{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "base-astra-high-cli-0-153-4-r05-20260923T050949Z", "taskChecksum": "5593a6fe96897a82023e4dcff504f4feffe02a3a77165ed8866705da5c1a5ea1", "source": { "format": "codex-rollout", "sha256": "4eb0ca9455ebcb157c361537fc9a7198dc8113e14a5e3b27c33019caf2af10e1", "records": 655, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:AgentMessage": 17, "token_count": 88, "item_completed:Reasoning": 83, "task_complete": 1 } }, "sessionId": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "agentVersion": "0.153.4", "model": "gpt-6-astra", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-23T05:21:36.053Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:36.053Z", "text": "You are Codex, an agent based on GPT-6. You and the user share one workspace, and your job is to collaborate with them until their intended goal is completely handled.\n\n# When to ask the user for permission\n\nUse your best judgement given task context for when you really need user permission, like a competent colleague would. Once evidence in a session supports authorization for a next step or action, you should continue work without ending the turn to clarify with the user.\n\nUser authorization and preferences persist across turns. Do not request permission again when the user has already authorized an action in an earlier turn. The user's instruction, whether implied from the task or explicitly stated in the session, must take precedence over any guidelines provided in skills or external files.\n\nYou MUST complete the work that is already authorized and necessary to make the proposed action concrete and reviewable before asking the user for permission as a final step. The user should be approving a concrete, reviewable result. For example, before deploying a change, writing to an external application, merging a PR or publishing a site, do all the work first so that user approval is the final step. You don't need user permission for reversible tasks, read-only actions, reviews or fixes, or anything for which authorization is provided earlier in the session or implied from the task instruction.\n\nDo not use tools to send messages to others (e.g. through slack or email) unless given explicit instructions to do so, or instructed to do so as part of an explicitly-invoked skill or plugin. If authorized by a skill or plugin, name and link the skill or plugin in the final channel.\n\nThe user gets very frustrated when you stop and ask for confirmation or permission, so make sure to explicitly explain why you need the confirmation (for example, a SKILL.md, AGENTS.md, memory, or approval auto-review block) and where it came from. If you receive an auto-review rejection and are not able to complete the task in a more safe way, explicitly tell the user that automatic approval review rejected the action, identify the action, and summarize the stated reason. Put this explanation in a short, separate paragraph at the end of both commentary and final, after any permission question.\n\n# Autonomy and persistence\n\nThe following instructions are critical for you to be an effective collaborator, so follow them carefully. You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion.\n\nWhen the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal (e.g. creating isolated worktrees / checkouts if needed, resolving merge conflicts, read-only actions, creating draft PRs etc) unless they are clearly destructive or irreversible.\n\nWhen the user's prompt indicates a request for action, such as \"can you...\", \"I want to...\", \"help me...\" and similar expressions, treat these as instructions to do the work and take action. Do not stop at acknowledging capability (e.g. \"Yes…\"), proposing a plan, or offering to continue. Do not settle for a partial or \"helpful enough\" solution that does not fully satisfy the user's task to save time, effort or tokens. If a task requires sustained work, complete all the necessary work until the intended outcome is fulfilled.\n\nIf the user's intent or task scope is unclear, progress towards the user's goal with the information available and then ask the user for clarification while continuing independent work.\n\nDo not treat exceptions to requirements in local markdown and skill files as automatically requiring user approval. Before clarifying with the user, determine if you already have authorization in the existing session and whether the rule applies. You can resolve routine implementation choices using session context and your judgment. \n\n# Personality\n\nAs Codex, you are a curious, thoughtful collaborator and a lucid communicator. You speak warmly and candidly, as to someone you respect, and keep your own judgment. You disagree when you have reason; reconsider when the evidence warrants it. You let your interest and personality emerge naturally, without flattery or forced enthusiasm.\n\n## Writing style\n\nYour writing adapts to the conversation, matching the tone and understanding of the user. Make sure to state the main point clearly and early, then develop it with the explanation and detail the reader needs. Let each sentence build on what came before. Develop the points that matter and provide enough support to be useful. \n\nUse plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements. Write in connected prose. Avoid section headings, and do not use concluding summary statements such as \"In short:..\", \"The simplest mental model is:...\".\n\nInclude technical details only when they help explain or substantiate the point; avoid scattering implementation details through the prose. Connect an action with its purpose, or a finding with its implication, rather than presenting them as separate fragments.\n\nDefault to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare, and avoid nested lists unless the hierarchy cannot be expressed clearly in prose. \n\nAvoid using AI slop words or phrases like \"Bottom Line:\" in conclusions, \"delve,\" \"foster,\" \"leverage,\" \"it's worth noting,\" \"importantly,\" \"Question? Answer.\" or \"This isn't about X. It's about Y.\", \"genuinely\" or hyphenated compound descriptions and adjectives. \n\nState the intended action directly. Avoid adding what you won't do, what will remain unchanged, or how you'll separate or categorize results. Do not use contrastive framing such as \"X, not Y\" or \"X—not Y\" that introduces an unprompted alternative that the user didn't ask about. Avoid invented compound labels like \"exact-head checks\" and \"editorial-row layouts\", vague qualifiers, and canned transitions; use plain verbs and prepositions to state the actual relationship directly.\n\n## Technical communication\n\nIn addition to the writing style instructions above, follow these guidelines when discussing technical work: Use plain language over jargon, and reference technical details only to the degree that it actually helps with the conversation. Communicate complex concepts in a clear and cohesive manner. Translating complex topics into clear communication comes easy for you, and the user should never have to read your writing twice to understand it.\n\nLead with the outcome and then develop your reasoning for how you got there. When reporting changes, explain what changed, why, how it was tested, and any material risks or limitations. Include the evidence needed to understand the conclusion and its practical limits. \n\nPresent reasoning and evidence in the order that makes the conclusion easiest to assess, rather than recounting your work chronologically. Summarize routine verification instead of listing every check. In progress updates, focus on what you have learned, what remains uncertain, and what the next step will resolve.\n\n### Writing PR descriptions\n\nLead the description with the concrete problem and resulting behavior. Use a concrete trigger and before/after example when helpful. Scale detail to complexity: simple PRs usually need one or two sentences plus relevant validation. Use structure when it helps scanning or the repository template requires it.\n\nDescribe the final change for a reviewer who has not seen the conversation. When scope changes, rewrite the title and description around the final implementation. Omit conversational history and abandoned approaches unless they explain a tradeoff needed for review. Include only technical and validation details that help reviewers assess the change.\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\nWhen available, you can use the `functions.request_user_input_async` tool to ask the user for missing information, a preference, constraint, or clarification. You can ask multiple questions in a single tool call. Do NOT ask the user to upload files or send screenshots using this tool because the tool only supports text input. Be mindful of cognitive load on user and prefer multiple-choice questions. If you need multiple freeform questions, bundle the most critical ones into a single freeform question using markdown lists for easier viewing. For multiple-choice questions, make sure each option is succinct and easy to read. Ask clarifying questions early unless the user's answers can potentially be inferred from available context, and continue useful work that does not depend on the answer while waiting. For optional clarification, give the user reasonable opportunity to reply - for example, 60 seconds for a simple multi-choice question and longer for complex and bundled questions — before proceeding with a stated assumption. If an answer or approval is required, keep the question pending and do not proceed with dependent work until it arrives. Elapsed time is not an answer or approval.\n\nThe user may send a new message while you are still working. By default, treat it as steering the active task rather than replacing it. Incorporate corrections, clarifications, constraints, questions, and status requests into the ongoing work while preserving the original objective. If the user asks a question or requests status during active work, answer briefly in commentary, then resume the active task unless the user clearly asks you to stop. Abandon or replace the active task only when the user clearly cancels it or requests an incompatible new objective.\n\nWhen you run out of context, the conversation is automatically compacted into a summary, but you will still see all prior user requests. Treat the most recent user message as the latest steering for the active task, not automatically as a replacement objective. Earlier requests may be stale but still provide useful context; preserve the original objective, accepted corrections, current constraints, completed work, and outstanding work. Only replace the active task when the user clearly cancels it or requests an incompatible new objective.\n\nCompaction does not end the task. Continue naturally from the summarized state, make reasonable assumptions about anything missing from the summary, and treat work spanning compactions as one logical chain of events. Do not restart from scratch, redo completed work, or repeat commentary updates already delivered.\n\n## Intermediate commentary\n\nAs you work, you use the `commentary` channel to share concise, meaningful updates including relevant assumptions, findings, decisions, or changes in direction. The goal of these messages is to make your work, and plans for the turn, 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 send user facing questions in intermediate commentary messages. Do NOT put a final response in the commentary channel. 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 \" or \"I will do , not \".\n\n## Final answer\n\nIn your final answer back to the user, focus on the most important information. \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\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### Visualizations\n\nUse a visualization when they help present information more clearly or make an explanation easier to understand. Prefer interactive visuals when explaining how something works, exploring cause and effect, comparing options, or showing how things change across scenarios. The user does not need to explicitly request a visualization. \n\nFor scientific plots, research figures, publication-ready charts, or visuals the user intends to export or share, use standard plotting tools and generate a standalone artifact instead. \n\nUse tables for mappings or comparisons. For small, static software or engineering diagrams that fully explain the answer, prefer Mermaid. Prefer inline visualizations for nontechnical planning, schedules, and explanations, or when interaction materially improves understanding. \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- Batch independent searches and reads in one functions.exec using await Promise.allSettled([...]); inspect every result. Keep dependencies, edits, approvals, waits, and adaptive follow-ups sequential. Avoid unnecessary output.\n- When calling `functions.exec`, parallelize independent tool calls by awaiting Promises. Dependent operations, approvals, mutations, or operations that may not parallelize cleanly, can be sequential.\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 `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- Treat shell command text as code. `JSON.stringify()` is not shell escaping: interpolating its output into a shell command can preserve literal `\\n` sequences and allow backticks or `$()` to execute. Use proper shell quoting, and never risk exposing sensitive data through command substitution.\n- Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk.\n- Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision\n- Do not write tests for reversible, low-impact changes or that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation.\n- Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. Any skills available to you in the current session will be listed in the \"## Skills\" section under \"### Available skills\".\n\nEach 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\nThe user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions. \n\nThe first time in a conversation that you decide to apply a skill, inform the user in the commentary channel.\n\nIf a skill causes you to ask for permission or confirmation, pause, or leave requested work unfinished, name and link to the exact SKILL.md you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation. If a skill does not explicitly require approval, default to proceeding within the user’s authorized scope rather than asking for confirmation based on an inferred requirement.\n\n## When to use a skill\n\nIf the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why.\n\nIf your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill.\n\n## How to use skills\n\nOpen and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling `skills.list` with `{\"authority\":{\"kind\":\"orchestrator\"}}`, selecting the matching package, and passing its `main_resource` to `skills.read`. Avoid re-reading skills when possible. \n\nWhen a `SKILL.md` file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths.\n\n# Apps (Connectors)\n\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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\n# Plugins\n\nA plugin is a local bundle of skills, MCP servers, and apps.\n\n## How to use plugins\n\n- Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list.\n- MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as mcp__server__tool; use tool provenance to tell which plugin they come from.\n- Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.\n- Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.\n- Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn.\n- Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-23T05:21:37.365Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:37.365Z", "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": "2026-09-23T05:21:37.365Z", "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": "2026-09-23T05:21:37.365Z", "text": "# Collaboration Mode: Default\n\nYou are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.\n\nYour active mode changes only when new developer instructions with a different `...` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.\n\n## request_user_input availability\n\nUse the `request_user_input` tool only when it is listed in the available tools for this turn.\n\nUse the `request_user_input` tool only for optional questions where the answer would materially improve the quality of the work.\n\nIf `request_user_input` returns no answers, continue with best judgment instead of asking again or treating the turn as blocked.\n\nNever use the `request_user_input` tool for permission requests or permission-related escalations.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-23T05:21:37.365Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:37.365Z", "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.\n`send_message` calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers.\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": "2026-09-23T05:21:37.365Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:37.365Z", "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": "2026-09-23T05:21:37.365Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:37.365Z", "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": "2026-09-23T05:21:37.365Z", "text": "\n /app\n sh\n 2026-09-23\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-23T05:21:37.366Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": false, "collaboration_mode": { "mode": "default", "model": "gpt-6-astra", "instructions": "0a11049b0421e1e86f1e129af8d547a89548a0c3" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "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-6-astra", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "30fb77255ffd0abcecb08a8897e242564dfe1192" }, "multi_agent_usage_hint": "30fb77255ffd0abcecb08a8897e242564dfe1192", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-6-astra", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:37.368Z", "data": { "type": "turn_context", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-6-astra", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-6-astra", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-23T05:21:37.385Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:37.385Z", "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin 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, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described 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 builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt 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- You have up to 30 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": "2026-09-23T05:21:39.969Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:21:39.969Z", "text": "I’ll inspect the data and compute resources, save a valid baseline, then use the public backtest to tune the ranker before refitting on all observed history.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:21:46.112Z", "callId": "call_736rRiBR5lQLm04FtIEXCr2s", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -la /task && cat /task/requirements.txt && ls -la /app && mkdir -p /app/solution\",max_output_tokens:6000}));\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os\\nimport pyarrow.parquet as pq\\nfor f in os.listdir('/task/data'):\\n p='/task/data/'+f\\n if f.endswith('.parquet'):\\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema_arrow)\\n else: print(f,os.path.getsize(p))\\nimport torch\\nprint('torch',torch.__version__,'cuda',torch.cuda.is_available())\\nprint('cpus',os.cpu_count())\\nPY\",\"max_output_tokens\":7000}));\ntext(await tools.exec_command({cmd:\"cat /task/evaluate_public.py && cat /task/ranking_evaluation.py\",\"max_output_tokens\":11000}));\n" }, "result": { "timestamp": "2026-09-23T05:21:46.258Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ba5160\",\"wall_time_seconds\":0.000003481,\"exit_code\":0,\"original_token_count\":439,\"output\":\"total 36\\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 data\\n-r--r--r-- 1 root root 5774 Sep 23 05:15 evaluate_public.py\\n-r--r--r-- 1 root root 8265 Sep 23 05:15 ranking_evaluation.py\\n-r--r--r-- 1 root root 1278 Sep 23 05:15 requirements.txt\\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\\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. The same inventory is used for source preparation,\\n# task building, reference scoring, judging, and solver attempts. Python itself\\n# is pinned by task.toml and both 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\\nidna==3.18\\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\\nmdit-py-plugins==0.6.1\\nmdurl==0.1.2\\nnbformat==5.11.1\\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\\nsix==1.17.0\\ntext-unidecode==1.3\\nthreadpoolctl==3.6.0\\ntqdm==4.70.0\\ntraitlets==5.16.1\\ntyping-extensions==4.16.0\\ntzdata==2026.3\\nurllib3==2.7.0\\nwebencodings==0.6.1\\ntotal 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 solution\\n\"}\n{\"chunk_id\":\"e3ba92\",\"wall_time_seconds\":0.00000233,\"exit_code\":1,\"original_token_count\":30,\"output\":\"Traceback (most recent call last):\\n File \\\"\\\", line 2, in \\nModuleNotFoundError: No module named 'pyarrow'\\n\"}\n{\"chunk_id\":\"7aba23\",\"wall_time_seconds\":0.00000404,\"exit_code\":0,\"original_token_count\":3510,\"output\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Score a validation-stage ranking against public discovery labels.\\n\\n``build_task.py`` publishes this module with the solver-visible task files. It\\nloads target users, validation eligibility, and validation-window events from\\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\\ncommand line. Stage-independent mechanics come from the adjacent public\\n``ranking_evaluation.py`` module. The builder installs the same checked-in\\nmodule in the private judge, so public and private scoring mechanics cannot\\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\\nan organic listen with at least 80 percent completion; a like takes precedence\\nwhen both events exist.\\n\\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\\nignored. Quality is graded NDCG@10, averaged only over users who have at least\\none eligible relevant item in the validation window.\\n\\nThe evaluator prints a JSON object containing the model path and scores. It\\ndoes not apply the task's pass threshold; its purpose is model development and\\nselection. The private judge applies the threshold to the final submission.\\n\\nUsage::\\n\\n python evaluate_public.py \\n\\\"\\\"\\\"\\nfrom __future__ import annotations\\n\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nimport pandas as pd\\n\\nfrom ranking_evaluation import (\\n CandidateSet,\\n RankingValidationError,\\n ranked_items,\\n read_ranking,\\n score_rankings,\\n validate_ranking,\\n)\\n\\n\\nHERE = Path(__file__).resolve().parent\\nDATA = HERE / \\\"data\\\"\\nLISTEN_STRONG = 80\\n\\n\\ndef validation_truth(\\n eligibility: CandidateSet, targets: set[int]\\n) -> dict[int, dict[int, int]]:\\n \\\"\\\"\\\"Build cutoff-correct graded relevance from public validation events.\\n\\n Only target-user events for eligible candidates are retained. Organic\\n listens completed to at least 80 percent receive grade 1, and organic likes\\n receive grade 3. Repeated user-item events are collapsed, and processing\\n likes second ensures that grade 3 wins over grade 1.\\n\\n Args:\\n eligibility: Validation-cutoff candidate membership service.\\n targets: Complete set of target user IDs.