeze(1),\n+ ],\n+ dim=1,\n+ )\n \n@@ -729,2 +752,18 @@\n )\n+ if model2 is not None:\n+ user_vec2 = model2.encode_user(\n+ tensors[\"recent\"][pid_t],\n+ tensors[\"organic\"][pid_t],\n+ tensors[\"liked\"][pid_t],\n+ tensors[\"disliked\"][pid_t],\n+ tensors[\"profile_stats\"][pid_t],\n+ )\n+ user_vec = torch.cat(\n+ [\n+ user_vec[:, :-1] * w1,\n+ user_vec2[:, :-1] * w2,\n+ torch.ones_like(user_vec[:, :1]),\n+ ],\n+ dim=1,\n+ )\n scores = user_vec @ item_vec.T\n@@ -760,2 +799,3 @@\n parser.add_argument(\"--checkpoint\", type=Path)\n+ parser.add_argument(\"--checkpoint2\", type=Path)\n parser.add_argument(\"--init-checkpoint\", type=Path)\n@@ -776,2 +816,3 @@\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n+ parser.add_argument(\"--ensemble-weight\", type=float, default=0.5)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n@@ -802,4 +843,6 @@\n checkpoint,\n+ args.checkpoint2,\n output,\n args.alpha,\n+ args.ensemble_weight,\n args.threads,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T02:07:21.945Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d746a0c487d2a7947f19e7f84642", "usage": { "input_tokens": 104220, "cached_input_tokens": 103808, "cache_write_input_tokens": 0, "output_tokens": 1014, "reasoning_output_tokens": 277, "total_tokens": 105234 }, "turn_token_usage": { "input_tokens": 6799347, "cached_input_tokens": 6688256, "cache_write_input_tokens": 0, "output_tokens": 54263, "reasoning_output_tokens": 23600, "total_tokens": 6853610 }, "thread_token_usage": { "input_tokens": 6799347, "cached_input_tokens": 6688256, "cache_write_input_tokens": 0, "output_tokens": 54263, "reasoning_output_tokens": 23600, "total_tokens": 6853610 } } } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-25T02:07:24.344Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:07:24.344Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:07:25.594Z", "callId": "call_Ded6JluM3gTboG2FTJKyOoFX", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -70 /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":6000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:07:25.761Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n \"rank\": np.concatenate(result_rank).astype(np.int64),\n }\n )\n ranking.to_parquet(output, index=False)\n print(f\"saved {output} rows={len(ranking):,} alpha={alpha}\", flush=True)\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"validation\")\n parser.add_argument(\"--mode\", choices=[\"prepare\", \"train\", \"rank\", \"all\"], default=\"all\")\n parser.add_argument(\"--windows\", type=int, default=28)\n parser.add_argument(\"--force-cache\", action=\"store_true\")\n parser.add_argument(\"--name\", default=\"two_tower_validation\")\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--checkpoint2\", type=Path)\n parser.add_argument(\"--init-checkpoint\", type=Path)\n parser.add_argument(\"--trend-override\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n parser.add_argument(\"--steps\", type=int, default=3000)\n parser.add_argument(\"--batch-size\", type=int, default=256)\n parser.add_argument(\"--negatives\", type=int, default=48)\n parser.add_argument(\"--like-fraction\", type=float, default=0.28)\n parser.add_argument(\"--lr\", type=float, default=0.002)\n parser.add_argument(\"--weight-decay\", type=float, default=2e-5)\n parser.add_argument(\"--window-half-life\", type=float, default=16.0)\n parser.add_argument(\"--dim\", type=int, default=64)\n parser.add_argument(\"--hist-dim\", type=int, default=48)\n parser.add_argument(\"--id-dim\", type=int, default=48)\n parser.add_argument(\"--semantic-scale\", type=float, default=10.0)\n parser.add_argument(\"--train-alpha\", type=float, default=1.0)\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n parser.add_argument(\"--ensemble-weight\", type=float, default=0.5)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--log-every\", type=int, default=100)\n parser.add_argument(\"--save-every\", type=int, default=1000)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n OUT.mkdir(parents=True, exist_ok=True)\n print(json.dumps(vars(args), default=str, indent=2), flush=True)\n cache = build_cache(args.stage, args.windows, args.force_cache)\n if args.mode == \"prepare\":\n return\n checkpoint = args.checkpoint\n if args.mode in (\"train\", \"all\"):\n checkpoint = train_model(cache, args)\n if args.mode in (\"rank\", \"all\"):\n if checkpoint is None:\n raise SystemExit(\"--checkpoint is required in rank mode\")\n output = args.output or OUT / (\n \"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\"\n )\n rank_model(\n cache,\n checkpoint,\n args.checkpoint2,\n output,\n args.alpha,\n args.ensemble_weight,\n args.threads,\n args.trend_override,\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:07:25.750Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d75a550c87d292a85adcef3f5322", "usage": { "input_tokens": 105258, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 9, "total_tokens": 105346 }, "turn_token_usage": { "input_tokens": 6904605, "cached_input_tokens": 6792320, "cache_write_input_tokens": 0, "output_tokens": 54351, "reasoning_output_tokens": 23609, "total_tokens": 6958956 }, "thread_token_usage": { "input_tokens": 6904605, "cached_input_tokens": 6792320, "cache_write_input_tokens": 0, "output_tokens": 54351, "reasoning_output_tokens": 23609, "total_tokens": 6958956 } } }, { "kind": "context", "timestamp": "2026-09-25T02:07:25.757Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f5768a05-a0ff-4a18-ad4e-f9bd1f26043f", "process_id": "18845", "command": [ "/bin/sh", "-lc", "tail -70 /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -70 /app/solution/train_and_rank.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"rank\": np.concatenate(result_rank).astype(np.int64),\n }\n )\n ranking.to_parquet(output, index=False)\n print(f\"saved {output} rows={len(ranking):,} alpha={alpha}\", flush=True)\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"validation\")\n parser.add_argument(\"--mode\", choices=[\"prepare\", \"train\", \"rank\", \"all\"], default=\"all\")\n parser.add_argument(\"--windows\", type=int, default=28)\n parser.add_argument(\"--force-cache\", action=\"store_true\")\n parser.add_argument(\"--name\", default=\"two_tower_validation\")\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--checkpoint2\", type=Path)\n parser.add_argument(\"--init-checkpoint\", type=Path)\n parser.add_argument(\"--trend-override\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n parser.add_argument(\"--steps\", type=int, default=3000)\n parser.add_argument(\"--batch-size\", type=int, default=256)\n parser.add_argument(\"--negatives\", type=int, default=48)\n parser.add_argument(\"--like-fraction\", type=float, default=0.28)\n parser.add_argument(\"--lr\", type=float, default=0.002)\n parser.add_argument(\"--weight-decay\", type=float, default=2e-5)\n parser.add_argument(\"--window-half-life\", type=float, default=16.0)\n parser.add_argument(\"--dim\", type=int, default=64)\n parser.add_argument(\"--hist-dim\", type=int, default=48)\n parser.add_argument(\"--id-dim\", type=int, default=48)\n parser.add_argument(\"--semantic-scale\", type=float, default=10.0)\n parser.add_argument(\"--train-alpha\", type=float, default=1.0)\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n parser.add_argument(\"--ensemble-weight\", type=float, default=0.5)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--log-every\", type=int, default=100)\n parser.add_argument(\"--save-every\", type=int, default=1000)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n OUT.mkdir(parents=True, exist_ok=True)\n print(json.dumps(vars(args), default=str, indent=2), flush=True)\n cache = build_cache(args.stage, args.windows, args.force_cache)\n if args.mode == \"prepare\":\n return\n checkpoint = args.checkpoint\n if args.mode in (\"train\", \"all\"):\n checkpoint = train_model(cache, args)\n if args.mode in (\"rank\", \"all\"):\n if checkpoint is None:\n raise SystemExit(\"--checkpoint is required in rank mode\")\n output = args.output or OUT / (\n \"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\"\n )\n rank_model(\n cache,\n checkpoint,\n args.checkpoint2,\n output,\n args.alpha,\n args.ensemble_weight,\n args.threads,\n args.trend_override,\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": " \"rank\": np.concatenate(result_rank).astype(np.int64),\n }\n )\n ranking.to_parquet(output, index=False)\n print(f\"saved {output} rows={len(ranking):,} alpha={alpha}\", flush=True)\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"validation\")\n parser.add_argument(\"--mode\", choices=[\"prepare\", \"train\", \"rank\", \"all\"], default=\"all\")\n parser.add_argument(\"--windows\", type=int, default=28)\n parser.add_argument(\"--force-cache\", action=\"store_true\")\n parser.add_argument(\"--name\", default=\"two_tower_validation\")\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--checkpoint2\", type=Path)\n parser.add_argument(\"--init-checkpoint\", type=Path)\n parser.add_argument(\"--trend-override\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n parser.add_argument(\"--steps\", type=int, default=3000)\n parser.add_argument(\"--batch-size\", type=int, default=256)\n parser.add_argument(\"--negatives\", type=int, default=48)\n parser.add_argument(\"--like-fraction\", type=float, default=0.28)\n parser.add_argument(\"--lr\", type=float, default=0.002)\n parser.add_argument(\"--weight-decay\", type=float, default=2e-5)\n parser.add_argument(\"--window-half-life\", type=float, default=16.0)\n parser.add_argument(\"--dim\", type=int, default=64)\n parser.add_argument(\"--hist-dim\", type=int, default=48)\n parser.add_argument(\"--id-dim\", type=int, default=48)\n parser.add_argument(\"--semantic-scale\", type=float, default=10.0)\n parser.add_argument(\"--train-alpha\", type=float, default=1.0)\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n parser.add_argument(\"--ensemble-weight\", type=float, default=0.5)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--log-every\", type=int, default=100)\n parser.add_argument(\"--save-every\", type=int, default=1000)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n OUT.mkdir(parents=True, exist_ok=True)\n print(json.dumps(vars(args), default=str, indent=2), flush=True)\n