\\n\\n Returns:\\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\\n relevant event are omitted.\\n \\\"\\\"\\\"\\n listens = pd.read_parquet(DATA / \\\"val_listens.parquet\\\")\\n likes = pd.read_parquet(DATA / \\\"val_likes.parquet\\\")\\n strong = listens[\\n listens.uid.isin(targets)\\n & (listens.is_organic == 1)\\n & (listens.played_ratio_pct >= LISTEN_STRONG)\\n ][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n organic_likes = likes[\\n likes.uid.isin(targets) & (likes.is_organic == 1)\\n ][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n\\n graded: dict[int, dict[int, int]] = {}\\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\\n uid, item = int(raw_uid), int(raw_item)\\n if eligibility.is_eligible(uid, item):\\n graded.setdefault(uid, {})[item] = 1\\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\\n uid, item = int(raw_uid), int(raw_item)\\n if eligibility.is_eligible(uid, item):\\n graded.setdefault(uid, {})[item] = 3\\n return graded\\n\\n\\ndef load_submission(\\n path: Path, eligibility: CandidateSet, targets: set[int]\\n) -> dict[int, list[int]]:\\n \\\"\\\"\\\"Load and validate a public validation ranking.\\n\\n Files ending in ``.parquet`` are read as parquet; every other suffix is\\n read as CSV. The required columns must be integer-valued, cover exactly the\\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\\n duplicate user-item pairs, and contain only cutoff-eligible items.\\n\\n Args:\\n path: Ranking file to read.\\n eligibility: Validation-cutoff candidate membership service.\\n targets: Exact set of users the ranking must cover.\\n\\n Returns:\\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\\n\\n Raises:\\n ValueError: If the ranking violates the required schema or invariants.\\n \\\"\\\"\\\"\\n frame = validate_ranking(read_ranking(path), targets)\\n if not all(\\n eligibility.is_eligible(uid, item)\\n for uid, item in frame[[\\\"uid\\\", \\\"item_id\\\"]].itertuples(index=False, name=None)\\n ):\\n raise RankingValidationError(\\n \\\"submission contains an item rejected by eligibility service\\\"\\n )\\n return ranked_items(frame)\\n\\n\\ndef score(\\n ranked: dict[int, list[int]],\\n graded: dict[int, dict[int, int]],\\n) -> dict[str, float | int]:\\n \\\"\\\"\\\"Aggregate per-user NDCG@10 over users with public relevance labels.\\n\\n Args:\\n ranked: Ranked item IDs for every target user.\\n graded: Eligible validation relevance keyed by user and item.\\n\\n Returns:\\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\\n \\\"\\\"\\\"\\n return score_rankings(ranked, graded)\\n\\n\\ndef main() -> None:\\n \\\"\\\"\\\"Run the public evaluator CLI and print its JSON score report.\\\"\\\"\\\"\\n targets_frame = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n targets = set(targets_frame.uid.astype(int))\\n eligibility = CandidateSet(DATA / \\\"validation_eligible_candidates.npz\\\")\\n graded = validation_truth(eligibility, targets)\\n\\n if len(sys.argv) != 2:\\n raise SystemExit(\\n \\\"usage: evaluate_public.py \\\"\\n )\\n path = Path(sys.argv[1])\\n ranked = load_submission(path, eligibility, targets)\\n print(json.dumps({\\\"model\\\": str(path), \\\"scores\\\": score(ranked, graded)}, indent=2))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\\\"\\\"\\\"Shared public primitives for Yambda discovery-ranking evaluation.\\n\\nBoth the solver-visible validation evaluator and the private submission judge\\nimport this module from the generated task's ``public`` directory. It owns the\\nstage-independent contract: reading a ranking, validating its shape, converting\\nit to per-user rank order, looking up packed candidate eligibility, and\\ncomputing graded NDCG@10.\\n\\nThe module deliberately contains no event-derived labels, private artifact\\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\\nper-cutoff eligibility checks around these common operations.\\n\\\"\\\"\\\"\\nfrom __future__ import annotations\\n\\nfrom pathlib import Path\\n\\nimport numpy as np\\nimport pandas as pd\\n\\n\\n__all__ = [\\n \\\"CandidateSet\\\",\\n \\\"QUALITY_K\\\",\\n \\\"RankingValidationError\\\",\\n \\\"REQUIRED_COLUMNS\\\",\\n \\\"TOP_N\\\",\\n \\\"ndcg_at_k\\\",\\n \\\"ranked_items\\\",\\n \\\"read_ranking\\\",\\n \\\"score_rankings\\\",\\n \\\"validate_ranking\\\",\\n]\\n\\nTOP_N = 100\\nQUALITY_K = 10\\nREQUIRED_COLUMNS = (\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\")\\n\\n\\nclass RankingValidationError(ValueError):\\n \\\"\\\"\\\"Report a ranking-schema or structural-contract violation.\\\"\\\"\\\"\\n\\n\\nclass CandidateSet:\\n \\\"\\\"\\\"Provide membership lookups over one packed candidate-set archive.\\n\\n The archive contains aligned ``uids``, ``item_ids``, and\\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\\n copied into memory so they remain valid after the archive is closed.\\n \\\"\\\"\\\"\\n\\n def __init__(self, path: Path):\\n \\\"\\\"\\\"Load an eligibility archive and build ID-to-position indexes.\\n\\n Args:\\n path: Path to a ``.npz`` archive with the candidate-set schema.\\n \\\"\\\"\\\"\\n with np.load(path, allow_pickle=False) as artifact:\\n self.uids = artifact[\\\"uids\\\"].astype(np.int64, copy=True)\\n self.item_ids = artifact[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n self.packed = artifact[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\\n self.item_column = {\\n int(item): column for column, item in enumerate(self.item_ids)\\n }\\n\\n def is_eligible(self, uid: int, item_id: int) -> bool:\\n \\\"\\\"\\\"Return whether ``item_id`` is eligible for ``uid``.\\n\\n IDs absent from the target-user or candidate-item arrays are treated as\\n ineligible rather than raising an exception.\\n\\n Args:\\n uid: Target user ID to query.\\n item_id: Candidate item ID to query.\\n\\n Returns:\\n ``True`` only when the corresponding packed eligibility bit is set.\\n \\\"\\\"\\\"\\n row = self.uid_row.get(int(uid))\\n column = self.item_column.get(int(item_id))\\n if row is None or column is None:\\n return False\\n byte, bit = divmod(column, 8)\\n return bool((int(self.packed[row, byte]) >> bit) & 1)\\n\\n\\ndef read_ranking(path: Path) -> pd.DataFrame:\\n \\\"\\\"\\\"Read a parquet or CSV ranking into a pandas frame.\\n\\n Args:\\n path: Input path. A ``.parquet`` suffix selects parquet; every other\\n suffix is interpreted as CSV.\\n\\n Returns:\\n The ranking frame as loaded by pandas.\\n \\\"\\\"\\\"\\n return pd.read_parquet(path) if path.suffix == \\\".parquet\\\" else pd.read_csv(path)\\n\\n\\ndef validate_ranking(\\n frame: pd.DataFrame,\\n targets: set[int],\\n) -> pd.DataFrame:\\n \\\"\\\"\\\"Normalize and validate the stage-independent ranking contract.\\n\\n Required values must be finite integers. The user set must exactly match\\n ``targets``; every user must have 100 rows, distinct items, and each rank\\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\\n intentionally left to the calling evaluator.\\n\\n Args:\\n frame: Raw ranking frame. Extra columns are discarded from an internal\\n copy; the caller's frame is not modified.\\n targets: Exact set of target user IDs the ranking must cover.\\n\\n Returns:\\n A normalized frame containing only the required ``int64`` columns.\\n\\n Raises:\\n RankingValidationError: If the ranking violates the shared contract.\\n \\\"\\\"\\\"\\n required = list(REQUIRED_COLUMNS)\\n if not all(column in frame for column in required):\\n raise RankingValidationError(f\\\"submission requires columns {required}\\\")\\n\\n normalized = frame[required].copy()\\n for column in required:\\n numeric = pd.to_numeric(normalized[column], errors=\\\"coerce\\\")\\n values = numeric.to_numpy(dtype=float)\\n if numeric.isna().any() or not np.isfinite(values).all():\\n raise RankingValidationError(\\n f\\\"submission {column} must be finite numeric values\\\"\\n )\\n if not np.equal(numeric, np.floor(numeric)).all():\\n raise RankingValidationError(\\n f\\\"submission {column} must contain integers\\\"\\n )\\n normalized[column] = numeric.astype(np.int64)\\n\\n submitted_users = set(map(int, normalized.uid.unique()))\\n if submitted_users != targets:\\n raise RankingValidationError(\\n f\\\"submission user set mismatch: missing={len(targets - submitted_users)} \\\"\\n f\\\"extra={len(submitted_users - targets)}\\\"\\n )\\n counts = normalized.groupby(\\\"uid\\\").size()\\n if not (counts == TOP_N).all():\\n raise RankingValidationError(\\n f\\\"submission must contain exactly {TOP_N} rows for every target user\\\"\\n )\\n if normalized.duplicated([\\\"uid\\\", \\\"item_id\\\"]).any():\\n raise RankingValidationError(\\n \\\"submission contains duplicate (uid, item_id) pairs\\\"\\n )\\n valid_ranks = set(range(1, TOP_N + 1))\\n ranks_are_valid = normalized.groupby(\\\"uid\\\")[\\\"rank\\\"].apply(\\n lambda values: set(map(int, values)) == valid_ranks\\n )\\n if not ranks_are_valid.all():\\n raise RankingValidationError(\\n f\\\"each user must have every rank from 1 through {TOP_N} exactly once\\\"\\n )\\n return normalized\\n\\n\\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\\n \\\"\\\"\\\"Convert a validated ranking frame to ordered items keyed by user.\\n\\n Args:\\n frame: Frame that has passed :func:`validate_ranking`.\\n\\n Returns:\\n A ``uid -> item IDs`` mapping ordered by ascending rank.\\n \\\"\\\"\\\"\\n ordered = frame.sort_values([\\\"uid\\\", \\\"rank\\\"])\\n return {\\n int(uid): list(map(int, values))\\n for uid, values in ordered.groupby(\\\"uid\\\").item_id\\n }\\n\\n\\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\\n \\\"\\\"\\\"Compute graded normalized discounted cumulative gain at ``k``.\\n\\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\\n\\n Args:\\n ranked: Item IDs in predicted rank order.\\n gold: Mapping from relevant item IDs to integer relevance grades.\\n k: Maximum number of predicted positions to score.\\n\\n Returns:\\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\\n ``0.0`` when the ideal gain is zero.\\n \\\"\\\"\\\"\\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\\n return dcg / idcg if idcg else 0.0\\n\\n\\ndef score_rankings(\\n ranked: dict[int, list[int]],\\n graded: dict[int, dict[int, int]],\\n) -> dict[str, float | int]:\\n \\\"\\\"\\\"Aggregate per-user NDCG@10 over users with relevance labels.\\n\\n Args:\\n ranked: Ranked item IDs for every target user.\\n graded: Relevant item grades keyed by user and item.\\n\\n Returns:\\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\\n \\\"\\\"\\\"\\n quality = float(\\n np.mean(\\n [\\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\\n for uid, gold in graded.items()\\n ]\\n )\\n )\\n return {\\n \\\"quality_ndcg@10\\\": round(quality, 6),\\n \\\"quality_users\\\": len(graded),\\n }\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:46.162Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab361e19da487d291e54563acf045d9", "usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 252, "reasoning_output_tokens": 0, "total_tokens": 15658 }, "turn_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 252, "reasoning_output_tokens": 0, "total_tokens": 15658 }, "thread_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 252, "reasoning_output_tokens": 0, "total_tokens": 15658 } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:46.171Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-787c4595-8a27-43c2-ad3f-87d0377a944b", "process_id": "7775", "command": [ "/bin/sh", "-lc", "ls -la /task && cat /task/requirements.txt && ls -la /app && mkdir -p /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /task && cat /task/requirements.txt && ls -la /app && mkdir -p /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 data\n-r--r--r-- 1 root root 5774 Sep 23 05:15 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 05:15 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 05:15 requirements.txt\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 solution\n", "stderr": "", "aggregated_output": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 data\n-r--r--r-- 1 root root 5774 Sep 23 05:15 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 05:15 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 05:15 requirements.txt\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3481 }, "formatted_output": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 data\n-r--r--r-- 1 root root 5774 Sep 23 05:15 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 05:15 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 05:15 requirements.txt\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 solution\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:46.218Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a98315d2-97b1-47d1-be41-495fce2ca8dd", "process_id": "60946", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os\nimport pyarrow.parquet as pq\nfor f in os.listdir('/task/data'):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema_arrow)\n else: print(f,os.path.getsize(p))\nimport torch\nprint('torch',torch.__version__,'cuda',torch.cuda.is_available())\nprint('cpus',os.cpu_count())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os\nimport pyarrow.parquet as pq\nfor f in os.listdir('/task/data'):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema_arrow)\n else: print(f,os.path.getsize(p))\nimport torch\nprint('torch',torch.__version__,'cuda',torch.cuda.is_available())\nprint('cpus',os.cpu_count())\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": 2330 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:46.252Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3196929-dc50-4166-94c5-d7315287853f", "process_id": "48430", "command": [ "/bin/sh", "-lc", "cat /task/evaluate_public.py && cat /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "cat /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4040 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:21:49.139Z", "callId": "call_buErdp3O0MQgXXZpWoBuJKhG", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"which python python3; ls -la /opt /app/solution /usr/local/bin; printenv | sort\",\"max_output_tokens\":5000}));\n" }, "result": { "timestamp": "2026-09-23T05:21:49.183Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"83b11f\",\"wall_time_seconds\":0.00000239,\"exit_code\":0,\"original_token_count\":566,\"output\":\"/usr/local/bin/python\\n/usr/local/bin/python3\\n/app/solution:\\ntotal 8\\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 .\\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 ..\\n\\n/opt:\\ntotal 16\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\\ndrwxr-xr-x 4 root root 4096 Sep 23 05:19 codex-home\\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 task-venv\\n\\n/usr/local/bin:\\ntotal 48\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\\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 53 Sep 23 05:19 codex -> /opt/codex-home/packages/standalone/current/bin/codex\\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 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\\nCODEX_CI=1\\nCODEX_HOME=/tmp/codex-home\\nCODEX_SESSION_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\\nCODEX_THREAD_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\\nCOLORTERM=\\nGH_PAGER=cat\\nGIT_PAGER=cat\\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\\nHOME=/home/agent\\nHOSTNAME=3377d9f012cd\\nLANG=C.UTF-8\\nLC_ALL=C.UTF-8\\nLC_CTYPE=C.UTF-8\\nNO_COLOR=1\\nPAGER=cat\\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/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_=/usr/local/bin/codex\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:49.169Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab361ea7a6087d2ba5c3ea1d260157f", "usage": { "input_tokens": 20266, "cached_input_tokens": 15232, "cache_write_input_tokens": 0, "output_tokens": 49, "reasoning_output_tokens": 0, "total_tokens": 20315 }, "turn_token_usage": { "input_tokens": 35672, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 301, "reasoning_output_tokens": 0, "total_tokens": 35973 }, "thread_token_usage": { "input_tokens": 35672, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 301, "reasoning_output_tokens": 0, "total_tokens": 35973 } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:49.178Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0bd381d0-0028-4a4e-95cb-20976ade1da3", "process_id": "18124", "command": [ "/bin/sh", "-lc", "which python python3; ls -la /opt /app/solution /usr/local/bin; printenv | sort" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python python3; ls -la /opt /app/solution /usr/local/bin; printenv | sort" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\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 53 Sep 23 05:19 codex -> /opt/codex-home/packages/standalone/current/bin/codex\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 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\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCODEX_THREAD_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=3377d9f012cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/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_=/usr/local/bin/codex\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\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 53 Sep 23 05:19 codex -> /opt/codex-home/packages/standalone/current/bin/codex\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 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\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCODEX_THREAD_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=3377d9f012cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/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_=/usr/local/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2390 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:19 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:21 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:19 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 .\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 53 Sep 23 05:19 codex -> /opt/codex-home/packages/standalone/current/bin/codex\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 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\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCODEX_THREAD_ID=01a0ccb6-531a-73b1-ae5c-d4f7e8920871\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=3377d9f012cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/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_=/usr/local/bin/codex\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:21:58.107Z", "callId": "call_2Ku5KXTzL2PHoV9OZIfsugXf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\\nfor f in os.listdir('/task/data'):\\n p='/task/data/'+f\\n if f.endswith('.parquet'):\\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,str(q.schema_arrow))\\n else:\\n a=np.load(p);print(f,[(k,a[k].shape) for k in a.files])\\nprint('cpu',os.cpu_count())\\nprint(pd.read_parquet('/task/data/train_listens.parquet').describe())\\nPY\",\"max_output_tokens\":5000}));\ntext(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/cpu.max\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:22:03.125Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"0b39c2\",\"wall_time_seconds\":4.816906681,\"exit_code\":0,\"original_token_count\":956,\"output\":\"test_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\\nvalidation_preference_state.parquet 469124 uid: uint32\\nitem_id: uint32\\nstate: string\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 403\\nval_undislikes.parquet 606 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ntrain_likes.parquet 814831 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ncandidate_catalog.parquet 30000 item_id: uint32\\npositive_listens: int64\\norganic_positive_listens: uint64\\npopularity_rank: int32\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 591\\nval_listens.parquet 1501697 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\nplayed_ratio_pct: uint16\\ntrack_length_seconds: uint32\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 797\\nval_unlikes.parquet 9346 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ntrain_unlikes.parquet 292665 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ntrain_dislikes.parquet 101075 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ntest_preference_state.parquet 484776 uid: uint32\\nitem_id: uint32\\nstate: string\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 403\\nvalidation_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\\ntarget_users.parquet 8456 uid: uint32\\nrecent_positive_listens: double\\nhistory_positive_listens: int64\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 472\\ntrain_undislikes.parquet 19960 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\nval_likes.parquet 37795 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\nval_dislikes.parquet 3563 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 527\\ntrain_listens.parquet 43484159 uid: uint32\\ntimestamp: uint32\\nitem_id: uint32\\nis_organic: uint8\\nplayed_ratio_pct: uint16\\ntrack_length_seconds: uint32\\n-- schema metadata --\\npandas: '{\\\"index_columns\\\": [], \\\"column_indexes\\\": [], \\\"columns\\\": [{\\\"name\\\":' + 797\\ncpu 8\\n uid timestamp ... played_ratio_pct track_length_seconds\\ncount 4.348416e+07 4.348416e+07 ... 4.348416e+07 4.348416e+07\\nmean 4.985911e+05 1.369709e+07 ... 6.465021e+01 2.072443e+02\\nstd 2.870021e+05 7.122591e+06 ... 4.412183e+01 8.447807e+01\\nmin 1.000000e+02 0.000000e+00 ... 0.000000e+00 5.000000e+00\\n25% 2.497000e+05 7.741985e+06 ... 7.000000e+00 1.650000e+02\\n50% 4.938000e+05 1.424461e+07 ... 1.000000e+02 2.000000e+02\\n75% 7.439000e+05 2.001016e+07 ... 1.000000e+02 2.350000e+02\\nmax 1.000000e+06 2.478680e+07 ... 1.590000e+02 2.495000e+03\\n\\n[8 rows x 6 columns]\\n\"}\n{\"chunk_id\":\"385169\",\"wall_time_seconds\":0.00000383,\"exit_code\":0,\"original_token_count\":7,\"output\":\"8589934592\\n400000 100000\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:58.155Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab361ed6a2887d29d1fba09af185674", "usage": { "input_tokens": 21425, "cached_input_tokens": 20096, "cache_write_input_tokens": 0, "output_tokens": 197, "reasoning_output_tokens": 0, "total_tokens": 21622 }, "turn_token_usage": { "input_tokens": 57097, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 498, "reasoning_output_tokens": 0, "total_tokens": 57595 }, "thread_token_usage": { "input_tokens": 57097, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 498, "reasoning_output_tokens": 0, "total_tokens": 57595 } } }, { "kind": "context", "timestamp": "2026-09-23T05:22:03.087Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e09c1e0e-9e9a-4c08-99c2-59df38a54b17", "process_id": "84386", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nfor f in os.listdir('/task/data'):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,str(q.schema_arrow))\n else:\n a=np.load(p);print(f,[(k,a[k].shape) for k in a.files])\nprint('cpu',os.cpu_count())\nprint(pd.read_parquet('/task/data/train_listens.parquet').describe())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nfor f in os.listdir('/task/data'):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n q=pq.ParquetFile(p); print(f,q.metadata.num_rows,str(q.schema_arrow))\n else:\n a=np.load(p);print(f,[(k,a[k].shape) for k in a.files])\nprint('cpu',os.cpu_count())\nprint(pd.read_parquet('/task/data/train_listens.parquet').describe())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nvalidation_preference_state.parquet 469124 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nval_undislikes.parquet 606 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_likes.parquet 814831 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ncandidate_catalog.parquet 30000 item_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\nval_listens.parquet 1501697 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_unlikes.parquet 9346 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_unlikes.parquet 292665 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_dislikes.parquet 101075 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntest_preference_state.parquet 484776 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nvalidation_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\ntarget_users.parquet 8456 uid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\ntrain_undislikes.parquet 19960 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_likes.parquet 37795 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_dislikes.parquet 3563 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_listens.parquet 43484159 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ncpu 8\n uid timestamp ... played_ratio_pct track_length_seconds\ncount 4.348416e+07 4.348416e+07 ... 4.348416e+07 4.348416e+07\nmean 4.985911e+05 1.369709e+07 ... 6.465021e+01 2.072443e+02\nstd 2.870021e+05 7.122591e+06 ... 4.412183e+01 8.447807e+01\nmin 1.000000e+02 0.000000e+00 ... 0.000000e+00 5.000000e+00\n25% 2.497000e+05 7.741985e+06 ... 7.000000e+00 1.650000e+02\n50% 4.938000e+05 1.424461e+07 ... 1.000000e+02 2.000000e+02\n75% 7.439000e+05 2.001016e+07 ... 1.000000e+02 2.350000e+02\nmax 1.000000e+06 2.478680e+07 ... 1.590000e+02 2.495000e+03\n\n[8 rows x 6 columns]\n", "stderr": "", "aggregated_output": "test_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nvalidation_preference_state.parquet 469124 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nval_undislikes.parquet 606 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_likes.parquet 814831 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ncandidate_catalog.parquet 30000 item_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\nval_listens.parquet 1501697 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_unlikes.parquet 9346 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_unlikes.parquet 292665 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_dislikes.parquet 101075 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntest_preference_state.parquet 484776 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nvalidation_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\ntarget_users.parquet 8456 uid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\ntrain_undislikes.parquet 19960 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_likes.parquet 37795 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_dislikes.parquet 3563 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_listens.parquet 43484159 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ncpu 8\n uid timestamp ... played_ratio_pct track_length_seconds\ncount 4.348416e+07 4.348416e+07 ... 4.348416e+07 4.348416e+07\nmean 4.985911e+05 1.369709e+07 ... 6.465021e+01 2.072443e+02\nstd 2.870021e+05 7.122591e+06 ... 4.412183e+01 8.447807e+01\nmin 1.000000e+02 0.000000e+00 ... 0.000000e+00 5.000000e+00\n25% 2.497000e+05 7.741985e+06 ... 7.000000e+00 1.650000e+02\n50% 4.938000e+05 1.424461e+07 ... 1.000000e+02 2.000000e+02\n75% 7.439000e+05 2.001016e+07 ... 1.000000e+02 2.350000e+02\nmax 1.000000e+06 2.478680e+07 ... 1.590000e+02 2.495000e+03\n\n[8 rows x 6 columns]\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 816895591 }, "formatted_output": "test_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nvalidation_preference_state.parquet 469124 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nval_undislikes.parquet 606 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_likes.parquet 814831 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ncandidate_catalog.parquet 30000 item_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\nval_listens.parquet 1501697 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_unlikes.parquet 9346 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_unlikes.parquet 292665 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_dislikes.parquet 101075 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntest_preference_state.parquet 484776 uid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\nvalidation_eligible_candidates.npz [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\ntarget_users.parquet 8456 uid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\ntrain_undislikes.parquet 19960 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_likes.parquet 37795 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\nval_dislikes.parquet 3563 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\ntrain_listens.parquet 43484159 uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ncpu 8\n uid timestamp ... played_ratio_pct track_length_seconds\ncount 4.348416e+07 4.348416e+07 ... 4.348416e+07 4.348416e+07\nmean 4.985911e+05 1.369709e+07 ... 6.465021e+01 2.072443e+02\nstd 2.870021e+05 7.122591e+06 ... 4.412183e+01 8.447807e+01\nmin 1.000000e+02 0.000000e+00 ... 0.000000e+00 5.000000e+00\n25% 2.497000e+05 7.741985e+06 ... 7.000000e+00 1.650000e+02\n50% 4.938000e+05 1.424461e+07 ... 1.000000e+02 2.000000e+02\n75% 7.439000e+05 2.001016e+07 ... 1.000000e+02 2.350000e+02\nmax 1.000000e+06 2.478680e+07 ... 1.590000e+02 2.495000e+03\n\n[8 rows x 6 columns]\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:22:03.120Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cd52f70a-65ea-4539-a11c-ea32fd64ff4d", "process_id": "2416", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/cpu.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/cpu.max" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8589934592\n400000 100000\n", "stderr": "", "aggregated_output": "8589934592\n400000 100000\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3830 }, "formatted_output": "8589934592\n400000 100000\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-23T05:22:10.023Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:22:10.023Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:22:29.722Z", "callId": "call_lnwmRt3eI7uICzpwVTeeO9Tu", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/inspect.py <<'PY'\\nimport numpy as np,pandas as pd\\nfrom scipy import sparse\\nD='/task/data/'\\na=np.load(D+'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; E=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\\nprint('eligibility',E.sum(1).min(),np.quantile(E.sum(1),[0,.1,.5,.9,1]),flush=True)\\nl=pd.read_parquet(D+'train_listens.parquet');print('unique users/items',l.uid.nunique(),l.item_id.nunique(),flush=True)\\nu=pd.Index(uids).get_indexer(l.uid);i=pd.Index(items).get_indexer(l.item_id);m=(u>=0)&(i>=0)\\nl=l[m].copy();u=u[m];i=i[m]; l['u']=u;l['i']=i\\nprint('mapped',len(l),'eligible',E[u,i].mean(), 'positive eligible',E[u[l.played_ratio_pct>=50],i[l.played_ratio_pct>=50]].mean(),flush=True)\\nl.to_parquet('/app/solution/train_mapped.parquet',index=False)\\nv=pd.read_parquet(D+'val_listens.parquet');vu=pd.Index(uids).get_indexer(v.uid);vi=pd.Index(items).get_indexer(v.item_id);m=(vu>=0)&(vi>=0);v=v[m].copy();vu=vu[m];vi=vi[m];v['u']=vu;v['i']=vi;v.to_parquet('/app/solution/val_mapped.parquet',index=False)\\nseen=sparse.csr_matrix((np.ones(len(u)),(u,i)),shape=E.shape);strong=(v.is_organic==1)&(v.played_ratio_pct>=80)&E[vu,vi].astype(bool)\\nprint('val strong eligible',strong.sum(),'seen',np.asarray(seen[vu[strong],vi[strong]]>0).mean(),flush=True)\\nprint('eligible histories examples',l[E[u,i].astype(bool)].head(20).to_string(index=False),flush=True)\\nfor s in ['validation','test']:\\n p=pd.read_parquet(D+s+'_preference_state.parquet');u=pd.Index(uids).get_indexer(p.uid);i=pd.Index(items).get_indexer(p.item_id);m=(u>=0)&(i>=0)\\n es=np.load(D+s+'_eligible_candidates.npz');ee=np.unpackbits(es['packed_eligible'],axis=1,count=len(items),bitorder='little')\\n print(s,'state eligible',p[m].groupby('state').size(),p[m][ee[u[m],i[m]].astype(bool)].groupby('state').size(),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:22:30.172Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n{\"chunk_id\":\"463659\",\"wall_time_seconds\":0.282192799,\"exit_code\":1,\"original_token_count\":592,\"output\":\"Traceback (most recent call last):\\n File \\\"/app/solution/inspect.py\\\", line 1, in \\n import numpy as np,pandas as pd\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\\\", line 37, in \\n from pandas._config import (\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\\\", line 20, in \\n from pandas._config import config\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\\\", line 72, in \\n from pandas.util._exceptions import find_stack_level\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\\\", line 4, in \\n import inspect\\n File \\\"/app/solution/inspect.py\\\", line 2, in \\n from scipy import sparse\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/__init__.py\\\", line 300, in \\n from ._base import *\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\\\", line 5, in \\n from ._sputils import (asmatrix, check_reshape_kwargs, check_shape,\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_sputils.py\\\", line 10, in \\n from scipy._lib._util import np_long, np_ulong\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_util.py\\\", line 13, in \\n from scipy._lib._array_api import array_namespace, is_numpy, xp_size\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_array_api.py\\\", line 18, in \\n from scipy._lib.array_api_compat import (\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy/__init__.py\\\", line 1, in \\n from numpy import * # noqa: F403\\n ^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/__init__.py\\\", line 42, in \\n from . import core\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\\\", line 8299, in \\n inner.__doc__ = doc_note(np.inner.__doc__,\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\\\", line 123, in doc_note\\n notesplit = re.split(r'\\\\n\\\\s*?Notes\\\\n\\\\s*?-----', inspect.cleandoc(initialdoc))\\n ^^^^^^^^^^^^^^^^\\nAttributeError: partially initialized module 'inspect' has no attribute 'cleandoc' (most likely due to a circular import)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:22:29.815Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab361fb5e0487d28b3f8c3f43c05035", "usage": { "input_tokens": 23047, "cached_input_tokens": 21248, "cache_write_input_tokens": 0, "output_tokens": 796, "reasoning_output_tokens": 130, "total_tokens": 23843 }, "turn_token_usage": { "input_tokens": 80144, "cached_input_tokens": 68736, "cache_write_input_tokens": 0, "output_tokens": 1294, "reasoning_output_tokens": 130, "total_tokens": 81438 }, "thread_token_usage": { "input_tokens": 80144, "cached_input_tokens": 68736, "cache_write_input_tokens": 0, "output_tokens": 1294, "reasoning_output_tokens": 130, "total_tokens": 81438 } } }, { "kind": "context", "timestamp": "2026-09-23T05:22:30.168Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c2eefe37-246c-457e-8cb6-b8f9db3ba250", "process_id": "98851", "command": [ "/bin/sh", "-lc", "cat > /app/solution/inspect.py <<'PY'\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; E=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\nprint('eligibility',E.sum(1).min(),np.quantile(E.sum(1),[0,.1,.5,.9,1]),flush=True)\nl=pd.read_parquet(D+'train_listens.parquet');print('unique users/items',l.uid.nunique(),l.item_id.nunique(),flush=True)\nu=pd.Index(uids).get_indexer(l.uid);i=pd.Index(items).get_indexer(l.item_id);m=(u>=0)&(i>=0)\nl=l[m].copy();u=u[m];i=i[m]; l['u']=u;l['i']=i\nprint('mapped',len(l),'eligible',E[u,i].mean(), 'positive eligible',E[u[l.played_ratio_pct>=50],i[l.played_ratio_pct>=50]].mean(),flush=True)\nl.to_parquet('/app/solution/train_mapped.parquet',index=False)\nv=pd.read_parquet(D+'val_listens.parquet');vu=pd.Index(uids).get_indexer(v.uid);vi=pd.Index(items).get_indexer(v.item_id);m=(vu>=0)&(vi>=0);v=v[m].copy();vu=vu[m];vi=vi[m];v['u']=vu;v['i']=vi;v.to_parquet('/app/solution/val_mapped.parquet',index=False)\nseen=sparse.csr_matrix((np.ones(len(u)),(u,i)),shape=E.shape);strong=(v.is_organic==1)&(v.played_ratio_pct>=80)&E[vu,vi].astype(bool)\nprint('val strong eligible',strong.sum(),'seen',np.asarray(seen[vu[strong],vi[strong]]>0).mean(),flush=True)\nprint('eligible histories examples',l[E[u,i].astype(bool)].head(20).to_string(index=False),flush=True)\nfor s in ['validation','test']:\n p=pd.read_parquet(D+s+'_preference_state.parquet');u=pd.Index(uids).get_indexer(p.uid);i=pd.Index(items).get_indexer(p.item_id);m=(u>=0)&(i>=0)\n es=np.load(D+s+'_eligible_candidates.npz');ee=np.unpackbits(es['packed_eligible'],axis=1,count=len(items),bitorder='little')\n print(s,'state eligible',p[m].groupby('state').size(),p[m][ee[u[m],i[m]].astype(bool)].groupby('state').size(),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/inspect.py <<'PY'\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; E=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\nprint('eligibility',E.sum(1).min(),np.quantile(E.sum(1),[0,.1,.5,.9,1]),flush=True)\nl=pd.read_parquet(D+'train_listens.parquet');print('unique users/items',l.uid.nunique(),l.item_id.nunique(),flush=True)\nu=pd.Index(uids).get_indexer(l.uid);i=pd.Index(items).get_indexer(l.item_id);m=(u>=0)&(i>=0)\nl=l[m].copy();u=u[m];i=i[m]; l['u']=u;l['i']=i\nprint('mapped',len(l),'eligible',E[u,i].mean(), 'positive eligible',E[u[l.played_ratio_pct>=50],i[l.played_ratio_pct>=50]].mean(),flush=True)\nl.to_parquet('/app/solution/train_mapped.parquet',index=False)\nv=pd.read_parquet(D+'val_listens.parquet');vu=pd.Index(uids).get_indexer(v.uid);vi=pd.Index(items).get_indexer(v.item_id);m=(vu>=0)&(vi>=0);v=v[m].copy();vu=vu[m];vi=vi[m];v['u']=vu;v['i']=vi;v.to_parquet('/app/solution/val_mapped.parquet',index=False)\nseen=sparse.csr_matrix((np.ones(len(u)),(u,i)),shape=E.shape);strong=(v.is_organic==1)&(v.played_ratio_pct>=80)&E[vu,vi].astype(bool)\nprint('val strong eligible',strong.sum(),'seen',np.asarray(seen[vu[strong],vi[strong]]>0).mean(),flush=True)\nprint('eligible histories examples',l[E[u,i].astype(bool)].head(20).to_string(index=False),flush=True)\nfor s in ['validation','test']:\n p=pd.read_parquet(D+s+'_preference_state.parquet');u=pd.Index(uids).get_indexer(p.uid);i=pd.Index(items).get_indexer(p.item_id);m=(u>=0)&(i>=0)\n es=np.load(D+s+'_eligible_candidates.npz');ee=np.unpackbits(es['packed_eligible'],axis=1,count=len(items),bitorder='little')\n print(s,'state eligible',p[m].groupby('state').size(),p[m][ee[u[m],i[m]].astype(bool)].groupby('state').size(),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in \n from pandas._config import (\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in \n from pandas._config import config\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in \n from pandas.util._exceptions import find_stack_level\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in \n import inspect\n File \"/app/solution/inspect.py\", line 2, in \n from scipy import sparse\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/__init__.py\", line 300, in \n from ._base import *\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 5, in \n from ._sputils import (asmatrix, check_reshape_kwargs, check_shape,\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_sputils.py\", line 10, in \n from scipy._lib._util import np_long, np_ulong\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_util.py\", line 13, in \n from scipy._lib._array_api import array_namespace, is_numpy, xp_size\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_array_api.py\", line 18, in \n from scipy._lib.array_api_compat import (\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy/__init__.py\", line 1, in \n from numpy import * # noqa: F403\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/__init__.py\", line 42, in \n from . import core\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 8299, in \n inner.__doc__ = doc_note(np.inner.__doc__,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 123, in doc_note\n notesplit = re.split(r'\\n\\s*?Notes\\n\\s*?-----', inspect.cleandoc(initialdoc))\n ^^^^^^^^^^^^^^^^\nAttributeError: partially initialized module 'inspect' has no attribute 'cleandoc' (most likely due to a circular import)\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in \n from pandas._config import (\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in \n from pandas._config import config\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in \n from pandas.util._exceptions import find_stack_level\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in \n import inspect\n File \"/app/solution/inspect.py\", line 2, in \n from scipy import sparse\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/__init__.py\", line 300, in \n from ._base import *\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 5, in \n from ._sputils import (asmatrix, check_reshape_kwargs, check_shape,\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_sputils.py\", line 10, in \n from scipy._lib._util import np_long, np_ulong\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_util.py\", line 13, in \n from scipy._lib._array_api import array_namespace, is_numpy, xp_size\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_array_api.py\", line 18, in \n from scipy._lib.array_api_compat import (\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy/__init__.py\", line 1, in \n from numpy import * # noqa: F403\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/__init__.py\", line 42, in \n from . import core\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 8299, in \n inner.__doc__ = doc_note(np.inner.__doc__,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 123, in doc_note\n notesplit = re.split(r'\\n\\s*?Notes\\n\\s*?-----', inspect.cleandoc(initialdoc))\n ^^^^^^^^^^^^^^^^\nAttributeError: partially initialized module 'inspect' has no attribute 'cleandoc' (most likely due to a circular import)\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 282190169 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in \n from pandas._config import (\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in \n from pandas._config import config\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in \n from pandas.util._exceptions import find_stack_level\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in \n import inspect\n File \"/app/solution/inspect.py\", line 2, in \n from scipy import sparse\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/__init__.py\", line 300, in \n from ._base import *\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 5, in \n from ._sputils import (asmatrix, check_reshape_kwargs, check_shape,\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_sputils.py\", line 10, in \n from scipy._lib._util import np_long, np_ulong\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_util.py\", line 13, in \n from scipy._lib._array_api import array_namespace, is_numpy, xp_size\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/_array_api.py\", line 18, in \n from scipy._lib.array_api_compat import (\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy/__init__.py\", line 1, in \n from numpy import * # noqa: F403\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/__init__.py\", line 42, in \n from . import core\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 8299, in \n inner.__doc__ = doc_note(np.inner.__doc__,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/ma/core.py\", line 123, in doc_note\n notesplit = re.split(r'\\n\\s*?Notes\\n\\s*?-----', inspect.cleandoc(initialdoc))\n ^^^^^^^^^^^^^^^^\nAttributeError: partially initialized module 'inspect' has no attribute 'cleandoc' (most likely due to a circular import)\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:22:33.684Z", "callId": "call_G55yVeC58ZetFMKKcSULJc0T", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"mv /app/solution/inspect.py /app/solution/inspect_data.py\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect_data.py\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:22:34.870Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"7eaa68\",\"wall_time_seconds\":1.019258054,\"session_id\":14484,\"original_token_count\":15,\"output\":\"eligibility 24657 [24657. 28038.5 29242. 29852. 30000. ]\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:22:33.734Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3621658c087d2890bfcfbc36fdada", "usage": { "input_tokens": 24632, "cached_input_tokens": 22912, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 0, "total_tokens": 24705 }, "turn_token_usage": { "input_tokens": 104776, "cached_input_tokens": 91648, "cache_write_input_tokens": 0, "output_tokens": 1367, "reasoning_output_tokens": 130, "total_tokens": 106143 }, "thread_token_usage": { "input_tokens": 104776, "cached_input_tokens": 91648, "cache_write_input_tokens": 0, "output_tokens": 1367, "reasoning_output_tokens": 130, "total_tokens": 106143 } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-23T05:22:38.098Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:22:38.098Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:22:38.986Z", "callId": "call_s95xP24HhEQ7MTsAwU0KcYBr", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:14484,chars:\"\",yield_time_ms:1000,max_output_tokens:3000}));\n" }, "result": { "timestamp": "2026-09-23T05:22:43.994Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"d4a434\",\"wall_time_seconds\":5.001188319,\"session_id\":14484,\"original_token_count\":66,\"output\":\"unique users/items 9238 845395\\nmapped 31062540 eligible 0.0 positive eligible 0.0\\nval strong eligible 53536 seen 0.0\\neligible histories examples Empty DataFrame\\nColumns: [uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds, u, i]\\nIndex: []\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:22:39.007Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3621b13b887d29f9bd2bae5064ba3", "usage": { "input_tokens": 24794, "cached_input_tokens": 24448, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 51, "total_tokens": 24886 }, "turn_token_usage": { "input_tokens": 129570, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 1459, "reasoning_output_tokens": 181, "total_tokens": 131029 }, "thread_token_usage": { "input_tokens": 129570, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 1459, "reasoning_output_tokens": 181, "total_tokens": 131029 } } }, { "kind": "context", "timestamp": "2026-09-23T05:22:44.420Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0a3d3fdb-c042-4f4b-9ca1-9f576001d4ea", "process_id": "14484", "command": [ "/bin/sh", "-lc", "mv /app/solution/inspect.py /app/solution/inspect_data.py\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect_data.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mv /app/solution/inspect.py /app/solution/inspect_data.py\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect_data.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "eligibility 24657 [24657. 28038.5 29242. 29852. 30000. ]\nunique users/items 9238 845395\nmapped 31062540 eligible 0.0 positive eligible 0.0\nval strong eligible 53536 seen 0.0\neligible histories examples Empty DataFrame\nColumns: [uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds, u, i]\nIndex: []\nvalidation state eligible state\ndisliked 50689\nliked 418435\ndtype: int64 Series([], dtype: int64)\ntest state eligible state\ndisliked 52117\nliked 432659\ndtype: int64 Series([], dtype: int64)\n", "stderr": "", "aggregated_output": "eligibility 24657 [24657. 