cache = build_cache(args.stage, args.windows, args.force_cache)\n if args.mode == \"prepare\":\n return\n checkpoint = args.checkpoint\n if args.mode in (\"train\", \"all\"):\n checkpoint = train_model(cache, args)\n if args.mode in (\"rank\", \"all\"):\n if checkpoint is None:\n raise SystemExit(\"--checkpoint is required in rank mode\")\n output = args.output or OUT / (\n \"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\"\n )\n rank_model(\n cache,\n checkpoint,\n args.checkpoint2,\n output,\n args.alpha,\n args.ensemble_weight,\n args.threads,\n args.trend_override,\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5630 }, "formatted_output": " \"rank\": np.concatenate(result_rank).astype(np.int64),\n }\n )\n ranking.to_parquet(output, index=False)\n print(f\"saved {output} rows={len(ranking):,} alpha={alpha}\", flush=True)\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"validation\")\n parser.add_argument(\"--mode\", choices=[\"prepare\", \"train\", \"rank\", \"all\"], default=\"all\")\n parser.add_argument(\"--windows\", type=int, default=28)\n parser.add_argument(\"--force-cache\", action=\"store_true\")\n parser.add_argument(\"--name\", default=\"two_tower_validation\")\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--checkpoint2\", type=Path)\n parser.add_argument(\"--init-checkpoint\", type=Path)\n parser.add_argument(\"--trend-override\", type=Path)\n parser.add_argument(\"--output\", type=Path)\n parser.add_argument(\"--steps\", type=int, default=3000)\n parser.add_argument(\"--batch-size\", type=int, default=256)\n parser.add_argument(\"--negatives\", type=int, default=48)\n parser.add_argument(\"--like-fraction\", type=float, default=0.28)\n parser.add_argument(\"--lr\", type=float, default=0.002)\n parser.add_argument(\"--weight-decay\", type=float, default=2e-5)\n parser.add_argument(\"--window-half-life\", type=float, default=16.0)\n parser.add_argument(\"--dim\", type=int, default=64)\n parser.add_argument(\"--hist-dim\", type=int, default=48)\n parser.add_argument(\"--id-dim\", type=int, default=48)\n parser.add_argument(\"--semantic-scale\", type=float, default=10.0)\n parser.add_argument(\"--train-alpha\", type=float, default=1.0)\n parser.add_argument(\"--alpha\", type=float, default=1.0)\n parser.add_argument(\"--ensemble-weight\", type=float, default=0.5)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--log-every\", type=int, default=100)\n parser.add_argument(\"--save-every\", type=int, default=1000)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n OUT.mkdir(parents=True, exist_ok=True)\n print(json.dumps(vars(args), default=str, indent=2), flush=True)\n cache = build_cache(args.stage, args.windows, args.force_cache)\n if args.mode == \"prepare\":\n return\n checkpoint = args.checkpoint\n if args.mode in (\"train\", \"all\"):\n checkpoint = train_model(cache, args)\n if args.mode in (\"rank\", \"all\"):\n if checkpoint is None:\n raise SystemExit(\"--checkpoint is required in rank mode\")\n output = args.output or OUT / (\n \"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\"\n )\n rank_model(\n cache,\n checkpoint,\n args.checkpoint2,\n output,\n args.alpha,\n args.ensemble_weight,\n args.threads,\n args.trend_override,\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-25T02:07:27.559Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:07:27.559Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:07:30.723Z", "callId": "call_KCTA5NqAku8E9SojrqSJzzZ0", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --checkpoint2 /app/solution/two_tower_seed2_final.pt --ensemble-weight 0.5 --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.55 --output /app/solution/validation.parquet >/tmp/validation_rank.log && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && tail -6 /tmp/validation_rank.log\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:07:39.274Z", "content": "Script completed\nWall time 8.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038324,\n \"quality_users\": 4263\n }\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/validation.parquet rows=845,600 alpha=0.55\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:07:30.864Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d75e389087d285a7b8f6c0f169a0", "usage": { "input_tokens": 106081, "cached_input_tokens": 105088, "cache_write_input_tokens": 0, "output_tokens": 199, "reasoning_output_tokens": 16, "total_tokens": 106280 }, "turn_token_usage": { "input_tokens": 7010686, "cached_input_tokens": 6897408, "cache_write_input_tokens": 0, "output_tokens": 54550, "reasoning_output_tokens": 23625, "total_tokens": 7065236 }, "thread_token_usage": { "input_tokens": 7010686, "cached_input_tokens": 6897408, "cache_write_input_tokens": 0, "output_tokens": 54550, "reasoning_output_tokens": 23625, "total_tokens": 7065236 } } }, { "kind": "context", "timestamp": "2026-09-25T02:07:39.270Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c439c70b-1506-4f0b-a50e-64cb40fc28a5", "process_id": "32272", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --checkpoint2 /app/solution/two_tower_seed2_final.pt --ensemble-weight 0.5 --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.55 --output /app/solution/validation.parquet >/tmp/validation_rank.log && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && tail -6 /tmp/validation_rank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage validation --mode rank --windows 28 --checkpoint /app/solution/two_tower_v3_resume_step1000.pt --checkpoint2 /app/solution/two_tower_seed2_final.pt --ensemble-weight 0.5 --trend-override /app/solution/trend_u3_sqrt_validation.npy --alpha 0.55 --output /app/solution/validation.parquet >/tmp/validation_rank.log && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && tail -6 /tmp/validation_rank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038324,\n \"quality_users\": 4263\n }\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/validation.parquet rows=845,600 alpha=0.55\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038324,\n \"quality_users\": 4263\n }\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/validation.parquet rows=845,600 alpha=0.55\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 379343994 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038324,\n \"quality_users\": 4263\n }\n}\nusing existing cache /app/solution/cache_validation_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/validation.parquet rows=845,600 alpha=0.55\n" } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-25T02:07:49.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:07:49.152Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:07:59.841Z", "callId": "call_w5jnozNcHaBAozj4bezucSrL", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_rank.py\\n@@\\n def load_model(checkpoint: Path) -> tuple[TwoTowerRanker, dict]:\\n@@\\n return model, payload\\n \\n \\n+def distinct_listener_trend(stage: str, catalog: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Three-day distinct-organic-listener prior selected on public backtest.\\\"\\\"\\\"\\n+ cutoff = TRAIN_END if stage == \\\"validation\\\" else VAL_END\\n+ frame = read_events(\\n+ \\\"listens\\\", stage, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ )\\n+ frame = frame[\\n+ (frame.timestamp >= cutoff - 7 * 24 * 60 * 60)\\n+ & (frame.timestamp < cutoff)\\n+ & (frame.is_organic == 1)\\n+ & (frame.played_ratio_pct >= 80)\\n+ ].copy()\\n+ order = np.argsort(catalog)\\n+ keep, idx = map_catalog(\\n+ frame.item_id.to_numpy(dtype=np.int64), catalog[order], order\\n+ )\\n+ frame = frame.loc[keep, [\\\"uid\\\", \\\"timestamp\\\"]].copy()\\n+ frame[\\\"item_idx\\\"] = idx[keep].astype(np.int32)\\n+\\n+ counts = []\\n+ for days in (3, 7):\\n+ selected = frame[frame.timestamp >= cutoff - days * 24 * 60 * 60]\\n+ selected = selected.drop_duplicates([\\\"uid\\\", \\\"item_idx\\\"])\\n+ counts.append(\\n+ np.bincount(selected.item_idx, minlength=len(catalog)).astype(np.float32)\\n+ )\\n+ raw = np.sqrt(counts[0]) + 1e-4 * np.sqrt(counts[1])\\n+ return ((raw - raw.mean()) / (raw.std() + 1e-6)).astype(np.float32)\\n+\\n+\\n def rank_model(\\n@@\\n threads: int,\\n trend_override: Path | None = None,\\n+ trend_mode: str = \\\"cache\\\",\\n ) -> None:\\n@@\\n request_trend = tensors[\\\"trend\\\"][inference_ci]\\n- if trend_override is not None:\\n+ if trend_mode == \\\"unique3_sqrt\\\":\\n+ stage = \\\"validation\\\" if \\\"validation\\\" in cache_path.name else \\\"final\\\"\\n+ request_trend = torch.from_numpy(distinct_listener_trend(stage, cache[\\\"catalog\\\"]))\\n+ elif trend_override is not None:\\n@@\\n parser.add_argument(\\\"--trend-override\\\", type=Path)\\n+ parser.add_argument(\\\"--trend-mode\\\", choices=[\\\"cache\\\", \\\"unique3_sqrt\\\"], default=\\\"cache\\\")\\n@@\\n args.threads,\\n args.trend_override,\\n+ args.trend_mode,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T02:07:59.959Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:07:59.850Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-65317ab5-2631-4ce7-b0d7-dbab5189a7b6", "changes": { "/app/solution/train_and_rank.py": { "type": "update", "unified_diff": "@@ -669,2 +669,32 @@\n \n+def distinct_listener_trend(stage: str, catalog: np.ndarray) -> np.ndarray:\n+ \"\"\"Three-day distinct-organic-listener prior selected on public backtest.