28038.5 29242. 29852. 30000. ]\nunique users/items 9238 845395\nmapped 31062540 eligible 0.0 positive eligible 0.0\nval strong eligible 53536 seen 0.0\neligible histories examples Empty DataFrame\nColumns: [uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds, u, i]\nIndex: []\nvalidation state eligible state\ndisliked 50689\nliked 418435\ndtype: int64 Series([], dtype: int64)\ntest state eligible state\ndisliked 52117\nliked 432659\ndtype: int64 Series([], dtype: int64)\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 570452768 }, "formatted_output": "eligibility 24657 [24657. 28038.5 29242. 29852. 30000. ]\nunique users/items 9238 845395\nmapped 31062540 eligible 0.0 positive eligible 0.0\nval strong eligible 53536 seen 0.0\neligible histories examples Empty DataFrame\nColumns: [uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds, u, i]\nIndex: []\nvalidation state eligible state\ndisliked 50689\nliked 418435\ndtype: int64 Series([], dtype: int64)\ntest state eligible state\ndisliked 52117\nliked 432659\ndtype: int64 Series([], dtype: int64)\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-23T05:22:53.323Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:22:53.323Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:22:54.943Z", "text": "The candidate sets exclude all previously heard tracks, so replay patterns cannot drive this shelf. I’m focusing on collaborative discovery: tracks that similar listeners enjoy, with recency and organic actions helping weight their histories.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:23:24.043Z", "callId": "call_GjkGALM8YGu9Z2zlvARB1qDC", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:14484,chars:\"\",yield_time_ms:1000,max_output_tokens:2000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/common.py <<'PY'\\nimport os,sys,time,json\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\\nimport numpy as np,pandas as pd\\nfrom scipy import sparse\\nD='/task/data/'; S='/app/solution/'\\na=np.load(D+'validation_eligible_candidates.npz'); U=a['uids']; I=a['item_ids']; shape=(len(U),len(I)); UI=pd.Index(U); II=pd.Index(I)\\ndisc=1/np.log2(np.arange(2,12))\\ndef eligible(stage):\\n a=np.load(D+stage+'_eligible_candidates.npz');assert np.array_equal(a['uids'],U) and np.array_equal(a['item_ids'],I)\\n return np.unpackbits(a['packed_eligible'],axis=1,count=len(I),bitorder='little').astype(bool)\\ndef truth():\\n v=pd.read_parquet(S+'val_mapped.parquet');m=(v.is_organic==1)&(v.played_ratio_pct>=80);u=v.u[m].to_numpy();i=v.i[m].to_numpy()\\n y=np.zeros(shape,np.uint8);y[u,i]=1\\n l=pd.read_parquet(D+'val_likes.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.is_organic==1);y[u[m],i[m]]=7;y*=eligible('validation')\\n idcg=np.sort(y,axis=1)[:,-10:][:,::-1]@disc\\n return y,idcg\\nY=None;IDCG=None\\ndef evaluate(scores,e=None):\\n global Y,IDCG\\n if Y is None:Y,IDCG=truth()\\n if e is None:e=eligible('validation')\\n vals=[]\\n for a in range(0,len(U),500):\\n z=np.array(scores[a:a+500],copy=True);z[~e[a:a+500]]=-np.inf\\n top=np.argpartition(z,-10,axis=1)[:,-10:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\\n g=np.take_along_axis(Y[a:a+500],top,axis=1)@disc;idc=IDCG[a:a+500];vals.extend((g[idc>0]/idc[idc>0]).tolist())\\n return np.mean(vals)\\ndef save_ranking(scores,stage,name):\\n e=eligible(stage); rows=[]\\n for a in range(0,len(U),256):\\n z=np.array(scores[a:a+256],copy=True);z[~e[a:a+256]]=-np.inf\\n top=np.argpartition(z,-100,axis=1)[:,-100:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\\n rows.append(pd.DataFrame({'uid':np.repeat(U[a:a+256],100),'item_id':I[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}))\\n df=pd.concat(rows,ignore_index=True);df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('saved',name,len(df),flush=True)\\ndef history(stage):\\n l=pd.read_parquet(S+'train_mapped.parquet')\\n if stage=='test':l=pd.concat([l,pd.read_parquet(S+'val_mapped.parquet')],ignore_index=True)\\n return l\\nif __name__=='__main__':\\n l=history('test');m=(l.played_ratio_pct>=80)&(l.is_organic==1);w=np.exp(-(l.timestamp.max()-l.timestamp[m].to_numpy(dtype=float))/(86400*21));pop=np.bincount(l.i[m],weights=w,minlength=len(I));save_ranking(np.broadcast_to(pop,shape),'test','submission');print('baseline saved')\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/common.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:23:25.215Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"e614cd\",\"wall_time_seconds\":0.00000247,\"exit_code\":0,\"original_token_count\":53,\"output\":\"validation state eligible state\\ndisliked 50689\\nliked 418435\\ndtype: int64 Series([], dtype: int64)\\ntest state eligible state\\ndisliked 52117\\nliked 432659\\ndtype: int64 Series([], dtype: int64)\\n\"}\n{\"chunk_id\":\"5ec45b\",\"wall_time_seconds\":1.003244935,\"session_id\":69360,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:24.067Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab362246f2487d2b4c5f652f222ef54", "usage": { "input_tokens": 25021, "cached_input_tokens": 24576, "cache_write_input_tokens": 0, "output_tokens": 1278, "reasoning_output_tokens": 250, "total_tokens": 26299 }, "turn_token_usage": { "input_tokens": 154591, "cached_input_tokens": 140672, "cache_write_input_tokens": 0, "output_tokens": 2737, "reasoning_output_tokens": 431, "total_tokens": 157328 }, "thread_token_usage": { "input_tokens": 154591, "cached_input_tokens": 140672, "cache_write_input_tokens": 0, "output_tokens": 2737, "reasoning_output_tokens": 431, "total_tokens": 157328 } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-23T05:23:27.218Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:23:27.218Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:23:28.431Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4964db7d-278a-472b-9200-d0a803d1d941", "process_id": "69360", "command": [ "/bin/sh", "-lc", "cat > /app/solution/common.py <<'PY'\nimport os,sys,time,json\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nD='/task/data/'; S='/app/solution/'\na=np.load(D+'validation_eligible_candidates.npz'); U=a['uids']; I=a['item_ids']; shape=(len(U),len(I)); UI=pd.Index(U); II=pd.Index(I)\ndisc=1/np.log2(np.arange(2,12))\ndef eligible(stage):\n a=np.load(D+stage+'_eligible_candidates.npz');assert np.array_equal(a['uids'],U) and np.array_equal(a['item_ids'],I)\n return np.unpackbits(a['packed_eligible'],axis=1,count=len(I),bitorder='little').astype(bool)\ndef truth():\n v=pd.read_parquet(S+'val_mapped.parquet');m=(v.is_organic==1)&(v.played_ratio_pct>=80);u=v.u[m].to_numpy();i=v.i[m].to_numpy()\n y=np.zeros(shape,np.uint8);y[u,i]=1\n l=pd.read_parquet(D+'val_likes.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.is_organic==1);y[u[m],i[m]]=7;y*=eligible('validation')\n idcg=np.sort(y,axis=1)[:,-10:][:,::-1]@disc\n return y,idcg\nY=None;IDCG=None\ndef evaluate(scores,e=None):\n global Y,IDCG\n if Y is None:Y,IDCG=truth()\n if e is None:e=eligible('validation')\n vals=[]\n for a in range(0,len(U),500):\n z=np.array(scores[a:a+500],copy=True);z[~e[a:a+500]]=-np.inf\n top=np.argpartition(z,-10,axis=1)[:,-10:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n g=np.take_along_axis(Y[a:a+500],top,axis=1)@disc;idc=IDCG[a:a+500];vals.extend((g[idc>0]/idc[idc>0]).tolist())\n return np.mean(vals)\ndef save_ranking(scores,stage,name):\n e=eligible(stage); rows=[]\n for a in range(0,len(U),256):\n z=np.array(scores[a:a+256],copy=True);z[~e[a:a+256]]=-np.inf\n top=np.argpartition(z,-100,axis=1)[:,-100:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n rows.append(pd.DataFrame({'uid':np.repeat(U[a:a+256],100),'item_id':I[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}))\n df=pd.concat(rows,ignore_index=True);df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('saved',name,len(df),flush=True)\ndef history(stage):\n l=pd.read_parquet(S+'train_mapped.parquet')\n if stage=='test':l=pd.concat([l,pd.read_parquet(S+'val_mapped.parquet')],ignore_index=True)\n return l\nif __name__=='__main__':\n l=history('test');m=(l.played_ratio_pct>=80)&(l.is_organic==1);w=np.exp(-(l.timestamp.max()-l.timestamp[m].to_numpy(dtype=float))/(86400*21));pop=np.bincount(l.i[m],weights=w,minlength=len(I));save_ranking(np.broadcast_to(pop,shape),'test','submission');print('baseline saved')\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/common.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/common.py <<'PY'\nimport os,sys,time,json\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nD='/task/data/'; S='/app/solution/'\na=np.load(D+'validation_eligible_candidates.npz'); U=a['uids']; I=a['item_ids']; shape=(len(U),len(I)); UI=pd.Index(U); II=pd.Index(I)\ndisc=1/np.log2(np.arange(2,12))\ndef eligible(stage):\n a=np.load(D+stage+'_eligible_candidates.npz');assert np.array_equal(a['uids'],U) and np.array_equal(a['item_ids'],I)\n return np.unpackbits(a['packed_eligible'],axis=1,count=len(I),bitorder='little').astype(bool)\ndef truth():\n v=pd.read_parquet(S+'val_mapped.parquet');m=(v.is_organic==1)&(v.played_ratio_pct>=80);u=v.u[m].to_numpy();i=v.i[m].to_numpy()\n y=np.zeros(shape,np.uint8);y[u,i]=1\n l=pd.read_parquet(D+'val_likes.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.is_organic==1);y[u[m],i[m]]=7;y*=eligible('validation')\n idcg=np.sort(y,axis=1)[:,-10:][:,::-1]@disc\n return y,idcg\nY=None;IDCG=None\ndef evaluate(scores,e=None):\n global Y,IDCG\n if Y is None:Y,IDCG=truth()\n if e is None:e=eligible('validation')\n vals=[]\n for a in range(0,len(U),500):\n z=np.array(scores[a:a+500],copy=True);z[~e[a:a+500]]=-np.inf\n top=np.argpartition(z,-10,axis=1)[:,-10:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n g=np.take_along_axis(Y[a:a+500],top,axis=1)@disc;idc=IDCG[a:a+500];vals.extend((g[idc>0]/idc[idc>0]).tolist())\n return np.mean(vals)\ndef save_ranking(scores,stage,name):\n e=eligible(stage); rows=[]\n for a in range(0,len(U),256):\n z=np.array(scores[a:a+256],copy=True);z[~e[a:a+256]]=-np.inf\n top=np.argpartition(z,-100,axis=1)[:,-100:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n rows.append(pd.DataFrame({'uid':np.repeat(U[a:a+256],100),'item_id':I[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}))\n df=pd.concat(rows,ignore_index=True);df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('saved',name,len(df),flush=True)\ndef history(stage):\n l=pd.read_parquet(S+'train_mapped.parquet')\n if stage=='test':l=pd.concat([l,pd.read_parquet(S+'val_mapped.parquet')],ignore_index=True)\n return l\nif __name__=='__main__':\n l=history('test');m=(l.played_ratio_pct>=80)&(l.is_organic==1);w=np.exp(-(l.timestamp.max()-l.timestamp[m].to_numpy(dtype=float))/(86400*21));pop=np.bincount(l.i[m],weights=w,minlength=len(I));save_ranking(np.broadcast_to(pop,shape),'test','submission');print('baseline saved')\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/common.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved submission 845600\nbaseline saved\n", "stderr": "", "aggregated_output": "saved submission 845600\nbaseline saved\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 221853774 }, "formatted_output": "saved submission 845600\nbaseline saved\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:23:48.814Z", "callId": "call_vorW4QNA9JnEmnxoRwPQ2XCX", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/fit_ease.py <<'PY'\\nfrom common import *\\nfrom scipy.linalg import cho_factor,cho_solve\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--reg',type=float,default=200);p.add_argument('--days',type=float,default=0);p.add_argument('--power',type=float,default=0);a=p.parse_args();start=time.time()\\nname=f'{a.stage}_{a.mode}_{a.reg:g}_{a.days:g}_{a.power:g}'\\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0)\\nm=l.played_ratio_pct.to_numpy()>=50\\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\\nw=np.ones(len(u),np.float32)\\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\\nif a.mode=='organic':w*=np.where(org,1,.3)\\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org;gc.collect()\\nif a.mode=='binary':X.data[:]=1\\nelif a.mode=='log':X.data=np.log1p(X.data)\\nelse:X.data=np.sqrt(X.data)\\nif a.power:\\n X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf;gc.collect()\\nprint('inverse',time.time()-start,flush=True)\\n# P @ X by sparse-dense multiplication\\nZ=(X.T@P.T).T\\nDg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z;gc.collect()\\nnp.save(S+name+'.npy',scores)\\nprint('finished',time.time()-start,flush=True)\\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True);save_ranking(scores,a.stage,'validation')\\nelse:save_ranking(scores,a.stage,'submission')\\nPY\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode binary --reg 200 > /app/solution/ease.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:69360,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" }, "result": { "timestamp": "2026-09-23T05:23:49.985Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"current_time\":\"2026-09-23 05:23:48 UTC\"}\n{\"chunk_id\":\"7cf7cd\",\"wall_time_seconds\":1.00085529,\"session_id\":68679,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"1449bf\",\"wall_time_seconds\":0.000002569,\"exit_code\":0,\"original_token_count\":10,\"output\":\"saved submission 845600\\nbaseline saved\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:48.853Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3624d78d087d2bd7a9d5d395c0dc6", "usage": { "input_tokens": 26453, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 741, "reasoning_output_tokens": 11, "total_tokens": 27194 }, "turn_token_usage": { "input_tokens": 181044, "cached_input_tokens": 165504, "cache_write_input_tokens": 0, "output_tokens": 3478, "reasoning_output_tokens": 442, "total_tokens": 184522 }, "thread_token_usage": { "input_tokens": 181044, "cached_input_tokens": 165504, "cache_write_input_tokens": 0, "output_tokens": 3478, "reasoning_output_tokens": 442, "total_tokens": 184522 } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-23T05:23:56.461Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:23:56.461Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:23:57.481Z", "callId": "call_eVTd2vDtA9I8ZWP2EVyfrt6K", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ease.log; ls -lh /app/solution\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:23:57.540Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"531b09\",\"wall_time_seconds\":0.00000534,\"exit_code\":0,\"original_token_count\":121,\"output\":\"validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\\ntotal 303M\\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:23 common.py\\n-rw-r--r-- 1 agent agent 65 Sep 23 05:23 ease.log\\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:23 fit_ease.py\\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:22 inspect_data.py\\n-rw-r--r-- 1 agent agent 628K Sep 23 05:23 submission.parquet\\n-rw-r--r-- 1 agent agent 295M Sep 23 05:22 train_mapped.parquet\\n-rw-r--r-- 1 agent agent 7.8M Sep 23 05:22 val_mapped.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:57.521Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-745fe893-2d65-4225-8745-dd4bd775569a", "process_id": "70050", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease.log; ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/ease.log", "name": "ease.log", "path": "/app/solution/ease.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution", "path": "solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ntotal 303M\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:23 common.py\n-rw-r--r-- 1 agent agent 65 Sep 23 05:23 ease.log\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:23 fit_ease.py\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:22 inspect_data.py\n-rw-r--r-- 1 agent agent 628K Sep 23 05:23 submission.parquet\n-rw-r--r-- 1 agent agent 295M Sep 23 05:22 train_mapped.parquet\n-rw-r--r-- 1 agent agent 7.8M Sep 23 05:22 val_mapped.parquet\n", "stderr": "", "aggregated_output": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ntotal 303M\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:23 common.py\n-rw-r--r-- 1 agent agent 65 Sep 23 05:23 ease.log\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:23 fit_ease.py\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:22 inspect_data.py\n-rw-r--r-- 1 agent agent 628K Sep 23 05:23 submission.parquet\n-rw-r--r-- 1 agent agent 295M Sep 23 05:22 train_mapped.parquet\n-rw-r--r-- 1 agent agent 7.8M Sep 23 05:22 val_mapped.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5340 }, "formatted_output": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ntotal 303M\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:23 common.py\n-rw-r--r-- 1 agent agent 65 Sep 23 05:23 ease.log\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:23 fit_ease.py\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:22 inspect_data.py\n-rw-r--r-- 1 agent agent 628K Sep 23 05:23 submission.parquet\n-rw-r--r-- 1 agent agent 295M Sep 23 05:22 train_mapped.parquet\n-rw-r--r-- 1 agent agent 7.8M Sep 23 05:22 val_mapped.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:23:57.538Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36266353c87d298db38ffb7a0bb60", "usage": { "input_tokens": 27311, "cached_input_tokens": 26240, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 121, "total_tokens": 27476 }, "turn_token_usage": { "input_tokens": 208355, "cached_input_tokens": 191744, "cache_write_input_tokens": 0, "output_tokens": 3643, "reasoning_output_tokens": 563, "total_tokens": 211998 }, "thread_token_usage": { "input_tokens": 208355, "cached_input_tokens": 191744, "cache_write_input_tokens": 0, "output_tokens": 3643, "reasoning_output_tokens": 563, "total_tokens": 211998 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-23T05:24:03.654Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:03.654Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:24:18.844Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73302d84-0794-4518-a1a8-2e4fcd68605f", "process_id": "68679", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_ease.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--reg',type=float,default=200);p.add_argument('--days',type=float,default=0);p.add_argument('--power',type=float,default=0);a=p.parse_args();start=time.time()\nname=f'{a.stage}_{a.mode}_{a.reg:g}_{a.days:g}_{a.power:g}'\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0)\nm=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nif a.mode=='organic':w*=np.where(org,1,.3)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org;gc.collect()\nif a.mode=='binary':X.data[:]=1\nelif a.mode=='log':X.data=np.log1p(X.data)\nelse:X.data=np.sqrt(X.data)\nif a.power:\n X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf;gc.collect()\nprint('inverse',time.time()-start,flush=True)\n# P @ X by sparse-dense multiplication\nZ=(X.T@P.T).T\nDg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z;gc.collect()\nnp.save(S+name+'.npy',scores)\nprint('finished',time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True);save_ranking(scores,a.stage,'validation')\nelse:save_ranking(scores,a.stage,'submission')\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode binary --reg 200 > /app/solution/ease.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_ease.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--reg',type=float,default=200);p.add_argument('--days',type=float,default=0);p.add_argument('--power',type=float,default=0);a=p.parse_args();start=time.time()\nname=f'{a.stage}_{a.mode}_{a.reg:g}_{a.days:g}_{a.power:g}'\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0)\nm=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nif a.mode=='organic':w*=np.where(org,1,.3)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org;gc.collect()\nif a.mode=='binary':X.data[:]=1\nelif a.mode=='log':X.data=np.log1p(X.data)\nelse:X.data=np.sqrt(X.data)\nif a.power:\n X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf;gc.collect()\nprint('inverse',time.time()-start,flush=True)\n# P @ X by sparse-dense multiplication\nZ=(X.T@P.T).T\nDg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z;gc.collect()\nnp.save(S+name+'.npy',scores)\nprint('finished',time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True);save_ranking(scores,a.stage,'validation')\nelse:save_ranking(scores,a.stage,'submission')\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode binary --reg 200 > /app/solution/ease.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 29, "nanos": 864020810 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:24:26.096Z", "callId": "call_UowvpCv5b1OTD6cD5zXdd5BV", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fit_knn.py <<'PY'\\nfrom common import *\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.5);p.add_argument('--k',type=int,default=100);p.add_argument('--like',type=float,default=3);a=p.parse_args();start=time.time()\\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0);m=l.played_ratio_pct.to_numpy()>=50\\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\\nw=np.ones(len(u),np.float32)\\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\\nif a.mode=='organic':w*=np.where(org,1,.3)\\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\\nX.data=np.log1p(X.data)\\nl=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');L=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape)\\nX+=L;del l,L;gc.collect()\\npop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)\\nA=X.multiply(pop**(-a.power/2)).tocsr();norm=np.sqrt(np.asarray(A.power(2).sum(axis=1)).ravel()).clip(1);A=A.multiply(1/norm[:,None]).tocsr()\\nprint('matrix',time.time()-start,flush=True)\\nG=(A@A.T).toarray();np.fill_diagonal(G,0)\\nprint('sim',time.time()-start,flush=True)\\nfor k in [a.k]:\\n ix=np.argpartition(G,-k,axis=1)[:,-k:];v=np.take_along_axis(G,ix,axis=1)**3\\n W=sparse.csr_matrix((v.flatten(),(np.repeat(np.arange(len(U)),k),ix.flatten())),shape=(len(U),len(U)))\\n # reduce heavy users and item popularity in neighbor voting\\n B=X.multiply(1/np.sqrt(np.asarray(X.power(2).sum(axis=1)).ravel()).clip(1)[:,None]).tocsr()\\n scores=(W@B).toarray();name=f'{a.stage}_knn_{a.mode}_{a.days:g}_{a.power:g}_{k}_{a.like:g}';np.save(S+name+'.npy',scores)\\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\\n else:save_ranking(scores,a.stage,'submission')\\nprint('done',time.time()-start,flush=True)\\nPY\",\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/ease.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:24:26.173Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"250b30\",\"wall_time_seconds\":0.00000149,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"f6c643\",\"wall_time_seconds\":0.00000637,\"exit_code\":0,\"original_token_count\":50,\"output\":\"validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\\ninverse 11.650678157806396\\nfinished 23.413398504257202\\nSCORE validation_binary_200_0_0 0.019490802932183665\\nsaved validation 845600\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:26.129Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3626dd1b887d2905d5b5a8a45c016", "usage": { "input_tokens": 27740, "cached_input_tokens": 27136, "cache_write_input_tokens": 0, "output_tokens": 851, "reasoning_output_tokens": 93, "total_tokens": 28591 }, "turn_token_usage": { "input_tokens": 236095, "cached_input_tokens": 218880, "cache_write_input_tokens": 0, "output_tokens": 4494, "reasoning_output_tokens": 656, "total_tokens": 240589 }, "thread_token_usage": { "input_tokens": 236095, "cached_input_tokens": 218880, "cache_write_input_tokens": 0, "output_tokens": 4494, "reasoning_output_tokens": 656, "total_tokens": 240589 } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:26.136Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c2d11e66-729d-43a3-a129-e8bbe040d560", "process_id": "91211", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_knn.