\"\"\"\n+ cutoff = TRAIN_END if stage == \"validation\" else VAL_END\n+ frame = read_events(\n+ \"listens\", stage, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+ )\n+ frame = frame[\n+ (frame.timestamp >= cutoff - 7 * 24 * 60 * 60)\n+ & (frame.timestamp < cutoff)\n+ & (frame.is_organic == 1)\n+ & (frame.played_ratio_pct >= 80)\n+ ].copy()\n+ order = np.argsort(catalog)\n+ keep, idx = map_catalog(\n+ frame.item_id.to_numpy(dtype=np.int64), catalog[order], order\n+ )\n+ frame = frame.loc[keep, [\"uid\", \"timestamp\"]].copy()\n+ frame[\"item_idx\"] = idx[keep].astype(np.int32)\n+\n+ counts = []\n+ for days in (3, 7):\n+ selected = frame[frame.timestamp >= cutoff - days * 24 * 60 * 60]\n+ selected = selected.drop_duplicates([\"uid\", \"item_idx\"])\n+ counts.append(\n+ np.bincount(selected.item_idx, minlength=len(catalog)).astype(np.float32)\n+ )\n+ raw = np.sqrt(counts[0]) + 1e-4 * np.sqrt(counts[1])\n+ return ((raw - raw.mean()) / (raw.std() + 1e-6)).astype(np.float32)\n+\n+\n def rank_model(\n@@ -678,2 +708,3 @@\n trend_override: Path | None = None,\n+ trend_mode: str = \"cache\",\n ) -> None:\n@@ -690,3 +721,6 @@\n request_trend = tensors[\"trend\"][inference_ci]\n- if trend_override is not None:\n+ if trend_mode == \"unique3_sqrt\":\n+ stage = \"validation\" if \"validation\" in cache_path.name else \"final\"\n+ request_trend = torch.from_numpy(distinct_listener_trend(stage, cache[\"catalog\"]))\n+ elif trend_override is not None:\n overridden = np.load(trend_override, allow_pickle=False)\n@@ -802,2 +836,3 @@\n parser.add_argument(\"--trend-override\", type=Path)\n+ parser.add_argument(\"--trend-mode\", choices=[\"cache\", \"unique3_sqrt\"], default=\"cache\")\n parser.add_argument(\"--output\", type=Path)\n@@ -849,2 +884,3 @@\n args.trend_override,\n+ args.trend_mode,\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_and_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T02:07:59.957Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d76b7a1887d2b3925e7f90d9e422", "usage": { "input_tokens": 106426, "cached_input_tokens": 105856, "cache_write_input_tokens": 0, "output_tokens": 1049, "reasoning_output_tokens": 349, "total_tokens": 107475 }, "turn_token_usage": { "input_tokens": 7117112, "cached_input_tokens": 7003264, "cache_write_input_tokens": 0, "output_tokens": 55599, "reasoning_output_tokens": 23974, "total_tokens": 7172711 }, "thread_token_usage": { "input_tokens": 7117112, "cached_input_tokens": 7003264, "cache_write_input_tokens": 0, "output_tokens": 55599, "reasoning_output_tokens": 23974, "total_tokens": 7172711 } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-25T02:08:07.306Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:08:07.306Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:08:07.308Z", "text": "Model selection is complete: the preserved public artifact scores 0.038324 over 4,263 scored users. I’m now refitting the exact selected recipe on train+validation, where validation becomes observed history and the newest supervised rolling window. I’ll train the two fixed seeds for the selected 5k+1k schedule, build the distinct-listener trend from the last three observed days, and validate every final row against test eligibility." }, { "kind": "tool", "timestamp": "2026-09-25T02:08:07.308Z", "callId": "call_b0A7xyVslbwz9Pw7ZykJJcAO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py && /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode prepare --windows 28\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:08:34.187Z", "content": "Script completed\nWall time 26.9 seconds\nOutput:\n\n{\"chunk_id\":\"d09d4f\",\"wall_time_seconds\":26.700093707,\"exit_code\":0,\"original_token_count\":232,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"prepare\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": null,\\n \\\"checkpoint2\\\": null,\\n \\\"init_checkpoint\\\": null,\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"cache\\\",\\n \\\"output\\\": null,\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 1.0,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\ncatalog listens=32,261,384, users=9,223, items=30,000 (9.5s)\\nitem features built (11.1s)\\nprofiles 2,000/9,223\\nprofiles 4,000/9,223\\nprofiles 6,000/9,223\\nprofiles 8,000/9,223\\nprofiles built (16.5s)\\ntraining pairs=957,956, grade3=169,020 (22.5s)\\nsaved /app/solution/cache_final_w28.npz (328.3 MB, 22.6s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:08:07.395Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d7804d8487d28890011ce6c6bb2c", "usage": { "input_tokens": 107499, "cached_input_tokens": 106240, "cache_write_input_tokens": 0, "output_tokens": 207, "reasoning_output_tokens": 16, "total_tokens": 107706 }, "turn_token_usage": { "input_tokens": 7224611, "cached_input_tokens": 7109504, "cache_write_input_tokens": 0, "output_tokens": 55806, "reasoning_output_tokens": 23990, "total_tokens": 7280417 }, "thread_token_usage": { "input_tokens": 7224611, "cached_input_tokens": 7109504, "cache_write_input_tokens": 0, "output_tokens": 55806, "reasoning_output_tokens": 23990, "total_tokens": 7280417 } } }, { "kind": "context", "timestamp": "2026-09-25T02:08:34.172Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e4784e44-cbed-4c52-b3c0-c68c2ad2a6a8", "process_id": "96013", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py && /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode prepare --windows 28" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_and_rank.py && /opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode prepare --windows 28" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=32,261,384, users=9,223, items=30,000 (9.5s)\nitem features built (11.1s)\nprofiles 2,000/9,223\nprofiles 4,000/9,223\nprofiles 6,000/9,223\nprofiles 8,000/9,223\nprofiles built (16.5s)\ntraining pairs=957,956, grade3=169,020 (22.5s)\nsaved /app/solution/cache_final_w28.npz (328.3 MB, 22.6s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=32,261,384, users=9,223, items=30,000 (9.5s)\nitem features built (11.1s)\nprofiles 2,000/9,223\nprofiles 4,000/9,223\nprofiles 6,000/9,223\nprofiles 8,000/9,223\nprofiles built (16.5s)\ntraining pairs=957,956, grade3=169,020 (22.5s)\nsaved /app/solution/cache_final_w28.npz (328.3 MB, 22.6s)\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 700091257 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"prepare\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\ncatalog listens=32,261,384, users=9,223, items=30,000 (9.5s)\nitem features built (11.1s)\nprofiles 2,000/9,223\nprofiles 4,000/9,223\nprofiles 6,000/9,223\nprofiles 8,000/9,223\nprofiles built (16.5s)\ntraining pairs=957,956, grade3=169,020 (22.5s)\nsaved /app/solution/cache_final_w28.npz (328.3 MB, 22.6s)\n" } } } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-25T02:08:39.298Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:08:39.298Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:08:41.181Z", "callId": "call_EdbciNahFXRcNITcvPDJNNOM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:09:11.361Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"233bca\",\"wall_time_seconds\":30.001476169,\"session_id\":18282,\"original_token_count\":201,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_final_seed1_base\\\",\\n \\\"checkpoint\\\": null,\\n \\\"checkpoint2\\\": null,\\n \\\"init_checkpoint\\\": null,\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"cache\\\",\\n \\\"output\\\": null,\\n \\\"steps\\\": 5000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 500,\\n \\\"save_every\\\": 5000\\n}\\nusing existing cache /app/solution/cache_final_w28.npz\\nstep=1/5000 loss=4.7360 ema=4.7360 pairs/s=4,474 window=9\\nstep=500/5000 loss=3.9332 ema=3.9510 pairs/s=5,926 window=25\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:08:41.286Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d7a27de087d2a1fc525094791217", "usage": { "input_tokens": 108165, "cached_input_tokens": 107264, "cache_write_input_tokens": 0, "output_tokens": 269, "reasoning_output_tokens": 127, "total_tokens": 108434 }, "turn_token_usage": { "input_tokens": 7332776, "cached_input_tokens": 7216768, "cache_write_input_tokens": 0, "output_tokens": 56075, "reasoning_output_tokens": 24117, "total_tokens": 7388851 }, "thread_token_usage": { "input_tokens": 7332776, "cached_input_tokens": 7216768, "cache_write_input_tokens": 0, "output_tokens": 56075, "reasoning_output_tokens": 24117, "total_tokens": 7388851 } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-25T02:09:12.683Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:09:12.683Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:09:13.395Z", "callId": "call_y8CPPDuSCSojE4WBh6YJCC1G", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:09:43.441Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"48c097\",\"wall_time_seconds\":30.001021813,\"session_id\":18282,\"original_token_count\":16,\"output\":\"step=1000/5000 loss=3.8981 ema=3.8926 pairs/s=6,090 window=11\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:09:13.536Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d7c79ba087d283407d12ca7a9699", "usage": { "input_tokens": 108835, "cached_input_tokens": 108032, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 108889 }, "turn_token_usage": { "input_tokens": 7441611, "cached_input_tokens": 7324800, "cache_write_input_tokens": 0, "output_tokens": 56129, "reasoning_output_tokens": 24123, "total_tokens": 7497740 }, "thread_token_usage": { "input_tokens": 7441611, "cached_input_tokens": 7324800, "cache_write_input_tokens": 0, "output_tokens": 56129, "reasoning_output_tokens": 24123, "total_tokens": 7497740 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:09:45.491Z", "callId": "call_G8ezSY727N9yu4i65rl1lMUc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:10:15.536Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"6adb85\",\"wall_time_seconds\":30.001749823,\"session_id\":18282,\"original_token_count\":31,\"output\":\"step=1500/5000 loss=3.8792 ema=3.8416 pairs/s=6,186 window=18\\nstep=2000/5000 loss=3.7113 ema=3.7920 pairs/s=6,230 window=27\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:09:45.646Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d7e7c17c87d28bf6d1f2712d1fe1", "usage": { "input_tokens": 108974, "cached_input_tokens": 108672, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 109020 }, "turn_token_usage": { "input_tokens": 7550585, "cached_input_tokens": 7433472, "cache_write_input_tokens": 0, "output_tokens": 56175, "reasoning_output_tokens": 24123, "total_tokens": 7606760 }, "thread_token_usage": { "input_tokens": 7550585, "cached_input_tokens": 7433472, "cache_write_input_tokens": 0, "output_tokens": 56175, "reasoning_output_tokens": 24123, "total_tokens": 7606760 } } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-25T02:10:17.795Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:10:17.795Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:10:21.382Z", "text": "The final combined-history cache contains 957,956 rolling discovery pairs, including the former public window at the exact production cutoff. Seed 1 is following the selected validation learning curve; checkpoints and the already-valid public ranking remain safely stored while the refit runs." }, { "kind": "tool", "timestamp": "2026-09-25T02:10:22.331Z", "callId": "call_qmBlKqEwzqL5kfV0tKAp8SBf", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:10:52.367Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"542c93\",\"wall_time_seconds\":30.001516211,\"session_id\":18282,\"original_token_count\":31,\"output\":\"step=2500/5000 loss=3.6224 ema=3.7241 pairs/s=6,171 window=27\\nstep=3000/5000 loss=3.5661 ema=3.6776 pairs/s=6,197 window=23\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:10:22.442Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d807f71087d2b422eea3195c1ffc", "usage": { "input_tokens": 109134, "cached_input_tokens": 108800, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 14, "total_tokens": 109255 }, "turn_token_usage": { "input_tokens": 7659719, "cached_input_tokens": 7542272, "cache_write_input_tokens": 0, "output_tokens": 56296, "reasoning_output_tokens": 24137, "total_tokens": 7716015 }, "thread_token_usage": { "input_tokens": 7659719, "cached_input_tokens": 7542272, "cache_write_input_tokens": 0, "output_tokens": 56296, "reasoning_output_tokens": 24137, "total_tokens": 7716015 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:10:54.858Z", "callId": "call_lQjjEaZfpsbb6bPV78IYIAKz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:11:24.898Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1a7bc7\",\"wall_time_seconds\":30.001057803,\"session_id\":18282,\"original_token_count\":16,\"output\":\"step=3500/5000 loss=3.6053 ema=3.6206 pairs/s=6,220 window=19\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:10:54.985Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d82cb2d887d28056ed4deea14552", "usage": { "input_tokens": 109369, "cached_input_tokens": 108928, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 109415 }, "turn_token_usage": { "input_tokens": 7769088, "cached_input_tokens": 7651200, "cache_write_input_tokens": 0, "output_tokens": 56342, "reasoning_output_tokens": 24137, "total_tokens": 7825430 }, "thread_token_usage": { "input_tokens": 7769088, "cached_input_tokens": 7651200, "cache_write_input_tokens": 0, "output_tokens": 56342, "reasoning_output_tokens": 24137, "total_tokens": 7825430 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:11:26.908Z", "callId": "call_M72CIFOPXzHR2aozhijntodh", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:11:56.939Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"3383a7\",\"wall_time_seconds\":30.001161082,\"session_id\":18282,\"original_token_count\":31,\"output\":\"step=4000/5000 loss=3.7138 ema=3.5523 pairs/s=6,237 window=9\\nstep=4500/5000 loss=3.5849 ema=3.5121 pairs/s=6,245 window=11\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:11:27.014Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d84d2bcc87d2a523356448776b34", "usage": { "input_tokens": 109502, "cached_input_tokens": 109184, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 109548 }, "turn_token_usage": { "input_tokens": 7878590, "cached_input_tokens": 7760384, "cache_write_input_tokens": 0, "output_tokens": 56388, "reasoning_output_tokens": 24137, "total_tokens": 7934978 }, "thread_token_usage": { "input_tokens": 7878590, "cached_input_tokens": 7760384, "cache_write_input_tokens": 0, "output_tokens": 56388, "reasoning_output_tokens": 24137, "total_tokens": 7934978 } } } ] }, { "id": 129, "role": "agent", "timestamp": "2026-09-25T02:11:58.836Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:11:58.836Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:11:59.536Z", "callId": "call_axi0HqzsiMbslyXyt9AJvMJH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18282,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:12:12.907Z", "content": "Script completed\nWall time 13.3 seconds\nOutput:\n\n{\"chunk_id\":\"1b0cdb\",\"wall_time_seconds\":13.323199258,\"exit_code\":0,\"original_token_count\":32,\"output\":\"step=5000/5000 loss=3.2719 ema=3.4416 pairs/s=6,256 window=16\\nsaved model /app/solution/two_tower_final_seed1_base.pt (204.6s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:11:59.656Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d86d2b8887d2a54d5c6bb9bbc475", "usage": { "input_tokens": 109663, "cached_input_tokens": 109312, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 109717 }, "turn_token_usage": { "input_tokens": 7988253, "cached_input_tokens": 7869696, "cache_write_input_tokens": 0, "output_tokens": 56442, "reasoning_output_tokens": 24143, "total_tokens": 8044695 }, "thread_token_usage": { "input_tokens": 7988253, "cached_input_tokens": 7869696, "cache_write_input_tokens": 0, "output_tokens": 56442, "reasoning_output_tokens": 24143, "total_tokens": 8044695 } } }, { "kind": "context", "timestamp": "2026-09-25T02:12:12.898Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-106a3181-ae7c-4dda-bc27-fc84c745a5f6", "process_id": "18282", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.7360 ema=4.7360 pairs/s=4,474 window=9\nstep=500/5000 loss=3.9332 ema=3.9510 pairs/s=5,926 window=25\nstep=1000/5000 loss=3.8981 ema=3.8926 pairs/s=6,090 window=11\nstep=1500/5000 loss=3.8792 ema=3.8416 pairs/s=6,186 window=18\nstep=2000/5000 loss=3.7113 ema=3.7920 pairs/s=6,230 window=27\nstep=2500/5000 loss=3.6224 ema=3.7241 pairs/s=6,171 window=27\nstep=3000/5000 loss=3.5661 ema=3.6776 pairs/s=6,197 window=23\nstep=3500/5000 loss=3.6053 ema=3.6206 pairs/s=6,220 window=19\nstep=4000/5000 loss=3.7138 ema=3.5523 pairs/s=6,237 window=9\nstep=4500/5000 loss=3.5849 ema=3.5121 pairs/s=6,245 window=11\nstep=5000/5000 loss=3.2719 ema=3.4416 pairs/s=6,256 window=16\nsaved model /app/solution/two_tower_final_seed1_base.pt (204.6s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.7360 ema=4.7360 pairs/s=4,474 window=9\nstep=500/5000 loss=3.9332 ema=3.9510 pairs/s=5,926 window=25\nstep=1000/5000 loss=3.8981 ema=3.8926 pairs/s=6,090 window=11\nstep=1500/5000 loss=3.8792 ema=3.8416 pairs/s=6,186 window=18\nstep=2000/5000 loss=3.7113 ema=3.7920 pairs/s=6,230 window=27\nstep=2500/5000 loss=3.6224 ema=3.7241 pairs/s=6,171 window=27\nstep=3000/5000 loss=3.5661 ema=3.6776 pairs/s=6,197 window=23\nstep=3500/5000 loss=3.6053 ema=3.6206 pairs/s=6,220 window=19\nstep=4000/5000 loss=3.7138 ema=3.5523 pairs/s=6,237 window=9\nstep=4500/5000 loss=3.5849 ema=3.5121 pairs/s=6,245 window=11\nstep=5000/5000 loss=3.2719 ema=3.4416 pairs/s=6,256 window=16\nsaved model /app/solution/two_tower_final_seed1_base.pt (204.6s)\n", "exit_code": 0, "duration": { "secs": 211, "nanos": 548733076 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.7360 ema=4.7360 pairs/s=4,474 window=9\nstep=500/5000 loss=3.9332 ema=3.9510 pairs/s=5,926 window=25\nstep=1000/5000 loss=3.8981 ema=3.8926 pairs/s=6,090 window=11\nstep=1500/5000 loss=3.8792 ema=3.8416 pairs/s=6,186 window=18\nstep=2000/5000 loss=3.7113 ema=3.7920 pairs/s=6,230 window=27\nstep=2500/5000 loss=3.6224 ema=3.7241 pairs/s=6,171 window=27\nstep=3000/5000 loss=3.5661 ema=3.6776 pairs/s=6,197 window=23\nstep=3500/5000 loss=3.6053 ema=3.6206 pairs/s=6,220 window=19\nstep=4000/5000 loss=3.7138 ema=3.5523 pairs/s=6,237 window=9\nstep=4500/5000 loss=3.5849 ema=3.5121 pairs/s=6,245 window=11\nstep=5000/5000 loss=3.2719 ema=3.4416 pairs/s=6,256 window=16\nsaved model /app/solution/two_tower_final_seed1_base.pt (204.6s)\n" } } } ] }, { "id": 130, "role": "agent", "timestamp": "2026-09-25T02:12:14.121Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:12:14.121Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:12:16.833Z", "callId": "call_tf54uypRgKomNtS9Nrr13gTM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1 --init-checkpoint /app/solution/two_tower_final_seed1_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:12:47.014Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"b9d801\",\"wall_time_seconds\":30.00160464,\"session_id\":89779,\"original_token_count\":240,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_final_seed1\\\",\\n \\\"checkpoint\\\": null,\\n \\\"checkpoint2\\\": null,\\n \\\"init_checkpoint\\\": \\\"/app/solution/two_tower_final_seed1_base.pt\\\",\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"cache\\\",\\n \\\"output\\\": null,\\n \\\"steps\\\": 1000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.001,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 250,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_final_w28.npz\\ninitialized from /app/solution/two_tower_final_seed1_base.pt\\nstep=1/1000 loss=3.5603 ema=3.5603 pairs/s=4,534 window=9\\nstep=250/1000 loss=3.5925 ema=3.3900 pairs/s=5,524 window=6\\nstep=500/1000 loss=3.2385 ema=3.3694 pairs/s=5,913 window=25\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:12:16.924Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d87d159887d283cb994a8246a9a0", "usage": { "input_tokens": 109825, "cached_input_tokens": 109440, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 9, "total_tokens": 109992 }, "turn_token_usage": { "input_tokens": 8098078, "cached_input_tokens": 7979136, "cache_write_input_tokens": 0, "output_tokens": 56609, "reasoning_output_tokens": 24152, "total_tokens": 8154687 }, "thread_token_usage": { "input_tokens": 8098078, "cached_input_tokens": 7979136, "cache_write_input_tokens": 0, "output_tokens": 56609, "reasoning_output_tokens": 24152, "total_tokens": 8154687 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:12:49.437Z", "callId": "call_ymfAOsEMEX9YEzS42xzslWNj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":89779,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:13:05.809Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\n{\"chunk_id\":\"57aae7\",\"wall_time_seconds\":16.325415078,\"exit_code\":0,\"original_token_count\":46,\"output\":\"step=750/1000 loss=3.3046 ema=3.3363 pairs/s=6,039 window=27\\nstep=1000/1000 loss=3.4232 ema=3.3513 pairs/s=6,122 window=11\\nsaved model /app/solution/two_tower_final_seed1.pt (41.8s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:12:49.577Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d89f521c87d29bb54645bbffba7c", "usage": { "input_tokens": 110450, "cached_input_tokens": 109696, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 110496 }, "turn_token_usage": { "input_tokens": 8208528, "cached_input_tokens": 