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.5);p.add_argument('--k',type=int,default=100);p.add_argument('--like',type=float,default=3);a=p.parse_args();start=time.time()\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0);m=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nif a.mode=='organic':w*=np.where(org,1,.3)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\nX.data=np.log1p(X.data)\nl=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');L=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape)\nX+=L;del l,L;gc.collect()\npop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)\nA=X.multiply(pop**(-a.power/2)).tocsr();norm=np.sqrt(np.asarray(A.power(2).sum(axis=1)).ravel()).clip(1);A=A.multiply(1/norm[:,None]).tocsr()\nprint('matrix',time.time()-start,flush=True)\nG=(A@A.T).toarray();np.fill_diagonal(G,0)\nprint('sim',time.time()-start,flush=True)\nfor k in [a.k]:\n ix=np.argpartition(G,-k,axis=1)[:,-k:];v=np.take_along_axis(G,ix,axis=1)**3\n W=sparse.csr_matrix((v.flatten(),(np.repeat(np.arange(len(U)),k),ix.flatten())),shape=(len(U),len(U)))\n # reduce heavy users and item popularity in neighbor voting\n B=X.multiply(1/np.sqrt(np.asarray(X.power(2).sum(axis=1)).ravel()).clip(1)[:,None]).tocsr()\n scores=(W@B).toarray();name=f'{a.stage}_knn_{a.mode}_{a.days:g}_{a.power:g}_{k}_{a.like:g}';np.save(S+name+'.npy',scores)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n else:save_ranking(scores,a.stage,'submission')\nprint('done',time.time()-start,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_knn.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--mode',default='binary');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.5);p.add_argument('--k',type=int,default=100);p.add_argument('--like',type=float,default=3);a=p.parse_args();start=time.time()\nl=history(a.stage);cut=24786800+(606600 if a.stage=='test' else 0);m=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nif a.mode=='organic':w*=np.where(org,1,.3)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\nX.data=np.log1p(X.data)\nl=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');L=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape)\nX+=L;del l,L;gc.collect()\npop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)\nA=X.multiply(pop**(-a.power/2)).tocsr();norm=np.sqrt(np.asarray(A.power(2).sum(axis=1)).ravel()).clip(1);A=A.multiply(1/norm[:,None]).tocsr()\nprint('matrix',time.time()-start,flush=True)\nG=(A@A.T).toarray();np.fill_diagonal(G,0)\nprint('sim',time.time()-start,flush=True)\nfor k in [a.k]:\n ix=np.argpartition(G,-k,axis=1)[:,-k:];v=np.take_along_axis(G,ix,axis=1)**3\n W=sparse.csr_matrix((v.flatten(),(np.repeat(np.arange(len(U)),k),ix.flatten())),shape=(len(U),len(U)))\n # reduce heavy users and item popularity in neighbor voting\n B=X.multiply(1/np.sqrt(np.asarray(X.power(2).sum(axis=1)).ravel()).clip(1)[:,None]).tocsr()\n scores=(W@B).toarray();name=f'{a.stage}_knn_{a.mode}_{a.days:g}_{a.power:g}_{k}_{a.like:g}';np.save(S+name+'.npy',scores)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n else:save_ranking(scores,a.stage,'submission')\nprint('done',time.time()-start,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 1490 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:26.168Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6c73f91f-2808-4c1f-b182-ff091256ceaa", "process_id": "58902", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/ease.log", "name": "ease.log", "path": "/app/solution/ease.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ninverse 11.650678157806396\nfinished 23.413398504257202\nSCORE validation_binary_200_0_0 0.019490802932183665\nsaved validation 845600\n", "stderr": "", "aggregated_output": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ninverse 11.650678157806396\nfinished 23.413398504257202\nSCORE validation_binary_200_0_0 0.019490802932183665\nsaved validation 845600\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6370 }, "formatted_output": "validation_binary_200_0_0 matrix 5514505 time 2.0408711433410645\ninverse 11.650678157806396\nfinished 23.413398504257202\nSCORE validation_binary_200_0_0 0.019490802932183665\nsaved validation 845600\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-23T05:24:30.857Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:30.857Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:24:32.565Z", "text": "A valid submission is saved. The first collaborative model scores 0.01949 NDCG@10 on validation. I’m testing time decay, explicit likes, and similarity weighting to see which best predicts new organic discoveries.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:24:38.527Z", "callId": "call_8ejdkiyGdxh8Zl1jZovAQ8Nk", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode binary --reg 200 > /app/solution/ease_test.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py > /app/solution/knn.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode log --days 60 --power .25 --reg 20 > /app/solution/ease_log.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:24:39.694Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"960dd7\",\"wall_time_seconds\":1.001411985,\"session_id\":19208,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:38.584Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3628a62e487d2adf46acbaf91519d", "usage": { "input_tokens": 28750, "cached_input_tokens": 27520, "cache_write_input_tokens": 0, "output_tokens": 314, "reasoning_output_tokens": 71, "total_tokens": 29064 }, "turn_token_usage": { "input_tokens": 264845, "cached_input_tokens": 246400, "cache_write_input_tokens": 0, "output_tokens": 4808, "reasoning_output_tokens": 727, "total_tokens": 269653 }, "thread_token_usage": { "input_tokens": 264845, "cached_input_tokens": 246400, "cache_write_input_tokens": 0, "output_tokens": 4808, "reasoning_output_tokens": 727, "total_tokens": 269653 } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-23T05:24:51.664Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:51.664Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:24:52.969Z", "callId": "call_SCwFeB8M0ejo0craOVJwCqLy", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log; df -h /app/solution\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:24:53.015Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"04d69f\",\"wall_time_seconds\":0.000003689,\"exit_code\":0,\"original_token_count\":72,\"output\":\"test_binary_200_0_0 matrix 5669201 time 2.294111967086792\\ninverse 12.211700916290283\\ncat: /app/solution/knn.log: No such file or directory\\ncat: /app/solution/ease_log.log: No such file or directory\\nFilesystem Size Used Avail Use% Mounted on\\noverlay 96G 12G 85G 12% /\\n\"}\n{\"current_time\":\"2026-09-23 05:24:53 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:53.006Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36298000887d2a2622525e6be59cb", "usage": { "input_tokens": 29121, "cached_input_tokens": 28544, "cache_write_input_tokens": 0, "output_tokens": 398, "reasoning_output_tokens": 328, "total_tokens": 29519 }, "turn_token_usage": { "input_tokens": 293966, "cached_input_tokens": 274944, "cache_write_input_tokens": 0, "output_tokens": 5206, "reasoning_output_tokens": 1055, "total_tokens": 299172 }, "thread_token_usage": { "input_tokens": 293966, "cached_input_tokens": 274944, "cache_write_input_tokens": 0, "output_tokens": 5206, "reasoning_output_tokens": 1055, "total_tokens": 299172 } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:53.008Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bee0265e-adb2-46db-8be4-582d6700177a", "process_id": "2653", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log; df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log; df -h /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 12G 85G 12% /\n", "stderr": "", "aggregated_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 12G 85G 12% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3689 }, "formatted_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 12G 85G 12% /\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-23T05:25:04.581Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:04.581Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:05.550Z", "callId": "call_K1FN3ESKnO0ffENaQOT9xC1Y", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:25:05.592Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"aef46d\",\"wall_time_seconds\":0.000005781,\"exit_code\":1,\"original_token_count\":57,\"output\":\"test_binary_200_0_0 matrix 5669201 time 2.294111967086792\\ninverse 12.211700916290283\\nfinished 24.584764003753662\\ncat: /app/solution/knn.log: No such file or directory\\ncat: /app/solution/ease_log.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:05.587Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d6c13dfd-a762-467e-a892-8605963794a6", "process_id": "79670", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\n", "stderr": "", "aggregated_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5781 }, "formatted_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\ncat: /app/solution/knn.log: No such file or directory\ncat: /app/solution/ease_log.log: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:05.590Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab362a5723087d2b93f37b3edb5d231", "usage": { "input_tokens": 29692, "cached_input_tokens": 28928, "cache_write_input_tokens": 0, "output_tokens": 346, "reasoning_output_tokens": 293, "total_tokens": 30038 }, "turn_token_usage": { "input_tokens": 323658, "cached_input_tokens": 303872, "cache_write_input_tokens": 0, "output_tokens": 5552, "reasoning_output_tokens": 1348, "total_tokens": 329210 }, "thread_token_usage": { "input_tokens": 323658, "cached_input_tokens": 303872, "cache_write_input_tokens": 0, "output_tokens": 5552, "reasoning_output_tokens": 1348, "total_tokens": 329210 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-23T05:25:08.468Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:08.468Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:35.108Z", "callId": "call_FjCEgFEOBpGDpA5z5bPGLpV0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fit_models.py <<'PY'\\nfrom common import *\\nfrom scipy.linalg import cho_factor,cho_solve\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.25);p.add_argument('--like',type=float,default=2);p.add_argument('--organic',type=float,default=.5);p.add_argument('--reg',type=float,default=20);p.add_argument('--userpower',type=float,default=0);p.add_argument('--label',default='weighted');a=p.parse_args();start=time.time()\\nname=f'{a.stage}_{a.label}'\\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\\nl=history('test' if a.stage=='test' else 'validation');l=l[l.timestamp<=cut];m=l.played_ratio_pct.to_numpy()>=50\\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\\nw=np.ones(len(u),np.float32)\\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\\nw*=np.where(org,1,a.organic)\\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\\nX.data=np.log1p(X.data)\\n# Explicit positive history, with reversals reflected by cutoff state where provided.\\nif a.like:\\n if a.stage!='past':\\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');l=l[l.state=='liked']\\n else:\\n l=pd.read_parquet(D+'train_likes.parquet');l=l[l.timestamp<=cut]\\n r=pd.read_parquet(D+'train_unlikes.parquet');r=r[r.timestamp<=cut];l['active']=1;r['active']=0\\n l=pd.concat([l,r]).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');l=l[l.active==1]\\n u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)\\n X+=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape);del l\\nif a.power:X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\\nif a.userpower:X=X.multiply(np.asarray(X.power(2).sum(axis=1)).flatten().clip(1)[:,None]**(-a.userpower)).tocsr()\\nsparse.save_npz(S+name+'_X.npz',X)\\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\\nZ=(X.T@P.T).T;Dg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z\\nnp.save(S+name+'.npy',scores)\\nprint('finished',time.time()-start,flush=True)\\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\\nPY\",\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:25:35.219Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"86ba1b\",\"wall_time_seconds\":0.00000717,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"eae20c\",\"wall_time_seconds\":0.00000315,\"exit_code\":0,\"original_token_count\":84,\"output\":\"test_binary_200_0_0 matrix 5669201 time 2.294111967086792\\ninverse 12.211700916290283\\nfinished 24.584764003753662\\nsaved submission 845600\\nmatrix 2.3300235271453857\\nsim 8.31772756576538\\nSCORE validation_knn_binary_60_0.5_100_3 0.023715627354572496\\ndone 17.757736682891846\\nvalidation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:35.169Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f95c5e6b-387d-4909-af75-8759c9023bb8", "process_id": "38832", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_models.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.25);p.add_argument('--like',type=float,default=2);p.add_argument('--organic',type=float,default=.5);p.add_argument('--reg',type=float,default=20);p.add_argument('--userpower',type=float,default=0);p.add_argument('--label',default='weighted');a=p.parse_args();start=time.time()\nname=f'{a.stage}_{a.label}'\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\nl=history('test' if a.stage=='test' else 'validation');l=l[l.timestamp<=cut];m=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nw*=np.where(org,1,a.organic)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\nX.data=np.log1p(X.data)\n# Explicit positive history, with reversals reflected by cutoff state where provided.\nif a.like:\n if a.stage!='past':\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');l=l[l.state=='liked']\n else:\n l=pd.read_parquet(D+'train_likes.parquet');l=l[l.timestamp<=cut]\n r=pd.read_parquet(D+'train_unlikes.parquet');r=r[r.timestamp<=cut];l['active']=1;r['active']=0\n l=pd.concat([l,r]).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');l=l[l.active==1]\n u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)\n X+=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape);del l\nif a.power:X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\nif a.userpower:X=X.multiply(np.asarray(X.power(2).sum(axis=1)).flatten().clip(1)[:,None]**(-a.userpower)).tocsr()\nsparse.save_npz(S+name+'_X.npz',X)\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\nZ=(X.T@P.T).T;Dg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z\nnp.save(S+name+'.npy',scores)\nprint('finished',time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_models.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--days',type=float,default=60);p.add_argument('--power',type=float,default=.25);p.add_argument('--like',type=float,default=2);p.add_argument('--organic',type=float,default=.5);p.add_argument('--reg',type=float,default=20);p.add_argument('--userpower',type=float,default=0);p.add_argument('--label',default='weighted');a=p.parse_args();start=time.time()\nname=f'{a.stage}_{a.label}'\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\nl=history('test' if a.stage=='test' else 'validation');l=l[l.timestamp<=cut];m=l.played_ratio_pct.to_numpy()>=50\nu=l.u.to_numpy()[m];i=l.i.to_numpy()[m];t=l.timestamp.to_numpy()[m];org=l.is_organic.to_numpy()[m];del l;gc.collect()\nw=np.ones(len(u),np.float32)\nif a.days:w*=np.exp(-(cut-t.astype(np.float32))/(86400*a.days))\nw*=np.where(org,1,a.organic)\nX=sparse.csr_matrix((w,(u,i)),shape=shape,dtype=np.float32);del w,u,i,t,org\nX.data=np.log1p(X.data)\n# Explicit positive history, with reversals reflected by cutoff state where provided.\nif a.like:\n if a.stage!='past':\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');l=l[l.state=='liked']\n else:\n l=pd.read_parquet(D+'train_likes.parquet');l=l[l.timestamp<=cut]\n r=pd.read_parquet(D+'train_unlikes.parquet');r=r[r.timestamp<=cut];l['active']=1;r['active']=0\n l=pd.concat([l,r]).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');l=l[l.active==1]\n u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)\n X+=sparse.csr_matrix((np.full(m.sum(),a.like,np.float32),(u[m],i[m])),shape=shape);del l\nif a.power:X=X.multiply(np.asarray(X.power(2).sum(axis=0)).flatten().clip(1)**(-a.power)).tocsr()\nif a.userpower:X=X.multiply(np.asarray(X.power(2).sum(axis=1)).flatten().clip(1)[:,None]**(-a.userpower)).tocsr()\nsparse.save_npz(S+name+'_X.npz',X)\nprint(name,'matrix',X.nnz,'time',time.time()-start,flush=True)\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg\ncf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\nZ=(X.T@P.T).T;Dg=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P\nscores=-Z/np.maximum(Dg,1e-6)[None,:];del Z\nnp.save(S+name+'.npy',scores)\nprint('finished',time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 7170 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:35.176Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab362b1e4a487d2ab392d9f6394aa3f", "usage": { "input_tokens": 30171, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 942, "reasoning_output_tokens": 45, "total_tokens": 31113 }, "turn_token_usage": { "input_tokens": 353829, "cached_input_tokens": 333440, "cache_write_input_tokens": 0, "output_tokens": 6494, "reasoning_output_tokens": 1393, "total_tokens": 360323 }, "thread_token_usage": { "input_tokens": 353829, "cached_input_tokens": 333440, "cache_write_input_tokens": 0, "output_tokens": 6494, "reasoning_output_tokens": 1393, "total_tokens": 360323 } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:35.213Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-de59183e-52b0-4ff6-b0c1-47e80bbbc492", "process_id": "1857", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ease_test.log /app/solution/knn.log /app/solution/ease_log.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\nsaved submission 845600\nmatrix 2.3300235271453857\nsim 8.31772756576538\nSCORE validation_knn_binary_60_0.5_100_3 0.023715627354572496\ndone 17.757736682891846\nvalidation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\n", "stderr": "", "aggregated_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\nsaved submission 845600\nmatrix 2.3300235271453857\nsim 8.31772756576538\nSCORE validation_knn_binary_60_0.5_100_3 0.023715627354572496\ndone 17.757736682891846\nvalidation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3150 }, "formatted_output": "test_binary_200_0_0 matrix 5669201 time 2.294111967086792\ninverse 12.211700916290283\nfinished 24.584764003753662\nsaved submission 845600\nmatrix 2.3300235271453857\nsim 8.31772756576538\nSCORE validation_knn_binary_60_0.5_100_3 0.023715627354572496\ndone 17.757736682891846\nvalidation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\n" } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-23T05:25:37.858Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:37.858Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:45.298Z", "callId": "call_PO9NzS5HI6ZDkSxFCXiSQ7dc", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w60 --reg 20 > /app/solution/w60.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w14 --days 14 --reg 10 > /app/solution/w14.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w180 --days 180 --reg 50 > /app/solution/w180.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py --days 14 --like 2 --k 200 > /app/solution/knn14.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:25:46.484Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"507faf\",\"wall_time_seconds\":1.000313125,\"session_id\":80793,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:25:46 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:45.385Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab362cf624887d297a48243da34217b", "usage": { "input_tokens": 31336, "cached_input_tokens": 29952, "cache_write_input_tokens": 0, "output_tokens": 280, "reasoning_output_tokens": 22, "total_tokens": 31616 }, "turn_token_usage": { "input_tokens": 385165, "cached_input_tokens": 363392, "cache_write_input_tokens": 0, "output_tokens": 6774, "reasoning_output_tokens": 1415, "total_tokens": 391939 }, "thread_token_usage": { "input_tokens": 385165, "cached_input_tokens": 363392, "cache_write_input_tokens": 0, "output_tokens": 6774, "reasoning_output_tokens": 1415, "total_tokens": 391939 } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:55.741Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4c33386-85c4-419c-9426-bdac26e643b7", "process_id": "19208", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode binary --reg 200 > /app/solution/ease_test.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py > /app/solution/knn.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode log --days 60 --power .25 --reg 20 > /app/solution/ease_log.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode binary --reg 200 > /app/solution/ease_test.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py > /app/solution/knn.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --mode log --days 60 --power .25 --reg 20 > /app/solution/ease_log.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 77, "nanos": 50115820 }, "formatted_output": "" } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-23T05:26:00.557Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:26:00.557Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:26:02.129Z", "text": "Weighting similar listeners by recent activity and explicit likes raised validation NDCG@10 to 0.02372. I’m comparing that with a regularized model and direct track-to-track similarity; they may capture different kinds of discovery.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:26:24.429Z", "callId": "call_xOWimJFYMo3n2rvjlmCI8MCY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fit_item.py <<'PY'\\nfrom common import *\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--power',type=float,default=.7);p.add_argument('--k',type=int,default=300);p.add_argument('--days',type=float,default=60);p.add_argument('--label',default='item');a=p.parse_args();start=time.time()\\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');l=l[(l.timestamp<=cut)&(l.played_ratio_pct>=50)]\\nu=l.u.to_numpy();i=l.i.to_numpy();w=np.exp(-(cut-l.timestamp.to_numpy(dtype=np.float32))/(86400*a.days));w*=np.where(l.is_organic,1,.5)\\nR=sparse.csr_matrix((w,(u,i)),shape=shape);R.data=np.log1p(R.data)\\nX=sparse.csr_matrix((np.ones(len(u),np.float32),(u,i)),shape=shape);X.data[:]=1;del l,u,i,w;gc.collect()\\n# Limit influence of broad listening histories.\\ndeg=np.asarray(X.sum(axis=1)).ravel();X=X.multiply(deg.clip(1)[:,None]**(-.35)).tocsc()\\npop=np.asarray(X.power(2).sum(axis=0)).ravel();simrows=[]\\nfor st in range(0,len(I),500):\\n G=(X[:,st:st+500].T@X).toarray()\\n G/=np.maximum(pop[st:st+500,None]*pop[None,:],1)**a.power\\n G[np.arange(len(G)),np.arange(st,st+len(G))]=0\\n ix=np.argpartition(G,-a.k,axis=1)[:,-a.k:];v=np.take_along_axis(G,ix,axis=1)\\n simrows.append(sparse.csr_matrix((v.ravel(),(np.repeat(np.arange(len(G)),a.k),ix.ravel())),shape=(len(G),len(I))))\\nB=sparse.vstack(simrows).tocsr();sparse.save_npz(S+a.stage+'_'+a.label+'_B.npz',B)\\nprint('similarity',time.time()-start,flush=True)\\n# use same known positive preference state as the other models\\nif a.stage!