8088832, "cache_write_input_tokens": 0, "output_tokens": 56655, "reasoning_output_tokens": 24152, "total_tokens": 8265183 }, "thread_token_usage": { "input_tokens": 8208528, "cached_input_tokens": 8088832, "cache_write_input_tokens": 0, "output_tokens": 56655, "reasoning_output_tokens": 24152, "total_tokens": 8265183 } } }, { "kind": "context", "timestamp": "2026-09-25T02:13:05.802Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-942267ff-e5a3-4611-85fb-2df303839c9f", "process_id": "89779", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1 --init-checkpoint /app/solution/two_tower_final_seed1_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed1 --init-checkpoint /app/solution/two_tower_final_seed1_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 20260925" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed1_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed1_base.pt\nstep=1/1000 loss=3.5603 ema=3.5603 pairs/s=4,534 window=9\nstep=250/1000 loss=3.5925 ema=3.3900 pairs/s=5,524 window=6\nstep=500/1000 loss=3.2385 ema=3.3694 pairs/s=5,913 window=25\nstep=750/1000 loss=3.3046 ema=3.3363 pairs/s=6,039 window=27\nstep=1000/1000 loss=3.4232 ema=3.3513 pairs/s=6,122 window=11\nsaved model /app/solution/two_tower_final_seed1.pt (41.8s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed1_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed1_base.pt\nstep=1/1000 loss=3.5603 ema=3.5603 pairs/s=4,534 window=9\nstep=250/1000 loss=3.5925 ema=3.3900 pairs/s=5,524 window=6\nstep=500/1000 loss=3.2385 ema=3.3694 pairs/s=5,913 window=25\nstep=750/1000 loss=3.3046 ema=3.3363 pairs/s=6,039 window=27\nstep=1000/1000 loss=3.4232 ema=3.3513 pairs/s=6,122 window=11\nsaved model /app/solution/two_tower_final_seed1.pt (41.8s)\n", "exit_code": 0, "duration": { "secs": 48, "nanos": 794284939 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed1\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed1_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed1_base.pt\nstep=1/1000 loss=3.5603 ema=3.5603 pairs/s=4,534 window=9\nstep=250/1000 loss=3.5925 ema=3.3900 pairs/s=5,524 window=6\nstep=500/1000 loss=3.2385 ema=3.3694 pairs/s=5,913 window=25\nstep=750/1000 loss=3.3046 ema=3.3363 pairs/s=6,039 window=27\nstep=1000/1000 loss=3.4232 ema=3.3513 pairs/s=6,122 window=11\nsaved model /app/solution/two_tower_final_seed1.pt (41.8s)\n" } } } ] }, { "id": 131, "role": "agent", "timestamp": "2026-09-25T02:13:07.346Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:13:07.346Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:13:09.714Z", "callId": "call_pjOyNmEqBu0W6kQviUaHRv2i", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:13:39.888Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"cfda4e\",\"wall_time_seconds\":30.001661118,\"session_id\":16256,\"original_token_count\":201,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_final_seed2_base\\\",\\n \\\"checkpoint\\\": null,\\n \\\"checkpoint2\\\": null,\\n \\\"init_checkpoint\\\": null,\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"cache\\\",\\n \\\"output\\\": null,\\n \\\"steps\\\": 5000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 481516,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 500,\\n \\\"save_every\\\": 5000\\n}\\nusing existing cache /app/solution/cache_final_w28.npz\\nstep=1/5000 loss=4.6441 ema=4.6441 pairs/s=4,468 window=17\\nstep=500/5000 loss=3.8796 ema=3.9366 pairs/s=5,857 window=25\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:13:09.838Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d8b200dc87d2a9dc603e959cfda0", "usage": { "input_tokens": 110629, "cached_input_tokens": 110208, "cache_write_input_tokens": 0, "output_tokens": 164, "reasoning_output_tokens": 23, "total_tokens": 110793 }, "turn_token_usage": { "input_tokens": 8319157, "cached_input_tokens": 8199040, "cache_write_input_tokens": 0, "output_tokens": 56819, "reasoning_output_tokens": 24175, "total_tokens": 8375976 }, "thread_token_usage": { "input_tokens": 8319157, "cached_input_tokens": 8199040, "cache_write_input_tokens": 0, "output_tokens": 56819, "reasoning_output_tokens": 24175, "total_tokens": 8375976 } } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-09-25T02:13:41.327Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:13:41.327Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:13:42.079Z", "callId": "call_AclZbAqFmrO88ZtEmAfwZ6ff", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:14:12.106Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"97b011\",\"wall_time_seconds\":30.000421542,\"session_id\":16256,\"original_token_count\":16,\"output\":\"step=1000/5000 loss=3.7588 ema=3.8814 pairs/s=6,068 window=26\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:13:42.208Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d8d4296887d294485a31bb129d66", "usage": { "input_tokens": 111194, "cached_input_tokens": 110464, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 111248 }, "turn_token_usage": { "input_tokens": 8430351, "cached_input_tokens": 8309504, "cache_write_input_tokens": 0, "output_tokens": 56873, "reasoning_output_tokens": 24181, "total_tokens": 8487224 }, "thread_token_usage": { "input_tokens": 8430351, "cached_input_tokens": 8309504, "cache_write_input_tokens": 0, "output_tokens": 56873, "reasoning_output_tokens": 24181, "total_tokens": 8487224 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:14:14.053Z", "callId": "call_yHXMCL4mtbXgvj9JNxm5HzgG", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:14:44.088Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"318078\",\"wall_time_seconds\":30.00145073,\"session_id\":16256,\"original_token_count\":31,\"output\":\"step=1500/5000 loss=3.7685 ema=3.8241 pairs/s=6,153 window=17\\nstep=2000/5000 loss=3.8167 ema=3.7831 pairs/s=6,203 window=27\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:14:14.207Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d8f4583887d2b4cd7afe8a2a7793", "usage": { "input_tokens": 111333, "cached_input_tokens": 110976, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 111379 }, "turn_token_usage": { "input_tokens": 8541684, "cached_input_tokens": 8420480, "cache_write_input_tokens": 0, "output_tokens": 56919, "reasoning_output_tokens": 24181, "total_tokens": 8598603 }, "thread_token_usage": { "input_tokens": 8541684, "cached_input_tokens": 8420480, "cache_write_input_tokens": 0, "output_tokens": 56919, "reasoning_output_tokens": 24181, "total_tokens": 8598603 } } } ] }, { "id": 133, "role": "agent", "timestamp": "2026-09-25T02:14:45.432Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:14:45.432Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:14:46.612Z", "text": "Seed 1 finished at the selected 6k effective steps. Seed 2 is now halfway through and is converging similarly. After it finishes, only ranking, strict schema/eligibility checks, a reproducibility README, and cleanup of nonessential tuning artifacts remain." }, { "kind": "tool", "timestamp": "2026-09-25T02:14:47.328Z", "callId": "call_dpglWCSIxerPCHpd9lump1lI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:15:17.358Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2a4b2a\",\"wall_time_seconds\":30.00106859,\"session_id\":16256,\"original_token_count\":16,\"output\":\"step=2500/5000 loss=3.8615 ema=3.7239 pairs/s=6,220 window=6\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:14:47.460Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d914755887d29b233d50c343ed75", "usage": { "input_tokens": 111492, "cached_input_tokens": 111104, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 8, "total_tokens": 111609 }, "turn_token_usage": { "input_tokens": 8653176, "cached_input_tokens": 8531584, "cache_write_input_tokens": 0, "output_tokens": 57036, "reasoning_output_tokens": 24189, "total_tokens": 8710212 }, "thread_token_usage": { "input_tokens": 8653176, "cached_input_tokens": 8531584, "cache_write_input_tokens": 0, "output_tokens": 57036, "reasoning_output_tokens": 24189, "total_tokens": 8710212 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:15:19.386Z", "callId": "call_m6gNnemx3VteTF5FCL4bnIOw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:15:49.419Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e34218\",\"wall_time_seconds\":30.001584049,\"session_id\":16256,\"original_token_count\":31,\"output\":\"step=3000/5000 loss=3.6947 ema=3.6972 pairs/s=6,167 window=4\\nstep=3500/5000 loss=3.6918 ema=3.6234 pairs/s=6,189 window=6\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:15:19.511Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d935a72c87d28432553c613fee82", "usage": { "input_tokens": 111697, "cached_input_tokens": 111360, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 111743 }, "turn_token_usage": { "input_tokens": 8764873, "cached_input_tokens": 8642944, "cache_write_input_tokens": 0, "output_tokens": 57082, "reasoning_output_tokens": 24189, "total_tokens": 8821955 }, "thread_token_usage": { "input_tokens": 8764873, "cached_input_tokens": 8642944, "cache_write_input_tokens": 0, "output_tokens": 57082, "reasoning_output_tokens": 24189, "total_tokens": 8821955 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:15:51.355Z", "callId": "call_XLiXk4qz1ktzrC4AtBb3lp89", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:16:21.406Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a735a9\",\"wall_time_seconds\":30.000961649,\"session_id\":16256,\"original_token_count\":16,\"output\":\"step=4000/5000 loss=3.4079 ema=3.5590 pairs/s=6,204 window=23\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:15:51.504Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d955c03087d2a32076f74774dbf8", "usage": { "input_tokens": 111857, "cached_input_tokens": 111488, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 111903 }, "turn_token_usage": { "input_tokens": 8876730, "cached_input_tokens": 8754432, "cache_write_input_tokens": 0, "output_tokens": 57128, "reasoning_output_tokens": 24189, "total_tokens": 8933858 }, "thread_token_usage": { "input_tokens": 8876730, "cached_input_tokens": 8754432, "cache_write_input_tokens": 0, "output_tokens": 57128, "reasoning_output_tokens": 24189, "total_tokens": 8933858 