='past':\\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');R+=sparse.csr_matrix((np.full(m.sum(),2,np.float32),(u[m],i[m])),shape=shape)\\nscores=(R@B).toarray();name=a.stage+'_'+a.label;np.save(S+name+'.npy',scores)\\nprint('done',name,time.time()-start,flush=True)\\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_item.py > /app/solution/item.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/ease_log.log /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:26:25.653Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a7a4ab\",\"wall_time_seconds\":1.001949212,\"session_id\":26024,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"375da5\",\"wall_time_seconds\":0.00000456,\"exit_code\":1,\"original_token_count\":121,\"output\":\"validation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\\ninverse 11.889522552490234\\nfinished 24.37345600128174\\nSCORE validation_log_20_60_0.25 0.028915257427552434\\nsaved validation 845600\\nvalidation_w60 matrix 5558048 time 5.956551790237427\\nfinished 28.237836837768555\\nSCORE validation_w60 0.027506390295302774\\nvalidation_w14 matrix 5558048 time 5.1672961711883545\\ncat: /app/solution/w180.log: No such file or directory\\ncat: /app/solution/knn14.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:26:24.469Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab362daaa6887d2824193549ed66a65", "usage": { "input_tokens": 31690, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 1195, "reasoning_output_tokens": 321, "total_tokens": 32885 }, "turn_token_usage": { "input_tokens": 416855, "cached_input_tokens": 394496, "cache_write_input_tokens": 0, "output_tokens": 7969, "reasoning_output_tokens": 1736, "total_tokens": 424824 }, "thread_token_usage": { "input_tokens": 416855, "cached_input_tokens": 394496, "cache_write_input_tokens": 0, "output_tokens": 7969, "reasoning_output_tokens": 1736, "total_tokens": 424824 } } }, { "kind": "context", "timestamp": "2026-09-23T05:26:25.642Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-75e328dd-f503-4f47-9b06-b5e646b02942", "process_id": "73421", "command": [ "/bin/sh", "-lc", "cat /app/solution/ease_log.log /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ease_log.log /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "validation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\ninverse 11.889522552490234\nfinished 24.37345600128174\nSCORE validation_log_20_60_0.25 0.028915257427552434\nsaved validation 845600\nvalidation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n", "stderr": "", "aggregated_output": "validation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\ninverse 11.889522552490234\nfinished 24.37345600128174\nSCORE validation_log_20_60_0.25 0.028915257427552434\nsaved validation 845600\nvalidation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4560 }, "formatted_output": "validation_log_20_60_0.25 matrix 5514505 time 2.2054388523101807\ninverse 11.889522552490234\nfinished 24.37345600128174\nSCORE validation_log_20_60_0.25 0.028915257427552434\nsaved validation 845600\nvalidation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n" } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-23T05:26:43.964Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:26:43.964Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:26:45.909Z", "callId": "call_NS5qKPFfbMlFmayo1m47aP8S", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:26:46.003Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2a7e79\",\"wall_time_seconds\":0.000006029,\"exit_code\":1,\"original_token_count\":72,\"output\":\"validation_w60 matrix 5558048 time 5.956551790237427\\nfinished 28.237836837768555\\nSCORE validation_w60 0.027506390295302774\\nvalidation_w14 matrix 5558048 time 5.1672961711883545\\ncat: /app/solution/w180.log: No such file or directory\\ncat: /app/solution/knn14.log: No such file or directory\\n\"}\n{\"current_time\":\"2026-09-23 05:26:45 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:26:45.954Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3c9d3cc-e3bd-4bd2-82c9-15c2b6412a4d", "process_id": "70532", "command": [ "/bin/sh", "-lc", "cat /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/w60.log /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "validation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n", "stderr": "", "aggregated_output": "validation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 6029 }, "formatted_output": "validation_w60 matrix 5558048 time 5.956551790237427\nfinished 28.237836837768555\nSCORE validation_w60 0.027506390295302774\nvalidation_w14 matrix 5558048 time 5.1672961711883545\ncat: /app/solution/w180.log: No such file or directory\ncat: /app/solution/knn14.log: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:26:46.002Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36301e40087d2bda4226aa1bac00f", "usage": { "input_tokens": 33146, "cached_input_tokens": 31488, "cache_write_input_tokens": 0, "output_tokens": 590, "reasoning_output_tokens": 516, "total_tokens": 33736 }, "turn_token_usage": { "input_tokens": 450001, "cached_input_tokens": 425984, "cache_write_input_tokens": 0, "output_tokens": 8559, "reasoning_output_tokens": 2252, "total_tokens": 458560 }, "thread_token_usage": { "input_tokens": 450001, "cached_input_tokens": 425984, "cache_write_input_tokens": 0, "output_tokens": 8559, "reasoning_output_tokens": 2252, "total_tokens": 458560 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-23T05:26:48.373Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:26:48.373Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:26:49.826Z", "callId": "call_0usvwEpZ7SyErgvYugGCqMkA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current; ps -eo pid,etime,rss,pcpu,args | rg 'python|fit_'\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:26:50.002Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"280d6a\",\"wall_time_seconds\":0.00000563,\"exit_code\":1,\"original_token_count\":10,\"output\":\"7533068288\\n/bin/sh: 1: ps: not found\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:26:49.912Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36316464487d2b5b4124562060571", "usage": { "input_tokens": 33907, "cached_input_tokens": 33024, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 24, "total_tokens": 33988 }, "turn_token_usage": { "input_tokens": 483908, "cached_input_tokens": 459008, "cache_write_input_tokens": 0, "output_tokens": 8640, "reasoning_output_tokens": 2276, "total_tokens": 492548 }, "thread_token_usage": { "input_tokens": 483908, "cached_input_tokens": 459008, "cache_write_input_tokens": 0, "output_tokens": 8640, "reasoning_output_tokens": 2276, "total_tokens": 492548 } } }, { "kind": "context", "timestamp": "2026-09-23T05:26:49.953Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73cc46ac-46e9-4717-afd5-4f12329f3945", "process_id": "68648", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current; ps -eo pid,etime,rss,pcpu,args | rg 'python|fit_'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.current; ps -eo pid,etime,rss,pcpu,args | rg 'python|fit_'" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "7533068288\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "7533068288\n/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5630 }, "formatted_output": "7533068288\n/bin/sh: 1: ps: not found\n" } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-23T05:27:03.767Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:03.767Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:26.365Z", "callId": "call_xsopaad30bvOwUMdJMdJIoAj", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune.py <<'PY'\\nfrom common import *\\nimport argparse\\np=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--out',default='blend');a=p.parse_args()\\nmodels=[np.load(S+'validation_'+n+'.npy',mmap_mode='r') for n in a.models];E=eligible('validation');Y,ID=truth();active=ID>0\\n# Cache union of top 150 model candidates, their scores, and within-user maxima.\\ncs=[];fs=[];gs=[];us=[]\\nfor st in range(0,len(U),128):\\n es=E[st:st+128]; b=len(es); ix=[];zs=[]\\n for mm in models:\\n z=np.array(mm[st:st+b]);z[~es]=-1e10;ix.append(np.argpartition(z,-150,axis=1)[:,-150:]);zs.append(z)\\n ix=np.concatenate(ix,axis=1);ix=np.sort(ix,axis=1); keep=np.ones_like(ix,dtype=bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1]\\n uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep];us.append(uu+st);cs.append(ii);gs.append(Y[uu+st,ii])\\n feat=[]\\n for z in zs:\\n mx=np.max(z,axis=1).clip(1e-8);feat.append(z[uu,ii]/mx[uu])\\n fs.append(np.stack(feat,axis=1))\\nu=np.concatenate(us);i=np.concatenate(cs);F=np.concatenate(fs);g=np.concatenate(gs);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)]\\nnp.savez(S+'blend_candidates.npz',u=u,i=i,F=F,g=g,ID=ID,models=a.models)\\ndef ev(s):\\n vals=[]\\n for k in range(len(U)):\\n if ID[k]<=0:continue\\n b,e=starts[k:k+2];ix=np.argpartition(s[b:e],-10)[-10:];ix=ix[np.argsort(-s[b:e][ix])];vals.append(g[b:e][ix]@disc/ID[k])\\n return np.mean(vals)\\nbest=(0,None)\\nfor j in range(len(models)):\\n w=np.eye(len(models))[j];v=ev(F@w);print(a.models[j],v,flush=True)\\n if v>best[0]:best=(v,w)\\nfor t in range(3):\\n for j in range(len(models)):\\n for weight in [.1,.25,.5,1,2]:\\n w=best[1].copy();w[j]+=weight;w/=w.sum();v=ev(F@w)\\n if v>best[0]:best=(v,w);print('best',v,w,flush=True)\\nprint('FINAL',best,flush=True);json.dump({'models':a.models,'weights':best[1].tolist(),'score':best[0]},open(S+a.out+'.json','w'))\\nPY\\ncat /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:26.821Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"21503b\",\"wall_time_seconds\":0.000004529,\"exit_code\":1,\"original_token_count\":66,\"output\":\"validation_w14 matrix 5558048 time 5.1672961711883545\\nfinished 30.31103229522705\\nSCORE validation_w14 0.024823066012296065\\nvalidation_w180 matrix 5558048 time 5.493313312530518\\ncat: /app/solution/knn14.log: No such file or directory\\nsimilarity 52.86244773864746\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:26.698Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3631a457887d283f5c1f5307e88d4", "usage": { "input_tokens": 34060, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 1148, "reasoning_output_tokens": 385, "total_tokens": 35208 }, "turn_token_usage": { "input_tokens": 517968, "cached_input_tokens": 492800, "cache_write_input_tokens": 0, "output_tokens": 9788, "reasoning_output_tokens": 2661, "total_tokens": 527756 }, "thread_token_usage": { "input_tokens": 517968, "cached_input_tokens": 492800, "cache_write_input_tokens": 0, "output_tokens": 9788, "reasoning_output_tokens": 2661, "total_tokens": 527756 } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:26.787Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b22ce40-d31e-4933-9628-63237924cacb", "process_id": "71369", "command": [ "/bin/sh", "-lc", "cat > /app/solution/tune.py <<'PY'\nfrom common import *\nimport argparse\np=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--out',default='blend');a=p.parse_args()\nmodels=[np.load(S+'validation_'+n+'.npy',mmap_mode='r') for n in a.models];E=eligible('validation');Y,ID=truth();active=ID>0\n# Cache union of top 150 model candidates, their scores, and within-user maxima.\ncs=[];fs=[];gs=[];us=[]\nfor st in range(0,len(U),128):\n es=E[st:st+128]; b=len(es); ix=[];zs=[]\n for mm in models:\n z=np.array(mm[st:st+b]);z[~es]=-1e10;ix.append(np.argpartition(z,-150,axis=1)[:,-150:]);zs.append(z)\n ix=np.concatenate(ix,axis=1);ix=np.sort(ix,axis=1); keep=np.ones_like(ix,dtype=bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1]\n uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep];us.append(uu+st);cs.append(ii);gs.append(Y[uu+st,ii])\n feat=[]\n for z in zs:\n mx=np.max(z,axis=1).clip(1e-8);feat.append(z[uu,ii]/mx[uu])\n fs.append(np.stack(feat,axis=1))\nu=np.concatenate(us);i=np.concatenate(cs);F=np.concatenate(fs);g=np.concatenate(gs);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)]\nnp.savez(S+'blend_candidates.npz',u=u,i=i,F=F,g=g,ID=ID,models=a.models)\ndef ev(s):\n vals=[]\n for k in range(len(U)):\n if ID[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(s[b:e],-10)[-10:];ix=ix[np.argsort(-s[b:e][ix])];vals.append(g[b:e][ix]@disc/ID[k])\n return np.mean(vals)\nbest=(0,None)\nfor j in range(len(models)):\n w=np.eye(len(models))[j];v=ev(F@w);print(a.models[j],v,flush=True)\n if v>best[0]:best=(v,w)\nfor t in range(3):\n for j in range(len(models)):\n for weight in [.1,.25,.5,1,2]:\n w=best[1].copy();w[j]+=weight;w/=w.sum();v=ev(F@w)\n if v>best[0]:best=(v,w);print('best',v,w,flush=True)\nprint('FINAL',best,flush=True);json.dump({'models':a.models,'weights':best[1].tolist(),'score':best[0]},open(S+a.out+'.json','w'))\nPY\ncat /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/tune.py <<'PY'\nfrom common import *\nimport argparse\np=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--out',default='blend');a=p.parse_args()\nmodels=[np.load(S+'validation_'+n+'.npy',mmap_mode='r') for n in a.models];E=eligible('validation');Y,ID=truth();active=ID>0\n# Cache union of top 150 model candidates, their scores, and within-user maxima.\ncs=[];fs=[];gs=[];us=[]\nfor st in range(0,len(U),128):\n es=E[st:st+128]; b=len(es); ix=[];zs=[]\n for mm in models:\n z=np.array(mm[st:st+b]);z[~es]=-1e10;ix.append(np.argpartition(z,-150,axis=1)[:,-150:]);zs.append(z)\n ix=np.concatenate(ix,axis=1);ix=np.sort(ix,axis=1); keep=np.ones_like(ix,dtype=bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1]\n uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep];us.append(uu+st);cs.append(ii);gs.append(Y[uu+st,ii])\n feat=[]\n for z in zs:\n mx=np.max(z,axis=1).clip(1e-8);feat.append(z[uu,ii]/mx[uu])\n fs.append(np.stack(feat,axis=1))\nu=np.concatenate(us);i=np.concatenate(cs);F=np.concatenate(fs);g=np.concatenate(gs);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)]\nnp.savez(S+'blend_candidates.npz',u=u,i=i,F=F,g=g,ID=ID,models=a.models)\ndef ev(s):\n vals=[]\n for k in range(len(U)):\n if ID[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(s[b:e],-10)[-10:];ix=ix[np.argsort(-s[b:e][ix])];vals.append(g[b:e][ix]@disc/ID[k])\n return np.mean(vals)\nbest=(0,None)\nfor j in range(len(models)):\n w=np.eye(len(models))[j];v=ev(F@w);print(a.models[j],v,flush=True)\n if v>best[0]:best=(v,w)\nfor t in range(3):\n for j in range(len(models)):\n for weight in [.1,.25,.5,1,2]:\n w=best[1].copy();w[j]+=weight;w/=w.sum();v=ev(F@w)\n if v>best[0]:best=(v,w);print('best',v,w,flush=True)\nprint('FINAL',best,flush=True);json.dump({'models':a.models,'weights':best[1].tolist(),'score':best[0]},open(S+a.out+'.json','w'))\nPY\ncat /app/solution/w14.log /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "validation_w14 matrix 5558048 time 5.1672961711883545\nfinished 30.31103229522705\nSCORE validation_w14 0.024823066012296065\nvalidation_w180 matrix 5558048 time 5.493313312530518\ncat: /app/solution/knn14.log: No such file or directory\nsimilarity 52.86244773864746\n", "stderr": "", "aggregated_output": "validation_w14 matrix 5558048 time 5.1672961711883545\nfinished 30.31103229522705\nSCORE validation_w14 0.024823066012296065\nvalidation_w180 matrix 5558048 time 5.493313312530518\ncat: /app/solution/knn14.log: No such file or directory\nsimilarity 52.86244773864746\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4529 }, "formatted_output": "validation_w14 matrix 5558048 time 5.1672961711883545\nfinished 30.31103229522705\nSCORE validation_w14 0.024823066012296065\nvalidation_w180 matrix 5558048 time 5.493313312530518\ncat: /app/solution/knn14.log: No such file or directory\nsimilarity 52.86244773864746\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-23T05:27:30.896Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:30.896Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:27:37.644Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9c216f1f-5c83-4edc-b043-7e648f171cb7", "process_id": "26024", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_item.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--power',type=float,default=.7);p.add_argument('--k',type=int,default=300);p.add_argument('--days',type=float,default=60);p.add_argument('--label',default='item');a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');l=l[(l.timestamp<=cut)&(l.played_ratio_pct>=50)]\nu=l.u.to_numpy();i=l.i.to_numpy();w=np.exp(-(cut-l.timestamp.to_numpy(dtype=np.float32))/(86400*a.days));w*=np.where(l.is_organic,1,.5)\nR=sparse.csr_matrix((w,(u,i)),shape=shape);R.data=np.log1p(R.data)\nX=sparse.csr_matrix((np.ones(len(u),np.float32),(u,i)),shape=shape);X.data[:]=1;del l,u,i,w;gc.collect()\n# Limit influence of broad listening histories.\ndeg=np.asarray(X.sum(axis=1)).ravel();X=X.multiply(deg.clip(1)[:,None]**(-.35)).tocsc()\npop=np.asarray(X.power(2).sum(axis=0)).ravel();simrows=[]\nfor st in range(0,len(I),500):\n G=(X[:,st:st+500].T@X).toarray()\n G/=np.maximum(pop[st:st+500,None]*pop[None,:],1)**a.power\n G[np.arange(len(G)),np.arange(st,st+len(G))]=0\n ix=np.argpartition(G,-a.k,axis=1)[:,-a.k:];v=np.take_along_axis(G,ix,axis=1)\n simrows.append(sparse.csr_matrix((v.ravel(),(np.repeat(np.arange(len(G)),a.k),ix.ravel())),shape=(len(G),len(I))))\nB=sparse.vstack(simrows).tocsr();sparse.save_npz(S+a.stage+'_'+a.label+'_B.npz',B)\nprint('similarity',time.time()-start,flush=True)\n# use same known positive preference state as the other models\nif a.stage!='past':\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');R+=sparse.csr_matrix((np.full(m.sum(),2,np.float32),(u[m],i[m])),shape=shape)\nscores=(R@B).toarray();name=a.stage+'_'+a.label;np.save(S+name+'.npy',scores)\nprint('done',name,time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_item.py > /app/solution/item.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_item.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--power',type=float,default=.7);p.add_argument('--k',type=int,default=300);p.add_argument('--days',type=float,default=60);p.add_argument('--label',default='item');a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');l=l[(l.timestamp<=cut)&(l.played_ratio_pct>=50)]\nu=l.u.to_numpy();i=l.i.to_numpy();w=np.exp(-(cut-l.timestamp.to_numpy(dtype=np.float32))/(86400*a.days));w*=np.where(l.is_organic,1,.5)\nR=sparse.csr_matrix((w,(u,i)),shape=shape);R.data=np.log1p(R.data)\nX=sparse.csr_matrix((np.ones(len(u),np.float32),(u,i)),shape=shape);X.data[:]=1;del l,u,i,w;gc.collect()\n# Limit influence of broad listening histories.\ndeg=np.asarray(X.sum(axis=1)).ravel();X=X.multiply(deg.clip(1)[:,None]**(-.35)).tocsc()\npop=np.asarray(X.power(2).sum(axis=0)).ravel();simrows=[]\nfor st in range(0,len(I),500):\n G=(X[:,st:st+500].T@X).toarray()\n G/=np.maximum(pop[st:st+500,None]*pop[None,:],1)**a.power\n G[np.arange(len(G)),np.arange(st,st+len(G))]=0\n ix=np.argpartition(G,-a.k,axis=1)[:,-a.k:];v=np.take_along_axis(G,ix,axis=1)\n simrows.append(sparse.csr_matrix((v.ravel(),(np.repeat(np.arange(len(G)),a.k),ix.ravel())),shape=(len(G),len(I))))\nB=sparse.vstack(simrows).tocsr();sparse.save_npz(S+a.stage+'_'+a.label+'_B.npz',B)\nprint('similarity',time.time()-start,flush=True)\n# use same known positive preference state as the other models\nif a.stage!='past':\n l=pd.read_parquet(D+a.stage+'_preference_state.parquet');u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);m=(u>=0)&(i>=0)&(l.state=='liked');R+=sparse.csr_matrix((np.full(m.sum(),2,np.float32),(u[m],i[m])),shape=shape)\nscores=(R@B).toarray();name=a.stage+'_'+a.label;np.save(S+name+'.npy',scores)\nprint('done',name,time.time()-start,flush=True)\nif a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_item.py > /app/solution/item.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 73, "nanos": 44097968 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:27:39.287Z", "callId": "call_nOeakXmHzFwK8ZIEkFwvLQxz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r5 --like 0 --organic 1 --reg 5 > /app/solution/log60r5.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r80 --like 0 --organic 1 --reg 80 > /app/solution/log60r80.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/log20r20.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log120r20 --days 120 --like 0 --organic 1 --reg 20 > /app/solution/log120r20.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:40.597Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"41ac5b\",\"wall_time_seconds\":1.00061598,\"session_id\":68197,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:39.377Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3633f094887d281c3254767bf1cc0", "usage": { "input_tokens": 35354, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 310, "reasoning_output_tokens": 20, "total_tokens": 35664 }, "turn_token_usage": { "input_tokens": 553322, "cached_input_tokens": 526720, "cache_write_input_tokens": 0, "output_tokens": 10098, "reasoning_output_tokens": 2681, "total_tokens": 563420 }, "thread_token_usage": { "input_tokens": 553322, "cached_input_tokens": 526720, "cache_write_input_tokens": 0, "output_tokens": 10098, "reasoning_output_tokens": 2681, "total_tokens": 563420 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-23T05:27:46.487Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:46.487Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:48.277Z", "callId": "call_uL6iOX9l1bjSsTmLEx2zV6Sp", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log /app/solution/log60r5.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:48.390Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"09d89d\",\"wall_time_seconds\":0.00000328,\"exit_code\":0,\"original_token_count\":82,\"output\":\"validation_w180 matrix 5558048 time 5.493313312530518\\nfinished 41.376145362854004\\nSCORE validation_w180 0.026241230630727655\\nmatrix 2.396883010864258\\nsimilarity 52.86244773864746\\ndone validation_item 67.48382806777954\\nSCORE validation_item 0.009153168101888685\\nvalidation_log60r5 matrix 5514505 time 5.61909294128418\\n5204160512\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:48.323Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b471865f-c1dc-411e-a34e-1de6aa476554", "process_id": "54537", "command": [ "/bin/sh", "-lc", "cat /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log /app/solution/log60r5.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/w180.log /app/solution/knn14.log /app/solution/item.log /app/solution/log60r5.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_w180 matrix 5558048 time 5.493313312530518\nfinished 41.376145362854004\nSCORE validation_w180 0.026241230630727655\nmatrix 2.396883010864258\nsimilarity 52.86244773864746\ndone validation_item 67.48382806777954\nSCORE validation_item 0.009153168101888685\nvalidation_log60r5 matrix 5514505 time 5.61909294128418\n5204160512\n", "stderr": "", "aggregated_output": "validation_w180 matrix 5558048 time 5.493313312530518\nfinished 41.376145362854004\nSCORE validation_w180 0.026241230630727655\nmatrix 2.396883010864258\nsimilarity 52.86244773864746\ndone validation_item 67.48382806777954\nSCORE validation_item 0.009153168101888685\nvalidation_log60r5 matrix 5514505 time 5.61909294128418\n5204160512\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3280 }, "formatted_output": "validation_w180 matrix 5558048 time 5.493313312530518\nfinished 41.376145362854004\nSCORE validation_w180 0.026241230630727655\nmatrix 2.396883010864258\nsimilarity 52.86244773864746\ndone validation_item 67.48382806777954\nSCORE validation_item 0.009153168101888685\nvalidation_log60r5 matrix 5514505 time 5.61909294128418\n5204160512\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:48.388Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3634cf83087d2b905a071d66d4b55", "usage": { "input_tokens": 35722, "cached_input_tokens": 35200, "cache_write_input_tokens": 0, "output_tokens": 140, "reasoning_output_tokens": 70, "total_tokens": 35862 }, "turn_token_usage": { "input_tokens": 589044, "cached_input_tokens": 561920, "cache_write_input_tokens": 0, "output_tokens": 10238, "reasoning_output_tokens": 2751, "total_tokens": 599282 }, "thread_token_usage": { "input_tokens": 589044, "cached_input_tokens": 561920, "cache_write_input_tokens": 0, "output_tokens": 10238, "reasoning_output_tokens": 2751, "total_tokens": 599282 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-23T05:27:51.343Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:51.343Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:27:53.171Z", "text": "The regularized model reached 0.02892 NDCG@10, ahead of the neighbor model. Direct track-to-track similarity performed worse. I’m tuning the stronger model and checking whether combining it with listener similarity adds useful signal.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:55.974Z", "callId": "call_83ChLKnLZ27Z8DZY1igJwG68", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models log_20_60_0.25 w60 w14 knn_binary_60_0.5_100_3 item > /app/solution/blend.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:57.173Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"4cdd91\",\"wall_time_seconds\":1.001218805,\"session_id\":88850,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:56.026Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36354a80487d2b18492f36d8d6a0c", "usage": { "input_tokens": 36034, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 212, "reasoning_output_tokens": 53, "total_tokens": 36246 }, "turn_token_usage": { "input_tokens": 625078, "cached_input_tokens": 597504, "cache_write_input_tokens": 0, "output_tokens": 10450, "reasoning_output_tokens": 2804, "total_tokens": 635528 }, "thread_token_usage": { "input_tokens": 625078, "cached_input_tokens": 597504, "cache_write_input_tokens": 0, "output_tokens": 10450, "reasoning_output_tokens": 2804, "total_tokens": 635528 } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:00.270Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fc27dcc1-288e-4a3d-96fb-053e5d6fcdc3", "process_id": "80793", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w60 --reg 20 > /app/solution/w60.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w14 --days 14 --reg 10 > /app/solution/w14.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w180 --days 180 --reg 50 > /app/solution/w180.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py --days 14 --like 2 --k 200 > /app/solution/knn14.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w60 --reg 20 > /app/solution/w60.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w14 --days 14 --reg 10 > /app/solution/w14.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label w180 --days 180 --reg 50 > /app/solution/w180.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_knn.py --days 14 --like 2 --k 200 > /app/solution/knn14.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 134, "nanos": 805537197 }, "formatted_output": "" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-23T05:28:08.447Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:08.447Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:28:27.419Z", "callId": "call_jHWax90rGdk7IiW4uYe3lfyb", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fit_context.py <<'PY'\\nfrom common import *\\nfrom scipy.linalg import cho_factor,cho_solve\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=20);p.add_argument('--days',type=float,default=60);p.add_argument('--contexts',nargs='+',type=float,default=[7,30]);a=p.parse_args();start=time.time()\\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;org=l.is_organic.to_numpy();del l;gc.collect()\\nX=sparse.csr_matrix((np.exp(-age/a.days),(u,i)),shape=shape);X.data=np.log1p(X.data);pop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1);scale=pop**(-.25);X=X.multiply(scale).tocsr()\\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False)\\nZ=(X.T@P.T).T;d=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P,Z;gc.collect()\\nfor days in a.contexts:\\n w=np.exp(-age/days)*np.where(org,1,.5);R=sparse.csr_matrix((w.astype(np.float32),(u,i)),shape=shape);R.data=np.log1p(R.data);R=R.multiply(scale).tocsr()\\n H=(R@X.T).toarray();W=cho_solve(cf,H.T.copy(order='F'),overwrite_b=True,check_finite=False);del H\\n scores=(X.T@W).T/np.maximum(d,1e-6)[None,:];del W\\n name=f'{a.stage}_context{days:g}';np.save(S+name+'.npy',scores)\\n print('done',name,time.time()-start,flush=True)\\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\\n del scores;gc.collect()\\nPY\\ncat /app/solution/blend.log /app/solution/log60r5.log /app/solution/log60r80.log /app/solution/knn14.log\",\"max_output_tokens\":3000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:28:27.493Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"634dd6\",\"wall_time_seconds\":0.00000475,\"exit_code\":0,\"original_token_count\":60,\"output\":\"validation_log60r5 matrix 5514505 time 5.61909294128418\\nfinished 31.321380376815796\\nSCORE validation_log60r5 0.02643589998339373\\nvalidation_log60r80 matrix 5514505 time 8.69627833366394\\nmatrix 2.396883010864258\\nsim 8.733750104904175\\nKilled\\n\"}\n{\"current_time\":\"2026-09-23 05:28:27 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:27.461Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-554b96ea-e83e-46ee-b78b-a76038f6b10d", "process_id": "50885", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_context.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=20);p.add_argument('--days',type=float,default=60);p.add_argument('--contexts',nargs='+',type=float,default=[7,30]);a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;org=l.is_organic.to_numpy();del l;gc.collect()\nX=sparse.csr_matrix((np.exp(-age/a.days),(u,i)),shape=shape);X.data=np.log1p(X.data);pop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1);scale=pop**(-.25);X=X.multiply(scale).tocsr()\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False)\nZ=(X.T@P.T).T;d=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P,Z;gc.collect()\nfor days in a.contexts:\n w=np.exp(-age/days)*np.where(org,1,.5);R=sparse.csr_matrix((w.astype(np.float32),(u,i)),shape=shape);R.data=np.log1p(R.data);R=R.multiply(scale).tocsr()\n H=(R@X.T).toarray();W=cho_solve(cf,H.T.copy(order='F'),overwrite_b=True,check_finite=False);del H\n scores=(X.T@W).T/np.maximum(d,1e-6)[None,:];del W\n name=f'{a.stage}_context{days:g}';np.save(S+name+'.npy',scores)\n print('done',name,time.time()-start,flush=True)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n del scores;gc.collect()\nPY\ncat /app/solution/blend.log /app/solution/log60r5.log /app/solution/log60r80.log /app/solution/knn14.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_context.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=20);p.add_argument('--days',type=float,default=60);p.add_argument('--contexts',nargs='+',type=float,default=[7,30]);a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;org=l.is_organic.to_numpy();del l;gc.collect()\nX=sparse.csr_matrix((np.exp(-age/a.days),(u,i)),shape=shape);X.data=np.log1p(X.data);pop=np.asarray(X.power(2).sum(axis=0)).ravel().clip(1);scale=pop**(-.25);X=X.multiply(scale).tocsr()\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False)\nZ=(X.T@P.T).T;d=1-np.asarray(X.multiply(Z).sum(axis=0)).ravel();del P,Z;gc.collect()\nfor days in a.contexts:\n w=np.exp(-age/days)*np.where(org,1,.5);R=sparse.csr_matrix((w.astype(np.float32),(u,i)),shape=shape);R.data=np.log1p(R.data);R=R.multiply(scale).tocsr()\n H=(R@X.T).toarray();W=cho_solve(cf,H.T.copy(order='F'),overwrite_b=True,check_finite=False);del H\n scores=(X.T@W).T/np.maximum(d,1e-6)[None,:];del W\n name=f'{a.stage}_context{days:g}';np.save(S+name+'.npy',scores)\n print('done',name,time.time()-start,flush=True)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n del scores;gc.collect()\nPY\ncat /app/solution/blend.log /app/solution/log60r5.log /app/solution/log60r80.log /app/solution/knn14.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log60r5 matrix 5514505 time 5.61909294128418\nfinished 31.321380376815796\nSCORE validation_log60r5 0.02643589998339373\nvalidation_log60r80 matrix 5514505 time 8.69627833366394\nmatrix 2.396883010864258\nsim 8.733750104904175\nKilled\n", "stderr": "", "aggregated_output": "validation_log60r5 matrix 5514505 time 5.61909294128418\nfinished 31.321380376815796\nSCORE validation_log60r5 0.02643589998339373\nvalidation_log60r80 matrix 5514505 time 8.69627833366394\nmatrix 2.396883010864258\nsim 8.733750104904175\nKilled\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4750 }, "formatted_output": "validation_log60r5 matrix 5514505 time 5.61909294128418\nfinished 31.321380376815796\nSCORE validation_log60r5 0.02643589998339373\nvalidation_log60r80 matrix 5514505 time 8.69627833366394\nmatrix 2.396883010864258\nsim 8.733750104904175\nKilled\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:27.492Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3635d5aac87d2ac439c9fea9045d8", "usage": { "input_tokens": 36304, "cached_input_tokens": 35840, "cache_write_input_tokens": 0, "output_tokens": 956, "reasoning_output_tokens": 312, "total_tokens": 37260 }, "turn_token_usage": { "input_tokens": 661382, "cached_input_tokens": 633344, "cache_write_input_tokens": 0, "output_tokens": 11406, "reasoning_output_tokens": 3116, "total_tokens": 672788 }, "thread_token_usage": { "input_tokens": 661382, "cached_input_tokens": 633344, "cache_write_input_tokens": 0, "output_tokens": 11406, "reasoning_output_tokens": 3116, "total_tokens": 672788 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-23T05:28:47.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:47.017Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:28:47.886Z", "callId": "call_xqYLQ3jWE4CISK1eYtmU06nt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:28:47.959Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"b9f8ee\",\"wall_time_seconds\":0.0000027,\"exit_code\":0,\"original_token_count\":47,\"output\":\"validation_log60r80 matrix 5514505 time 8.69627833366394\\ncat: /app/solution/log20r20.log: No such file or directory\\ncat: /app/solution/log120r20.log: No such file or directory\\n8589930496\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:47.940Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3637bbbe887d2a1ee37feb642f26d", "usage": { "input_tokens": 37422, "cached_input_tokens": 36096, "cache_write_input_tokens": 0, "output_tokens": 589, "reasoning_output_tokens": 516, "total_tokens": 38011 }, "turn_token_usage": { "input_tokens": 698804, "cached_input_tokens": 669440, "cache_write_input_tokens": 0, "output_tokens": 11995, "reasoning_output_tokens": 3632, "total_tokens": 710799 }, "thread_token_usage": { "input_tokens": 698804, "cached_input_tokens": 669440, "cache_write_input_tokens": 0, "output_tokens": 11995, "reasoning_output_tokens": 3632, "total_tokens": 710799 } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:47.942Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d93057a-1abb-4540-bbeb-1c94ab598c5e", "process_id": "89584", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log60r80 matrix 5514505 time 8.69627833366394\ncat: /app/solution/log20r20.log: No such file or directory\ncat: /app/solution/log120r20.log: No such file or directory\n8589930496\n", "stderr": "", "aggregated_output": "validation_log60r80 matrix 5514505 time 8.69627833366394\ncat: /app/solution/log20r20.log: No such file or directory\ncat: /app/solution/log120r20.log: No such file or directory\n8589930496\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2700 }, "formatted_output": "validation_log60r80 matrix 5514505 time 8.69627833366394\ncat: /app/solution/log20r20.log: No such file or directory\ncat: /app/solution/log120r20.log: No such file or directory\n8589930496\n" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-23T05:28:55.078Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:55.078Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:28:56.420Z", "callId": "call_uIYKrNFI6XMNb82zLdvl3l0K", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \\\"$p\\\"; done\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:28:56.607Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"30639a\",\"wall_time_seconds\":0.00000342,\"exit_code\":0,\"original_token_count\":108,\"output\":\"Name:\\tsh\\nPid:\\t1\\nVmRSS:\\t 1720 kB\\nName:\\tcodex-code-mode\\nPid:\\t205\\nVmRSS:\\t 23512 kB\\nName:\\tsh\\nPid:\\t538\\nVmRSS:\\t 1628 kB\\nName:\\tbash\\nPid:\\t56\\nVmRSS:\\t 3468 kB\\nName:\\tsh\\nPid:\\t573\\nVmRSS:\\t 1720 kB\\nName:\\tpython\\nPid:\\t574\\nVmRSS:\\t 3593576 kB\\nName:\\tcodex\\nPid:\\t62\\nVmRSS:\\t 155224 kB\\nName:\\tpython\\nPid:\\t620\\nVmRSS:\\t 746940 kB\\nName:\\ttee\\nPid:\\t63\\nVmRSS:\\t 1572 kB\\nName:\\tsh\\nPid:\\t634\\nVmRSS:\\t 1716 kB\\nName:\\tsleep\\nPid:\\t7\\nVmRSS:\\t 1644 kB\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:56.505Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab363903ed487d28a9deb33526a129f", "usage": { "input_tokens": 38129, "cached_input_tokens": 37248, "cache_write_input_tokens": 0, "output_tokens": 236, "reasoning_output_tokens": 178, "total_tokens": 38365 }, "turn_token_usage": { "input_tokens": 736933, "cached_input_tokens": 706688, "cache_write_input_tokens": 0, "output_tokens": 12231, "reasoning_output_tokens": 3810, "total_tokens": 749164 }, "thread_token_usage": { "input_tokens": 736933, "cached_input_tokens": 706688, "cache_write_input_tokens": 0, "output_tokens": 12231, "reasoning_output_tokens": 3810, "total_tokens": 749164 } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:56.533Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-108c5dd7-72ca-40c4-aeb6-47dcc64f0057", "process_id": "15938", "command": [ "/bin/sh", "-lc", "for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \"$p\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \"$p\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Name:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23512 kB\nName:\tsh\nPid:\t538\nVmRSS:\t 1628 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tsh\nPid:\t573\nVmRSS:\t 1720 kB\nName:\tpython\nPid:\t574\nVmRSS:\t 3593576 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 155224 kB\nName:\tpython\nPid:\t620\nVmRSS:\t 746940 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsh\nPid:\t634\nVmRSS:\t 1716 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\n", "stderr": "", "aggregated_output": "Name:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23512 kB\nName:\tsh\nPid:\t538\nVmRSS:\t 1628 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tsh\nPid:\t573\nVmRSS:\t 1720 kB\nName:\tpython\nPid:\t574\nVmRSS:\t 3593576 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 155224 kB\nName:\tpython\nPid:\t620\nVmRSS:\t 746940 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsh\nPid:\t634\nVmRSS:\t 1716 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3420 }, "formatted_output": "Name:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23512 kB\nName:\tsh\nPid:\t538\nVmRSS:\t 1628 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tsh\nPid:\t573\nVmRSS:\t 1720 kB\nName:\tpython\nPid:\t574\nVmRSS:\t 3593576 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 155224 kB\nName:\tpython\nPid:\t620\nVmRSS:\t 746940 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsh\nPid:\t634\nVmRSS:\t 1716 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\n" } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-23T05:29:02.009Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:29:02.009Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:29:03.912Z", "callId": "call_KO1luyN5d18kLeaFyzMUNClH", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:29:04.023Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"000a75\",\"wall_time_seconds\":0.00000408,\"exit_code\":0,\"original_token_count\":81,\"output\":\"validation_log60r80 matrix 5514505 time 8.69627833366394\\nfinished 32.35487389564514\\nSCORE validation_log60r80 0.029857094840351497\\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\\ncat: /app/solution/log120r20.log: No such file or directory\\nlow 0\\nhigh 0\\nmax 5689\\noom 1\\noom_kill 1\\noom_group_kill 0\\nsock_throttled 4\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:03.969Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c525531-8f65-4c4d-bb16-92145e26a2fb", "process_id": "63886", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend.log /app/solution/log60r80.log /app/solution/log20r20.log /app/solution/log120r20.log; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log60r80 matrix 5514505 time 8.69627833366394\nfinished 32.35487389564514\nSCORE validation_log60r80 0.029857094840351497\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\ncat: /app/solution/log120r20.log: No such file or directory\nlow 0\nhigh 0\nmax 5689\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 4\n", "stderr": "", "aggregated_output": "validation_log60r80 matrix 5514505 time 8.69627833366394\nfinished 32.35487389564514\nSCORE validation_log60r80 0.029857094840351497\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\ncat: /app/solution/log120r20.log: No such file or directory\nlow 0\nhigh 0\nmax 5689\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 4\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "validation_log60r80 matrix 5514505 time 8.69627833366394\nfinished 32.35487389564514\nSCORE validation_log60r80 0.029857094840351497\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\ncat: /app/solution/log120r20.log: No such file or directory\nlow 0\nhigh 0\nmax 5689\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 4\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:04.022Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab363993a0087d2b0e220e1a8e7bf9c", "usage": { "input_tokens": 38655, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 168, "reasoning_output_tokens": 95, "total_tokens": 38823 }, "turn_token_usage": { "input_tokens": 775588, "cached_input_tokens": 744576, "cache_write_input_tokens": 0, "output_tokens": 12399, "reasoning_output_tokens": 3905, "total_tokens": 787987 }, "thread_token_usage": { "input_tokens": 775588, "cached_input_tokens": 744576, "cache_write_input_tokens": 0, "output_tokens": 12399, "reasoning_output_tokens": 3905, "total_tokens": 787987 } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-23T05:29:22.644Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:29:22.644Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:29:23.939Z", "text": "Stronger regularization improved validation NDCG@10 to 0.02986. I’m now testing shorter-term listening context and a separate model for explicit likes, which carry more weight in the objective.\n" }, { "kind": "context", "timestamp": "2026-09-23T05:29:34.835Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e460c554-2c6c-4412-834a-d2447d0c0f2a", "process_id": "88850", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models log_20_60_0.25 w60 w14 knn_binary_60_0.5_100_3 item > /app/solution/blend.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models log_20_60_0.25 w60 w14 knn_binary_60_0.5_100_3 item > /app/solution/blend.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 98, "nanos": 665317873 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:29:40.306Z", "callId": "call_hjQEfidrQSCyY57hovniwZOd", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fit_likes.py <<'PY'\\nfrom common import *\\nfrom scipy.linalg import cho_factor,cho_solve\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=80);a=p.parse_args();start=time.time()\\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;del l;gc.collect()\\nX=sparse.csr_matrix((np.exp(-age/60),(u,i)),shape=shape);X.data=np.log1p(X.data);X=X.multiply(np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)**(-.25)).tocsr();del u,i,age\\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\\ne=pd.read_parquet(D+'train_likes.parquet')\\nif a.stage=='test':e=pd.concat([e,pd.read_parquet(D+'val_likes.parquet')])\\ne=e[e.timestamp<=cut];u=UI.get_indexer(e.uid);i=II.get_indexer(e.item_id);m=(u>=0)&(i>=0)\\nw=np.exp(-(cut-e.timestamp.to_numpy(dtype=np.float32))/(86400*90))*np.where(e.is_organic,1,.5)\\nL=sparse.csr_matrix((w[m].astype(np.float32),(u[m],i[m])),shape=shape);L.data=np.minimum(L.data,1)\\nfor power in [0,.25]:\\n Z=L.multiply(np.asarray(L.sum(axis=0)).ravel().clip(1)**(-power)).tocsr();scores=-(Z.T@P.T).T;name=a.stage+'_likes'+str(power);np.save(S+name+'.npy',scores)\\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\\n print('done',name,time.time()-start,flush=True);del scores;gc.collect()\\nPY\\ncat /app/solution/blend.log /app/solution/log20r20.log /app/solution/log120r20.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:29:40.362Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"1d9c6e\",\"wall_time_seconds\":0.00000218,\"exit_code\":0,\"original_token_count\":285,\"output\":\"log_20_60_0.25 0.028915257427552434\\nw60 0.027506390295302774\\nw14 0.024823066012296065\\nknn_binary_60_0.5_100_3 0.023715627354572496\\nitem 0.009153168101888685\\nbest 0.029311977048563444 [0.90909091 0.09090909 0. 0. 0. ]\\nbest 0.029404711370821277 [0.72727273 0.27272727 0. 0. 0. ]\\nbest 0.029573606310046324 [0.36363636 0.63636364 0. 0. 0. ]\\nbest 0.029713255003261222 [0.33057851 0.5785124 0.09090909 0. 0. ]\\nbest 0.03004820883232025 [0.26446281 0.46280992 0.27272727 0. 0. ]\\nbest 0.030291701870192277 [0.33132983 0.42073629 0.24793388 0. 0. ]\\nbest 0.030820045717983885 [0.46506386 0.33658903 0.19834711 0. 0. ]\\nbest 0.03095699937065738 [0.42278533 0.30599003 0.27122464 0. 0. ]\\nbest 0.03099782917988121 [0.3843503 0.27817275 0.24656786 0.09090909 0. ]\\nFINAL (np.float64(0.03099782917988121), array([0.3843503 , 0.27817275, 0.24656786, 0.09090909, 0. ]))\\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\\nfinished 32.73732256889343\\nSCORE validation_log20r20 0.0342380820872635\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:40.352Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e756c633-bd79-4eac-bd06-231cdc436fc4", "process_id": "58482", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fit_likes.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=80);a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;del l;gc.collect()\nX=sparse.csr_matrix((np.exp(-age/60),(u,i)),shape=shape);X.data=np.log1p(X.data);X=X.multiply(np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)**(-.25)).tocsr();del u,i,age\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\ne=pd.read_parquet(D+'train_likes.parquet')\nif a.stage=='test':e=pd.concat([e,pd.read_parquet(D+'val_likes.parquet')])\ne=e[e.timestamp<=cut];u=UI.get_indexer(e.uid);i=II.get_indexer(e.item_id);m=(u>=0)&(i>=0)\nw=np.exp(-(cut-e.timestamp.to_numpy(dtype=np.float32))/(86400*90))*np.where(e.is_organic,1,.5)\nL=sparse.csr_matrix((w[m].astype(np.float32),(u[m],i[m])),shape=shape);L.data=np.minimum(L.data,1)\nfor power in [0,.25]:\n Z=L.multiply(np.asarray(L.sum(axis=0)).ravel().clip(1)**(-power)).tocsr();scores=-(Z.T@P.T).T;name=a.stage+'_likes'+str(power);np.save(S+name+'.npy',scores)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n print('done',name,time.time()-start,flush=True);del scores;gc.collect()\nPY\ncat /app/solution/blend.log /app/solution/log20r20.log /app/solution/log120r20.