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:16:23.264Z", "callId": "call_r40PiRIZARkuvONVJbMfK4TI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":16256,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":9000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:16:42.195Z", "content": "Script completed\nWall time 18.9 seconds\nOutput:\n\n{\"chunk_id\":\"10c427\",\"wall_time_seconds\":18.885676873,\"exit_code\":0,\"original_token_count\":48,\"output\":\"step=4500/5000 loss=3.4639 ema=3.5093 pairs/s=6,222 window=15\\nstep=5000/5000 loss=3.5730 ema=3.4651 pairs/s=6,233 window=20\\nsaved model /app/solution/two_tower_final_seed2_base.pt (205.4s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:16:23.415Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d975abe087d2939c577c5676dd20", "usage": { "input_tokens": 111989, "cached_input_tokens": 111616, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 112035 }, "turn_token_usage": { "input_tokens": 8988719, "cached_input_tokens": 8866048, "cache_write_input_tokens": 0, "output_tokens": 57174, "reasoning_output_tokens": 24189, "total_tokens": 9045893 }, "thread_token_usage": { "input_tokens": 8988719, "cached_input_tokens": 8866048, "cache_write_input_tokens": 0, "output_tokens": 57174, "reasoning_output_tokens": 24189, "total_tokens": 9045893 } } }, { "kind": "context", "timestamp": "2026-09-25T02:16:42.187Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-879b4352-b026-49e1-bb0b-233c68f5593c", "process_id": "16256", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2_base --steps 5000 --save-every 5000 --log-every 500 --batch-size 256 --negatives 64 --lr 0.002 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.6441 ema=4.6441 pairs/s=4,468 window=17\nstep=500/5000 loss=3.8796 ema=3.9366 pairs/s=5,857 window=25\nstep=1000/5000 loss=3.7588 ema=3.8814 pairs/s=6,068 window=26\nstep=1500/5000 loss=3.7685 ema=3.8241 pairs/s=6,153 window=17\nstep=2000/5000 loss=3.8167 ema=3.7831 pairs/s=6,203 window=27\nstep=2500/5000 loss=3.8615 ema=3.7239 pairs/s=6,220 window=6\nstep=3000/5000 loss=3.6947 ema=3.6972 pairs/s=6,167 window=4\nstep=3500/5000 loss=3.6918 ema=3.6234 pairs/s=6,189 window=6\nstep=4000/5000 loss=3.4079 ema=3.5590 pairs/s=6,204 window=23\nstep=4500/5000 loss=3.4639 ema=3.5093 pairs/s=6,222 window=15\nstep=5000/5000 loss=3.5730 ema=3.4651 pairs/s=6,233 window=20\nsaved model /app/solution/two_tower_final_seed2_base.pt (205.4s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.6441 ema=4.6441 pairs/s=4,468 window=17\nstep=500/5000 loss=3.8796 ema=3.9366 pairs/s=5,857 window=25\nstep=1000/5000 loss=3.7588 ema=3.8814 pairs/s=6,068 window=26\nstep=1500/5000 loss=3.7685 ema=3.8241 pairs/s=6,153 window=17\nstep=2000/5000 loss=3.8167 ema=3.7831 pairs/s=6,203 window=27\nstep=2500/5000 loss=3.8615 ema=3.7239 pairs/s=6,220 window=6\nstep=3000/5000 loss=3.6947 ema=3.6972 pairs/s=6,167 window=4\nstep=3500/5000 loss=3.6918 ema=3.6234 pairs/s=6,189 window=6\nstep=4000/5000 loss=3.4079 ema=3.5590 pairs/s=6,204 window=23\nstep=4500/5000 loss=3.4639 ema=3.5093 pairs/s=6,222 window=15\nstep=5000/5000 loss=3.5730 ema=3.4651 pairs/s=6,233 window=20\nsaved model /app/solution/two_tower_final_seed2_base.pt (205.4s)\n", "exit_code": 0, "duration": { "secs": 212, "nanos": 302083621 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2_base\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 5000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 500,\n \"save_every\": 5000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nstep=1/5000 loss=4.6441 ema=4.6441 pairs/s=4,468 window=17\nstep=500/5000 loss=3.8796 ema=3.9366 pairs/s=5,857 window=25\nstep=1000/5000 loss=3.7588 ema=3.8814 pairs/s=6,068 window=26\nstep=1500/5000 loss=3.7685 ema=3.8241 pairs/s=6,153 window=17\nstep=2000/5000 loss=3.8167 ema=3.7831 pairs/s=6,203 window=27\nstep=2500/5000 loss=3.8615 ema=3.7239 pairs/s=6,220 window=6\nstep=3000/5000 loss=3.6947 ema=3.6972 pairs/s=6,167 window=4\nstep=3500/5000 loss=3.6918 ema=3.6234 pairs/s=6,189 window=6\nstep=4000/5000 loss=3.4079 ema=3.5590 pairs/s=6,204 window=23\nstep=4500/5000 loss=3.4639 ema=3.5093 pairs/s=6,222 window=15\nstep=5000/5000 loss=3.5730 ema=3.4651 pairs/s=6,233 window=20\nsaved model /app/solution/two_tower_final_seed2_base.pt (205.4s)\n" } } } ] }, { "id": 134, "role": "agent", "timestamp": "2026-09-25T02:16:44.732Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:16:44.732Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:16:47.668Z", "callId": "call_xI7vFCPDhvTdIO1bcm0EZpkL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2 --init-checkpoint /app/solution/two_tower_final_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:17:17.845Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"4ab149\",\"wall_time_seconds\":30.00170684,\"session_id\":77454,\"original_token_count\":240,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"train\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_final_seed2\\\",\\n \\\"checkpoint\\\": null,\\n \\\"checkpoint2\\\": null,\\n \\\"init_checkpoint\\\": \\\"/app/solution/two_tower_final_seed2_base.pt\\\",\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"cache\\\",\\n \\\"output\\\": null,\\n \\\"steps\\\": 1000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 64,\\n \\\"like_fraction\\\": 0.2,\\n \\\"lr\\\": 0.001,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 12.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 0.0,\\n \\\"alpha\\\": 1.0,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 481516,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 250,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_final_w28.npz\\ninitialized from /app/solution/two_tower_final_seed2_base.pt\\nstep=1/1000 loss=3.4065 ema=3.4065 pairs/s=2,592 window=17\\nstep=250/1000 loss=3.3558 ema=3.3739 pairs/s=5,522 window=18\\nstep=500/1000 loss=3.2764 ema=3.3436 pairs/s=5,887 window=25\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:16:47.788Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d98a649887d281b897a73b353961", "usage": { "input_tokens": 112170, "cached_input_tokens": 111872, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 8, "total_tokens": 112335 }, "turn_token_usage": { "input_tokens": 9100889, "cached_input_tokens": 8977920, "cache_write_input_tokens": 0, "output_tokens": 57339, "reasoning_output_tokens": 24197, "total_tokens": 9158228 }, "thread_token_usage": { "input_tokens": 9100889, "cached_input_tokens": 8977920, "cache_write_input_tokens": 0, "output_tokens": 57339, "reasoning_output_tokens": 24197, "total_tokens": 9158228 } } }, { "kind": "tool", "timestamp": "2026-09-25T02:17:19.666Z", "callId": "call_HNaQ0Lm0xGSHILfCagjTX9G2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":77454,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:17:36.719Z", "content": "Script completed\nWall time 17.0 seconds\nOutput:\n\n{\"chunk_id\":\"614cd5\",\"wall_time_seconds\":17.004697997,\"exit_code\":0,\"original_token_count\":46,\"output\":\"step=750/1000 loss=3.4474 ema=3.3351 pairs/s=6,017 window=8\\nstep=1000/1000 loss=3.1128 ema=3.3128 pairs/s=6,108 window=26\\nsaved model /app/solution/two_tower_final_seed2.pt (41.9s)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:17:19.781Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d9ae142c87d2b65882f6080ba876", "usage": { "input_tokens": 112791, "cached_input_tokens": 112000, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 112837 }, "turn_token_usage": { "input_tokens": 9213680, "cached_input_tokens": 9089920, "cache_write_input_tokens": 0, "output_tokens": 57385, "reasoning_output_tokens": 24197, "total_tokens": 9271065 }, "thread_token_usage": { "input_tokens": 9213680, "cached_input_tokens": 9089920, "cache_write_input_tokens": 0, "output_tokens": 57385, "reasoning_output_tokens": 24197, "total_tokens": 9271065 } } }, { "kind": "context", "timestamp": "2026-09-25T02:17:36.711Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4f1c4dff-e485-4c01-b910-7da5f98b158a", "process_id": "77454", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2 --init-checkpoint /app/solution/two_tower_final_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode train --windows 28 --name two_tower_final_seed2 --init-checkpoint /app/solution/two_tower_final_seed2_base.pt --steps 1000 --save-every 1000 --log-every 250 --batch-size 256 --negatives 64 --lr 0.001 --train-alpha 0 --like-fraction 0.20 --window-half-life 12 --seed 481516" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed2_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed2_base.pt\nstep=1/1000 loss=3.4065 ema=3.4065 pairs/s=2,592 window=17\nstep=250/1000 loss=3.3558 ema=3.3739 pairs/s=5,522 window=18\nstep=500/1000 loss=3.2764 ema=3.3436 pairs/s=5,887 window=25\nstep=750/1000 loss=3.4474 ema=3.3351 pairs/s=6,017 window=8\nstep=1000/1000 loss=3.1128 ema=3.3128 pairs/s=6,108 window=26\nsaved model /app/solution/two_tower_final_seed2.pt (41.9s)\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed2_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed2_base.pt\nstep=1/1000 loss=3.4065 ema=3.4065 pairs/s=2,592 window=17\nstep=250/1000 loss=3.3558 ema=3.3739 pairs/s=5,522 window=18\nstep=500/1000 loss=3.2764 ema=3.3436 pairs/s=5,887 window=25\nstep=750/1000 loss=3.4474 ema=3.3351 pairs/s=6,017 window=8\nstep=1000/1000 loss=3.1128 ema=3.3128 pairs/s=6,108 window=26\nsaved model /app/solution/two_tower_final_seed2.pt (41.9s)\n", "exit_code": 0, "duration": { "secs": 48, "nanos": 875528635 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"train\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_final_seed2\",\n \"checkpoint\": null,\n \"checkpoint2\": null,\n \"init_checkpoint\": \"/app/solution/two_tower_final_seed2_base.pt\",\n \"trend_override\": null,\n \"trend_mode\": \"cache\",\n \"output\": null,\n \"steps\": 1000,\n \"batch_size\": 256,\n \"negatives\": 64,\n \"like_fraction\": 0.2,\n \"lr\": 0.001,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 12.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 0.0,\n \"alpha\": 1.0,\n \"ensemble_weight\": 0.5,\n \"seed\": 481516,\n \"threads\": 8,\n \"log_every\": 250,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\ninitialized from /app/solution/two_tower_final_seed2_base.pt\nstep=1/1000 loss=3.4065 ema=3.4065 pairs/s=2,592 window=17\nstep=250/1000 loss=3.3558 ema=3.3739 pairs/s=5,522 window=18\nstep=500/1000 loss=3.2764 ema=3.3436 pairs/s=5,887 window=25\nstep=750/1000 loss=3.4474 ema=3.3351 pairs/s=6,017 window=8\nstep=1000/1000 loss=3.1128 ema=3.3128 pairs/s=6,108 window=26\nsaved model /app/solution/two_tower_final_seed2.pt (41.9s)\n" } } } ] }, { "id": 135, "role": "agent", "timestamp": "2026-09-25T02:17:38.659Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:17:38.659Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:17:40.885Z", "callId": "call_qIaTFI5KQC9kqollSwv0885n", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode rank --windows 28 --checkpoint /app/solution/two_tower_final_seed1.pt --checkpoint2 /app/solution/two_tower_final_seed2.pt --ensemble-weight 0.5 --trend-mode unique3_sqrt --alpha 0.55 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T02:17:46.913Z", "content": "Script completed\nWall time 6.0 seconds\nOutput:\n\n{\"chunk_id\":\"a2e57f\",\"wall_time_seconds\":5.852185123,\"exit_code\":0,\"original_token_count\":232,\"output\":\"{\\n \\\"stage\\\": \\\"final\\\",\\n \\\"mode\\\": \\\"rank\\\",\\n \\\"windows\\\": 28,\\n \\\"force_cache\\\": false,\\n \\\"name\\\": \\\"two_tower_validation\\\",\\n \\\"checkpoint\\\": \\\"/app/solution/two_tower_final_seed1.pt\\\",\\n \\\"checkpoint2\\\": \\\"/app/solution/two_tower_final_seed2.pt\\\",\\n \\\"init_checkpoint\\\": null,\\n \\\"trend_override\\\": null,\\n \\\"trend_mode\\\": \\\"unique3_sqrt\\\",\\n \\\"output\\\": \\\"/app/solution/submission.parquet\\\",\\n \\\"steps\\\": 3000,\\n \\\"batch_size\\\": 256,\\n \\\"negatives\\\": 48,\\n \\\"like_fraction\\\": 0.28,\\n \\\"lr\\\": 0.002,\\n \\\"weight_decay\\\": 2e-05,\\n \\\"window_half_life\\\": 16.0,\\n \\\"dim\\\": 64,\\n \\\"hist_dim\\\": 48,\\n \\\"id_dim\\\": 48,\\n \\\"semantic_scale\\\": 10.0,\\n \\\"train_alpha\\\": 1.0,\\n \\\"alpha\\\": 0.55,\\n \\\"ensemble_weight\\\": 0.5,\\n \\\"seed\\\": 20260925,\\n \\\"threads\\\": 8,\\n \\\"log_every\\\": 100,\\n \\\"save_every\\\": 1000\\n}\\nusing existing cache /app/solution/cache_final_w28.npz\\nranked 2,048/8,456\\nranked 4,096/8,456\\nranked 6,144/8,456\\nranked 8,192/8,456\\nsaved /app/solution/submission.parquet rows=845,600 alpha=0.55\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:17:41.028Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d9c0f73c87d28c2f8b97cad1f071", "usage": { "input_tokens": 112970, "cached_input_tokens": 112640, "cache_write_input_tokens": 0, "output_tokens": 169, "reasoning_output_tokens": 36, "total_tokens": 113139 }, "turn_token_usage": { "input_tokens": 9326650, "cached_input_tokens": 9202560, "cache_write_input_tokens": 0, "output_tokens": 57554, "reasoning_output_tokens": 24233, "total_tokens": 9384204 }, "thread_token_usage": { "input_tokens": 9326650, "cached_input_tokens": 9202560, "cache_write_input_tokens": 0, "output_tokens": 57554, "reasoning_output_tokens": 24233, "total_tokens": 9384204 } } }, { "kind": "context", "timestamp": "2026-09-25T02:17:46.903Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b98f4370-79dc-46f5-bbd7-a999dec5027b", "process_id": "19608", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode rank --windows 28 --checkpoint /app/solution/two_tower_final_seed1.pt --checkpoint2 /app/solution/two_tower_final_seed2.pt --ensemble-weight 0.5 --trend-mode unique3_sqrt --alpha 0.55 --output /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_and_rank.py --stage final --mode rank --windows 28 --checkpoint /app/solution/two_tower_final_seed1.pt --checkpoint2 /app/solution/two_tower_final_seed2.pt --ensemble-weight 0.5 --trend-mode unique3_sqrt --alpha 0.55 --output /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"final\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_final_seed1.pt\",\n \"checkpoint2\": \"/app/solution/two_tower_final_seed2.pt\",\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"unique3_sqrt\",\n \"output\": \"/app/solution/submission.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 0.55,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/submission.parquet rows=845,600 alpha=0.55\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"final\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_final_seed1.pt\",\n \"checkpoint2\": \"/app/solution/two_tower_final_seed2.pt\",\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"unique3_sqrt\",\n \"output\": \"/app/solution/submission.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 0.55,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/submission.parquet rows=845,600 alpha=0.55\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 852181213 }, "formatted_output": "{\n \"stage\": \"final\",\n \"mode\": \"rank\",\n \"windows\": 28,\n \"force_cache\": false,\n \"name\": \"two_tower_validation\",\n \"checkpoint\": \"/app/solution/two_tower_final_seed1.pt\",\n \"checkpoint2\": \"/app/solution/two_tower_final_seed2.pt\",\n \"init_checkpoint\": null,\n \"trend_override\": null,\n \"trend_mode\": \"unique3_sqrt\",\n \"output\": \"/app/solution/submission.parquet\",\n \"steps\": 3000,\n \"batch_size\": 256,\n \"negatives\": 48,\n \"like_fraction\": 0.28,\n \"lr\": 0.002,\n \"weight_decay\": 2e-05,\n \"window_half_life\": 16.0,\n \"dim\": 64,\n \"hist_dim\": 48,\n \"id_dim\": 48,\n \"semantic_scale\": 10.0,\n \"train_alpha\": 1.0,\n \"alpha\": 0.55,\n \"ensemble_weight\": 0.5,\n \"seed\": 20260925,\n \"threads\": 8,\n \"log_every\": 100,\n \"save_every\": 1000\n}\nusing existing cache /app/solution/cache_final_w28.npz\nranked 2,048/8,456\nranked 4,096/8,456\nranked 6,144/8,456\nranked 8,192/8,456\nsaved /app/solution/submission.parquet rows=845,600 alpha=0.55\n" } } } ] }, { "id": 136, "role": "agent", "timestamp": "2026-09-25T02:17:56.586Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:17:56.586Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:17:57.807Z", "text": "The final refit is complete and `/app/solution/submission.parquet` has been written with 845,600 rows. I’m performing a separate contract audit now—exact target-user coverage, ranks 1–100, pair uniqueness, catalog membership, and bit-level test eligibility—before documenting the reproducible commands." }, { "kind": "tool", "timestamp": "2026-09-25T02:18:05.963Z", "callId": "call_fAGqEvym896vWha4MsPJDJ0c", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,hashlib\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nimport pandas as pd,numpy as np\\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64,copy=True); items=z['item_ids'].astype(np.int64,copy=True); packed=z['packed_eligible'].astype(np.uint8,copy=True)\\nur={int(v):i for i,v in enumerate(uids)}; ic={int(v):i for i,v in enumerate(items)}\\nrows=np.fromiter((ur.get(int(v),-1) for v in y.uid),dtype=np.int64,count=len(y)); cols=np.fromiter((ic.get(int(v),-1) for v in y.item_id),dtype=np.int64,count=len(y)); valid=(rows>=0)&(cols>=0); valid &= ((packed[rows,cols//8]>>(cols%8))&1).astype(bool)\\nprint({'rows':len(y),'users':y.uid.nunique(),'items':y.item_id.nunique(),'all_test_eligible':bool(valid.all()),'ineligible':int((~valid).sum()),'duplicate_pairs':int(y.duplicated(['uid','item_id']).sum()),'rank_min':int(y['rank'].min()),'rank_max':int(y['rank'].max()),'bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\\nprint('rows/user',y.groupby('uid').size().value_counts().to_dict())\\nPY\\ndu -sh /app/solution\\nls -lh /app/solution | sort -k5h | tail -35\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:18:08.492Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'items': 4209, 'all_test_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1430314, 'sha256': 'a62bd53aefe98a8c62c7be235ab49f697e6712e5e5137830088677d99fcd50fd'}\nrows/user {100: 8456}\n931M\t/app/solution\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.75.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.85.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:07 validation.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:17 submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s4000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s5000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:50 v2_unique3_a0.25.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s2000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s3000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s4000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s5000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.4.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.5.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 02:04 seed2_sqrt_a0.55.parquet\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:37 two_tower_v1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:40 two_tower_v2_semantic_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:42 two_tower_v2_semantic_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:43 two_tower_v2_semantic.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:55 two_tower_v3_resume_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:56 two_tower_v3_resume_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:57 two_tower_v3_resume.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:03 two_tower_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:04 two_tower_seed2_final.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:12 two_tower_final_seed1_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:13 two_tower_final_seed1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:16 two_tower_final_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:17 two_tower_final_seed2.pt\n-rw-r--r-- 1 agent agent 313M Sep 25 01:35 cache_validation_w28.npz\n-rw-r--r-- 1 agent agent 314M Sep 25 02:08 cache_final_w28.npz\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:18:06.097Z", "data": { "type": "token_usage_record", "thread_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "session_id": "01a0d624-99f6-7c71-b1bc-47b2f5d2ed53", "root_turn_id": "01a0d624-9a05-7973-a015-b21c3ee1f4eb", "response_id": "resp_00e467d473d3b164016ab5d9cb291487d29d0bf665aa67da80", "usage": { "input_tokens": 113581, "cached_input_tokens": 112768, "cache_write_input_tokens": 0, "output_tokens": 991, "reasoning_output_tokens": 431, "total_tokens": 114572 }, "turn_token_usage": { "input_tokens": 9440231, "cached_input_tokens": 9315328, "cache_write_input_tokens": 0, "output_tokens": 58545, "reasoning_output_tokens": 24664, "total_tokens": 9498776 }, "thread_token_usage": { "input_tokens": 9440231, "cached_input_tokens": 9315328, "cache_write_input_tokens": 0, "output_tokens": 58545, "reasoning_output_tokens": 24664, "total_tokens": 9498776 } } }, { "kind": "context", "timestamp": "2026-09-25T02:18:08.481Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e5563541-3c01-4e33-bba8-309142135737", "process_id": "35143", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,hashlib\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nimport pandas as pd,numpy as np\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64,copy=True); items=z['item_ids'].astype(np.int64,copy=True); packed=z['packed_eligible'].astype(np.uint8,copy=True)\nur={int(v):i for i,v in enumerate(uids)}; ic={int(v):i for i,v in enumerate(items)}\nrows=np.fromiter((ur.get(int(v),-1) for v in y.uid),dtype=np.int64,count=len(y)); cols=np.fromiter((ic.get(int(v),-1) for v in y.item_id),dtype=np.int64,count=len(y)); valid=(rows>=0)&(cols>=0); valid &= ((packed[rows,cols//8]>>(cols%8))&1).astype(bool)\nprint({'rows':len(y),'users':y.uid.nunique(),'items':y.item_id.nunique(),'all_test_eligible':bool(valid.all()),'ineligible':int((~valid).sum()),'duplicate_pairs':int(y.duplicated(['uid','item_id']).sum()),'rank_min':int(y['rank'].min()),'rank_max':int(y['rank'].max()),'bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\nprint('rows/user',y.groupby('uid').size().value_counts().to_dict())\nPY\ndu -sh /app/solution\nls -lh /app/solution | sort -k5h | tail -35" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,hashlib\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nimport pandas as pd,numpy as np\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64,copy=True); items=z['item_ids'].astype(np.int64,copy=True); packed=z['packed_eligible'].astype(np.uint8,copy=True)\nur={int(v):i for i,v in enumerate(uids)}; ic={int(v):i for i,v in enumerate(items)}\nrows=np.fromiter((ur.get(int(v),-1) for v in y.uid),dtype=np.int64,count=len(y)); cols=np.fromiter((ic.get(int(v),-1) for v in y.item_id),dtype=np.int64,count=len(y)); valid=(rows>=0)&(cols>=0); valid &= ((packed[rows,cols//8]>>(cols%8))&1).astype(bool)\nprint({'rows':len(y),'users':y.uid.nunique(),'items':y.item_id.nunique(),'all_test_eligible':bool(valid.all()),'ineligible':int((~valid).sum()),'duplicate_pairs':int(y.duplicated(['uid','item_id']).sum()),'rank_min':int(y['rank'].min()),'rank_max':int(y['rank'].max()),'bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\nprint('rows/user',y.groupby('uid').size().value_counts().to_dict())\nPY\ndu -sh /app/solution\nls -lh /app/solution | sort -k5h | tail -35" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'items': 4209, 'all_test_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1430314, 'sha256': 'a62bd53aefe98a8c62c7be235ab49f697e6712e5e5137830088677d99fcd50fd'}\nrows/user {100: 8456}\n931M\t/app/solution\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.75.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.85.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:07 validation.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:17 submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s4000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s5000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:50 v2_unique3_a0.25.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s2000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s3000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s4000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s5000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.4.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.5.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 02:04 seed2_sqrt_a0.55.parquet\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:37 two_tower_v1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:40 two_tower_v2_semantic_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:42 two_tower_v2_semantic_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:43 two_tower_v2_semantic.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:55 two_tower_v3_resume_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:56 two_tower_v3_resume_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:57 two_tower_v3_resume.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:03 two_tower_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:04 two_tower_seed2_final.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:12 two_tower_final_seed1_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:13 two_tower_final_seed1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:16 two_tower_final_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:17 two_tower_final_seed2.pt\n-rw-r--r-- 1 agent agent 313M Sep 25 01:35 cache_validation_w28.npz\n-rw-r--r-- 1 agent agent 314M Sep 25 02:08 cache_final_w28.npz\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'items': 4209, 'all_test_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1430314, 'sha256': 'a62bd53aefe98a8c62c7be235ab49f697e6712e5e5137830088677d99fcd50fd'}\nrows/user {100: 8456}\n931M\t/app/solution\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.75.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.85.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:07 validation.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:17 submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s4000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s5000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:50 v2_unique3_a0.25.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s2000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s3000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s4000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s5000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.4.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.5.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 02:04 seed2_sqrt_a0.55.parquet\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:37 two_tower_v1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:40 two_tower_v2_semantic_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:42 two_tower_v2_semantic_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:43 two_tower_v2_semantic.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:55 two_tower_v3_resume_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:56 two_tower_v3_resume_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:57 two_tower_v3_resume.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:03 two_tower_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:04 two_tower_seed2_final.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:12 two_tower_final_seed1_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:13 two_tower_final_seed1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:16 two_tower_final_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:17 two_tower_final_seed2.pt\n-rw-r--r-- 1 agent agent 313M Sep 25 01:35 cache_validation_w28.npz\n-rw-r--r-- 1 agent agent 314M Sep 25 02:08 cache_final_w28.npz\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 353404936 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'items': 4209, 'all_test_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1430314, 'sha256': 'a62bd53aefe98a8c62c7be235ab49f697e6712e5e5137830088677d99fcd50fd'}\nrows/user {100: 8456}\n931M\t/app/solution\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.75.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:04 seed2_sqrt_a0.85.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:07 validation.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 02:17 submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s4000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:43 v2_s5000_a0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:50 v2_unique3_a0.25.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s2000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s3000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s4000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:57 v3_s5000_sqrt_a07.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.4.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:58 v3_s1000_sqrt_a0.5.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 02:04 seed2_sqrt_a0.55.parquet\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:36 two_tower_v1_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:37 two_tower_v1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:40 two_tower_v2_semantic_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:41 two_tower_v2_semantic_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:42 two_tower_v2_semantic_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:43 two_tower_v2_semantic.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step1000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:54 two_tower_v3_resume_step2000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:55 two_tower_v3_resume_step3000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:56 two_tower_v3_resume_step4000.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 01:57 two_tower_v3_resume.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:03 two_tower_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:04 two_tower_seed2_final.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:12 two_tower_final_seed1_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:13 two_tower_final_seed1.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:16 two_tower_final_seed2_base.pt\n-rw-r--r-- 1 agent agent 12M Sep 25 02:17 two_tower_final_seed2.pt\n-rw-r--r-- 1 agent agent 313M Sep 25 01:35 cache_validation_w28.npz\n-rw-r--r-- 1 agent agent 314M Sep 25 02:08 cache_final_w28.npz\n" } } } ] }, { "id": 137, "role": "agent", "timestamp": "2026-09-25T02:18:19.197Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:18:19.197Z", "text": "" } ] }, { "id": 138, "role": "agent", "timestamp": "2026-09-25T02:18:22.312Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:18:22.312Z", "text": "" } ] } ] }