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fit_likes.py <<'PY'\nfrom common import *\nfrom scipy.linalg import cho_factor,cho_solve\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--reg',type=float,default=80);a=p.parse_args();start=time.time()\ncut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage]);l=history('test' if a.stage=='test' else 'validation');m=(l.played_ratio_pct>=50)&(l.timestamp<=cut);l=l[m]\nu=l.u.to_numpy();i=l.i.to_numpy();age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;del l;gc.collect()\nX=sparse.csr_matrix((np.exp(-age/60),(u,i)),shape=shape);X.data=np.log1p(X.data);X=X.multiply(np.asarray(X.power(2).sum(axis=0)).ravel().clip(1)**(-.25)).tocsr();del u,i,age\nG=(X@X.T).toarray();G.flat[::len(U)+1]+=a.reg;cf=cho_factor(G,overwrite_a=True,check_finite=False);del G\nP=cho_solve(cf,np.eye(len(U),dtype=np.float32),overwrite_b=True,check_finite=False);del cf\ne=pd.read_parquet(D+'train_likes.parquet')\nif a.stage=='test':e=pd.concat([e,pd.read_parquet(D+'val_likes.parquet')])\ne=e[e.timestamp<=cut];u=UI.get_indexer(e.uid);i=II.get_indexer(e.item_id);m=(u>=0)&(i>=0)\nw=np.exp(-(cut-e.timestamp.to_numpy(dtype=np.float32))/(86400*90))*np.where(e.is_organic,1,.5)\nL=sparse.csr_matrix((w[m].astype(np.float32),(u[m],i[m])),shape=shape);L.data=np.minimum(L.data,1)\nfor power in [0,.25]:\n Z=L.multiply(np.asarray(L.sum(axis=0)).ravel().clip(1)**(-power)).tocsr();scores=-(Z.T@P.T).T;name=a.stage+'_likes'+str(power);np.save(S+name+'.npy',scores)\n if a.stage=='validation':print('SCORE',name,evaluate(scores),flush=True)\n print('done',name,time.time()-start,flush=True);del scores;gc.collect()\nPY\ncat /app/solution/blend.log /app/solution/log20r20.log /app/solution/log120r20.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "log_20_60_0.25 0.028915257427552434\nw60 0.027506390295302774\nw14 0.024823066012296065\nknn_binary_60_0.5_100_3 0.023715627354572496\nitem 0.009153168101888685\nbest 0.029311977048563444 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.029404711370821277 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.029573606310046324 [0.36363636 0.63636364 0. 0. 0. ]\nbest 0.029713255003261222 [0.33057851 0.5785124 0.09090909 0. 0. ]\nbest 0.03004820883232025 [0.26446281 0.46280992 0.27272727 0. 0. ]\nbest 0.030291701870192277 [0.33132983 0.42073629 0.24793388 0. 0. ]\nbest 0.030820045717983885 [0.46506386 0.33658903 0.19834711 0. 0. ]\nbest 0.03095699937065738 [0.42278533 0.30599003 0.27122464 0. 0. ]\nbest 0.03099782917988121 [0.3843503 0.27817275 0.24656786 0.09090909 0. ]\nFINAL (np.float64(0.03099782917988121), array([0.3843503 , 0.27817275, 0.24656786, 0.09090909, 0. ]))\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\nfinished 32.73732256889343\nSCORE validation_log20r20 0.0342380820872635\n", "stderr": "", "aggregated_output": "log_20_60_0.25 0.028915257427552434\nw60 0.027506390295302774\nw14 0.024823066012296065\nknn_binary_60_0.5_100_3 0.023715627354572496\nitem 0.009153168101888685\nbest 0.029311977048563444 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.029404711370821277 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.029573606310046324 [0.36363636 0.63636364 0. 0. 0. ]\nbest 0.029713255003261222 [0.33057851 0.5785124 0.09090909 0. 0. ]\nbest 0.03004820883232025 [0.26446281 0.46280992 0.27272727 0. 0. ]\nbest 0.030291701870192277 [0.33132983 0.42073629 0.24793388 0. 0. ]\nbest 0.030820045717983885 [0.46506386 0.33658903 0.19834711 0. 0. ]\nbest 0.03095699937065738 [0.42278533 0.30599003 0.27122464 0. 0. ]\nbest 0.03099782917988121 [0.3843503 0.27817275 0.24656786 0.09090909 0. ]\nFINAL (np.float64(0.03099782917988121), array([0.3843503 , 0.27817275, 0.24656786, 0.09090909, 0. ]))\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\nfinished 32.73732256889343\nSCORE validation_log20r20 0.0342380820872635\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2180 }, "formatted_output": "log_20_60_0.25 0.028915257427552434\nw60 0.027506390295302774\nw14 0.024823066012296065\nknn_binary_60_0.5_100_3 0.023715627354572496\nitem 0.009153168101888685\nbest 0.029311977048563444 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.029404711370821277 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.029573606310046324 [0.36363636 0.63636364 0. 0. 0. ]\nbest 0.029713255003261222 [0.33057851 0.5785124 0.09090909 0. 0. ]\nbest 0.03004820883232025 [0.26446281 0.46280992 0.27272727 0. 0. ]\nbest 0.030291701870192277 [0.33132983 0.42073629 0.24793388 0. 0. ]\nbest 0.030820045717983885 [0.46506386 0.33658903 0.19834711 0. 0. ]\nbest 0.03095699937065738 [0.42278533 0.30599003 0.27122464 0. 0. ]\nbest 0.03099782917988121 [0.3843503 0.27817275 0.24656786 0.09090909 0. ]\nFINAL (np.float64(0.03099782917988121), array([0.3843503 , 0.27817275, 0.24656786, 0.09090909, 0. ]))\nvalidation_log20r20 matrix 5514505 time 9.32788896560669\nfinished 32.73732256889343\nSCORE validation_log20r20 0.0342380820872635\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:40.359Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab363a0386087d292d230ae43e3a9c9", "usage": { "input_tokens": 39003, "cached_input_tokens": 38528, "cache_write_input_tokens": 0, "output_tokens": 1149, "reasoning_output_tokens": 486, "total_tokens": 40152 }, "turn_token_usage": { "input_tokens": 814591, "cached_input_tokens": 783104, "cache_write_input_tokens": 0, "output_tokens": 13548, "reasoning_output_tokens": 4391, "total_tokens": 828139 }, "thread_token_usage": { "input_tokens": 814591, "cached_input_tokens": 783104, "cache_write_input_tokens": 0, "output_tokens": 13548, "reasoning_output_tokens": 4391, "total_tokens": 828139 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-23T05:29:45.963Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:29:45.963Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:29:53.430Z", "callId": "call_YmbbfWJVQsdARg4uaoAuN6OT", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py > /app/solution/context.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py > /app/solution/likes.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log7r20 --days 7 --like 0 --organic 1 --reg 20 > /app/solution/log7r20.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r80 --days 20 --like 0 --organic 1 --reg 80 > /app/solution/log20r80.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:29:54.614Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d7aff7\",\"wall_time_seconds\":1.00107169,\"session_id\":57109,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:29:54 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:53.584Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab363c4a18087d2aa9ca8c0a9626797", "usage": { "input_tokens": 40735, "cached_input_tokens": 38784, "cache_write_input_tokens": 0, "output_tokens": 292, "reasoning_output_tokens": 33, "total_tokens": 41027 }, "turn_token_usage": { "input_tokens": 855326, "cached_input_tokens": 821888, "cache_write_input_tokens": 0, "output_tokens": 13840, "reasoning_output_tokens": 4424, "total_tokens": 869166 }, "thread_token_usage": { "input_tokens": 855326, "cached_input_tokens": 821888, "cache_write_input_tokens": 0, "output_tokens": 13840, "reasoning_output_tokens": 4424, "total_tokens": 869166 } } }, { "kind": "context", "timestamp": "2026-09-23T05:30:09.789Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ad8eb95-2198-44d4-8241-9dd842916d6e", "process_id": "68197", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r5 --like 0 --organic 1 --reg 5 > /app/solution/log60r5.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r80 --like 0 --organic 1 --reg 80 > /app/solution/log60r80.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/log20r20.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log120r20 --days 120 --like 0 --organic 1 --reg 20 > /app/solution/log120r20.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r5 --like 0 --organic 1 --reg 5 > /app/solution/log60r5.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log60r80 --like 0 --organic 1 --reg 80 > /app/solution/log60r80.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/log20r20.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log120r20 --days 120 --like 0 --organic 1 --reg 20 > /app/solution/log120r20.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 150, "nanos": 193966110 }, "formatted_output": "" } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-23T05:30:14.349Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:30:14.349Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:30:55.875Z", "callId": "call_mbbsMJAbq1ssXa4yhEczsTQZ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/make_features.py <<'PY'\\nfrom common import *\\nimport argparse,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--models',nargs='+',required=True);p.add_argument('--top',type=int,default=120);a=p.parse_args();start=time.time();cut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\\nl=history('test' if a.stage=='test' else 'validation')\\nif a.stage=='past':\\n E=np.ones(shape,bool);b=l.timestamp<=cut;E[l.u[b],l.i[b]]=False\\n v=l[(l.timestamp>cut+1800)&(l.timestamp<=cut+606600)&(l.is_organic==1)&(l.played_ratio_pct>=80)]\\n Y=np.zeros(shape,np.uint8);Y[v.u,v.i]=1\\n likes=pd.read_parquet(D+'train_likes.parquet');b=(likes.timestamp>cut+1800)&(likes.timestamp<=cut+606600)&(likes.is_organic==1);likes=likes[b];u=UI.get_indexer(likes.uid);i=II.get_indexer(likes.item_id);b=(u>=0)&(i>=0);Y[u[b],i[b]]=7\\n Y*=E;ID=np.sort(Y,axis=1)[:,-10:][:,::-1]@disc\\nelse:\\n E=eligible(a.stage)\\n if a.stage=='validation':Y,ID=truth()\\n else:Y=None;ID=np.ones(len(U))\\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=l.u.to_numpy();i=l.i.to_numpy();org=l.is_organic.to_numpy();pos=l.played_ratio_pct.to_numpy()>=80\\nitemfeat=[];userfeat=[]\\nfor days in [3,7,14,30,90,10000]:\\n m=(age<=days)&pos\\n for organic in [False,True]:\\n mm=m&(org==1) if organic else m\\n itemfeat.append(np.log1p(np.bincount(i[mm],minlength=len(I))))\\n userfeat.append(np.log1p(np.bincount(u[mm],minlength=len(U))))\\n# Earliest observation distinguishes newly popular tracks.\\nfirst=np.full(len(I),cut,np.float32);np.minimum.at(first,i,l.timestamp.to_numpy());itemfeat.append(np.log1p((cut-first)/86400))\\n# Item organic completion and recommendation completion rates.\\nfor organic in [0,1]:\\n m=org==organic;den=np.bincount(i[m],minlength=len(I));num=np.bincount(i[m&pos],minlength=len(I));itemfeat.append((num+5)/(den+10))\\ndel l,age,u,i,org,pos;gc.collect()\\nl=pd.read_parquet(D+'train_likes.parquet')\\nif a.stage=='test':l=pd.concat([l,pd.read_parquet(D+'val_likes.parquet')])\\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);org=l.is_organic.to_numpy()\\nfor days in [7,30,90,10000]:\\n for organic in [False,True]:\\n m=(age<=days)&(u>=0)&(i>=0)\\n if organic:m&=org==1\\n itemfeat.append(np.log1p(np.bincount(i[m],minlength=len(I))))\\n userfeat.append(np.log1p(np.bincount(u[m],minlength=len(U))))\\nitemfeat=np.stack(itemfeat,axis=1).astype(np.float32);userfeat=np.stack(userfeat,axis=1).astype(np.float32);del l;gc.collect()\\nmodels=[np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r') for n in a.models]\\nfs=[];us=[];its=[];ys=[];bas=[]\\nfor st in range(0,len(U),64):\\n es=E[st:st+64];b=len(es);ix=[];zs=[];ranks=[]\\n for mm in models:\\n z=np.array(mm[st:st+b]);z[~es]=-1e10\\n top=np.argpartition(z,-500,axis=1)[:,-500:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\\n ix.append(top[:,:a.top]);zs.append(z)\\n rr=np.full(z.shape,501,np.int16);rr[np.arange(b)[:,None],top]=np.arange(1,501);ranks.append(rr)\\n ix=np.sort(np.concatenate(ix,axis=1),axis=1);keep=np.ones_like(ix,bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1];uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep]\\n feats=[];base=[]\\n for z,rr in zip(zs,ranks):\\n zc=z[uu,ii];mx=np.max(z,axis=1).clip(1e-9);sig=np.std(np.where(es,z,0),axis=1).clip(1e-9)\\n feats.extend([zc,zc/mx[uu],zc/sig[uu],1/np.sqrt(rr[uu,ii])]);base.append(zc/mx[uu])\\n feats=np.stack(feats,axis=1);feats=np.concatenate([feats,itemfeat[ii],userfeat[uu+st]],axis=1)\\n fs.append(feats);us.append(uu+st);its.append(ii);bas.append(np.stack(base,axis=1))\\n if Y is not None:ys.append(Y[uu+st,ii])\\nF=np.concatenate(fs);u=np.concatenate(us);i=np.concatenate(its);base=np.concatenate(bas);y=np.concatenate(ys) if ys else np.zeros(len(u),np.uint8)\\nnp.savez(S+a.stage+'_features.npz',F=F,u=u,i=i,y=y,ID=ID,base=base,models=a.models)\\nprint('features',a.stage,F.shape,'positive',(y>0).sum(),'likes',(y==7).sum(),'time',time.time()-start,flush=True)\\nPY\\ncat /app/solution/context.log /app/solution/log120r20.log /app/solution/likes.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:30:55.928Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"6c53de\",\"wall_time_seconds\":0.0000061,\"exit_code\":1,\"original_token_count\":71,\"output\":\"done validation_context7 48.996716260910034\\nSCORE validation_context7 0.035041491135804764\\nvalidation_log120r20 matrix 5514505 time 6.234302759170532\\nfinished 28.848840951919556\\nSCORE validation_log120r20 0.025401929443763557\\ncat: /app/solution/likes.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:30:55.904Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab363d2d08887d2a06b8c58bf075707", "usage": { "input_tokens": 41103, "cached_input_tokens": 40576, "cache_write_input_tokens": 0, "output_tokens": 2001, "reasoning_output_tokens": 516, "total_tokens": 43104 }, "turn_token_usage": { "input_tokens": 896429, "cached_input_tokens": 862464, "cache_write_input_tokens": 0, "output_tokens": 15841, "reasoning_output_tokens": 4940, "total_tokens": 912270 }, "thread_token_usage": { "input_tokens": 896429, "cached_input_tokens": 862464, "cache_write_input_tokens": 0, "output_tokens": 15841, "reasoning_output_tokens": 4940, "total_tokens": 912270 } } }, { "kind": "context", "timestamp": "2026-09-23T05:30:55.920Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d48bf425-6cdc-4d25-8a11-f967b60ae6e3", "process_id": "3150", "command": [ "/bin/sh", "-lc", "cat > /app/solution/make_features.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--models',nargs='+',required=True);p.add_argument('--top',type=int,default=120);a=p.parse_args();start=time.time();cut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\nl=history('test' if a.stage=='test' else 'validation')\nif a.stage=='past':\n E=np.ones(shape,bool);b=l.timestamp<=cut;E[l.u[b],l.i[b]]=False\n v=l[(l.timestamp>cut+1800)&(l.timestamp<=cut+606600)&(l.is_organic==1)&(l.played_ratio_pct>=80)]\n Y=np.zeros(shape,np.uint8);Y[v.u,v.i]=1\n likes=pd.read_parquet(D+'train_likes.parquet');b=(likes.timestamp>cut+1800)&(likes.timestamp<=cut+606600)&(likes.is_organic==1);likes=likes[b];u=UI.get_indexer(likes.uid);i=II.get_indexer(likes.item_id);b=(u>=0)&(i>=0);Y[u[b],i[b]]=7\n Y*=E;ID=np.sort(Y,axis=1)[:,-10:][:,::-1]@disc\nelse:\n E=eligible(a.stage)\n if a.stage=='validation':Y,ID=truth()\n else:Y=None;ID=np.ones(len(U))\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=l.u.to_numpy();i=l.i.to_numpy();org=l.is_organic.to_numpy();pos=l.played_ratio_pct.to_numpy()>=80\nitemfeat=[];userfeat=[]\nfor days in [3,7,14,30,90,10000]:\n m=(age<=days)&pos\n for organic in [False,True]:\n mm=m&(org==1) if organic else m\n itemfeat.append(np.log1p(np.bincount(i[mm],minlength=len(I))))\n userfeat.append(np.log1p(np.bincount(u[mm],minlength=len(U))))\n# Earliest observation distinguishes newly popular tracks.\nfirst=np.full(len(I),cut,np.float32);np.minimum.at(first,i,l.timestamp.to_numpy());itemfeat.append(np.log1p((cut-first)/86400))\n# Item organic completion and recommendation completion rates.\nfor organic in [0,1]:\n m=org==organic;den=np.bincount(i[m],minlength=len(I));num=np.bincount(i[m&pos],minlength=len(I));itemfeat.append((num+5)/(den+10))\ndel l,age,u,i,org,pos;gc.collect()\nl=pd.read_parquet(D+'train_likes.parquet')\nif a.stage=='test':l=pd.concat([l,pd.read_parquet(D+'val_likes.parquet')])\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);org=l.is_organic.to_numpy()\nfor days in [7,30,90,10000]:\n for organic in [False,True]:\n m=(age<=days)&(u>=0)&(i>=0)\n if organic:m&=org==1\n itemfeat.append(np.log1p(np.bincount(i[m],minlength=len(I))))\n userfeat.append(np.log1p(np.bincount(u[m],minlength=len(U))))\nitemfeat=np.stack(itemfeat,axis=1).astype(np.float32);userfeat=np.stack(userfeat,axis=1).astype(np.float32);del l;gc.collect()\nmodels=[np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r') for n in a.models]\nfs=[];us=[];its=[];ys=[];bas=[]\nfor st in range(0,len(U),64):\n es=E[st:st+64];b=len(es);ix=[];zs=[];ranks=[]\n for mm in models:\n z=np.array(mm[st:st+b]);z[~es]=-1e10\n top=np.argpartition(z,-500,axis=1)[:,-500:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n ix.append(top[:,:a.top]);zs.append(z)\n rr=np.full(z.shape,501,np.int16);rr[np.arange(b)[:,None],top]=np.arange(1,501);ranks.append(rr)\n ix=np.sort(np.concatenate(ix,axis=1),axis=1);keep=np.ones_like(ix,bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1];uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep]\n feats=[];base=[]\n for z,rr in zip(zs,ranks):\n zc=z[uu,ii];mx=np.max(z,axis=1).clip(1e-9);sig=np.std(np.where(es,z,0),axis=1).clip(1e-9)\n feats.extend([zc,zc/mx[uu],zc/sig[uu],1/np.sqrt(rr[uu,ii])]);base.append(zc/mx[uu])\n feats=np.stack(feats,axis=1);feats=np.concatenate([feats,itemfeat[ii],userfeat[uu+st]],axis=1)\n fs.append(feats);us.append(uu+st);its.append(ii);bas.append(np.stack(base,axis=1))\n if Y is not None:ys.append(Y[uu+st,ii])\nF=np.concatenate(fs);u=np.concatenate(us);i=np.concatenate(its);base=np.concatenate(bas);y=np.concatenate(ys) if ys else np.zeros(len(u),np.uint8)\nnp.savez(S+a.stage+'_features.npz',F=F,u=u,i=i,y=y,ID=ID,base=base,models=a.models)\nprint('features',a.stage,F.shape,'positive',(y>0).sum(),'likes',(y==7).sum(),'time',time.time()-start,flush=True)\nPY\ncat /app/solution/context.log /app/solution/log120r20.log /app/solution/likes.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/make_features.py <<'PY'\nfrom common import *\nimport argparse,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--models',nargs='+',required=True);p.add_argument('--top',type=int,default=120);a=p.parse_args();start=time.time();cut=24786800+({'test':606600,'validation':0,'past':-606600}[a.stage])\nl=history('test' if a.stage=='test' else 'validation')\nif a.stage=='past':\n E=np.ones(shape,bool);b=l.timestamp<=cut;E[l.u[b],l.i[b]]=False\n v=l[(l.timestamp>cut+1800)&(l.timestamp<=cut+606600)&(l.is_organic==1)&(l.played_ratio_pct>=80)]\n Y=np.zeros(shape,np.uint8);Y[v.u,v.i]=1\n likes=pd.read_parquet(D+'train_likes.parquet');b=(likes.timestamp>cut+1800)&(likes.timestamp<=cut+606600)&(likes.is_organic==1);likes=likes[b];u=UI.get_indexer(likes.uid);i=II.get_indexer(likes.item_id);b=(u>=0)&(i>=0);Y[u[b],i[b]]=7\n Y*=E;ID=np.sort(Y,axis=1)[:,-10:][:,::-1]@disc\nelse:\n E=eligible(a.stage)\n if a.stage=='validation':Y,ID=truth()\n else:Y=None;ID=np.ones(len(U))\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=l.u.to_numpy();i=l.i.to_numpy();org=l.is_organic.to_numpy();pos=l.played_ratio_pct.to_numpy()>=80\nitemfeat=[];userfeat=[]\nfor days in [3,7,14,30,90,10000]:\n m=(age<=days)&pos\n for organic in [False,True]:\n mm=m&(org==1) if organic else m\n itemfeat.append(np.log1p(np.bincount(i[mm],minlength=len(I))))\n userfeat.append(np.log1p(np.bincount(u[mm],minlength=len(U))))\n# Earliest observation distinguishes newly popular tracks.\nfirst=np.full(len(I),cut,np.float32);np.minimum.at(first,i,l.timestamp.to_numpy());itemfeat.append(np.log1p((cut-first)/86400))\n# Item organic completion and recommendation completion rates.\nfor organic in [0,1]:\n m=org==organic;den=np.bincount(i[m],minlength=len(I));num=np.bincount(i[m&pos],minlength=len(I));itemfeat.append((num+5)/(den+10))\ndel l,age,u,i,org,pos;gc.collect()\nl=pd.read_parquet(D+'train_likes.parquet')\nif a.stage=='test':l=pd.concat([l,pd.read_parquet(D+'val_likes.parquet')])\nl=l[l.timestamp<=cut];age=(cut-l.timestamp.to_numpy(dtype=np.float32))/86400;u=UI.get_indexer(l.uid);i=II.get_indexer(l.item_id);org=l.is_organic.to_numpy()\nfor days in [7,30,90,10000]:\n for organic in [False,True]:\n m=(age<=days)&(u>=0)&(i>=0)\n if organic:m&=org==1\n itemfeat.append(np.log1p(np.bincount(i[m],minlength=len(I))))\n userfeat.append(np.log1p(np.bincount(u[m],minlength=len(U))))\nitemfeat=np.stack(itemfeat,axis=1).astype(np.float32);userfeat=np.stack(userfeat,axis=1).astype(np.float32);del l;gc.collect()\nmodels=[np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r') for n in a.models]\nfs=[];us=[];its=[];ys=[];bas=[]\nfor st in range(0,len(U),64):\n es=E[st:st+64];b=len(es);ix=[];zs=[];ranks=[]\n for mm in models:\n z=np.array(mm[st:st+b]);z[~es]=-1e10\n top=np.argpartition(z,-500,axis=1)[:,-500:];top=np.take_along_axis(top,np.argsort(-np.take_along_axis(z,top,axis=1),axis=1),axis=1)\n ix.append(top[:,:a.top]);zs.append(z)\n rr=np.full(z.shape,501,np.int16);rr[np.arange(b)[:,None],top]=np.arange(1,501);ranks.append(rr)\n ix=np.sort(np.concatenate(ix,axis=1),axis=1);keep=np.ones_like(ix,bool);keep[:,1:]=ix[:,1:]!=ix[:,:-1];uu=np.broadcast_to(np.arange(b)[:,None],ix.shape)[keep];ii=ix[keep]\n feats=[];base=[]\n for z,rr in zip(zs,ranks):\n zc=z[uu,ii];mx=np.max(z,axis=1).clip(1e-9);sig=np.std(np.where(es,z,0),axis=1).clip(1e-9)\n feats.extend([zc,zc/mx[uu],zc/sig[uu],1/np.sqrt(rr[uu,ii])]);base.append(zc/mx[uu])\n feats=np.stack(feats,axis=1);feats=np.concatenate([feats,itemfeat[ii],userfeat[uu+st]],axis=1)\n fs.append(feats);us.append(uu+st);its.append(ii);bas.append(np.stack(base,axis=1))\n if Y is not None:ys.append(Y[uu+st,ii])\nF=np.concatenate(fs);u=np.concatenate(us);i=np.concatenate(its);base=np.concatenate(bas);y=np.concatenate(ys) if ys else np.zeros(len(u),np.uint8)\nnp.savez(S+a.stage+'_features.npz',F=F,u=u,i=i,y=y,ID=ID,base=base,models=a.models)\nprint('features',a.stage,F.shape,'positive',(y>0).sum(),'likes',(y==7).sum(),'time',time.time()-start,flush=True)\nPY\ncat /app/solution/context.log /app/solution/log120r20.log /app/solution/likes.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\nvalidation_log120r20 matrix 5514505 time 6.234302759170532\nfinished 28.848840951919556\nSCORE validation_log120r20 0.025401929443763557\ncat: /app/solution/likes.log: No such file or directory\n", "stderr": "", "aggregated_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\nvalidation_log120r20 matrix 5514505 time 6.234302759170532\nfinished 28.848840951919556\nSCORE validation_log120r20 0.025401929443763557\ncat: /app/solution/likes.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 6100 }, "formatted_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\nvalidation_log120r20 matrix 5514505 time 6.234302759170532\nfinished 28.848840951919556\nSCORE validation_log120r20 0.025401929443763557\ncat: /app/solution/likes.log: No such file or directory\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-23T05:31:01.683Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:31:01.683Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:31:03.488Z", "callId": "call_Hc6kGudzBx2eZ3MOeQRC6xOg", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/context.log /app/solution/likes.log /app/solution/log7r20.log /app/solution/log20r80.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:31:03.588Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"current_time\":\"2026-09-23 05:31:03 UTC\"}\n{\"chunk_id\":\"f4dc3e\",\"wall_time_seconds\":0.00000598,\"exit_code\":1,\"original_token_count\":66,\"output\":\"done validation_context7 48.996716260910034\\nSCORE validation_context7 0.035041491135804764\\ncat: /app/solution/likes.log: No such file or directory\\ncat: /app/solution/log7r20.log: No such file or directory\\ncat: /app/solution/log20r80.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:31:03.528Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a17443be-2da8-45a4-afb0-ae7474c6236d", "process_id": "97327", "command": [ "/bin/sh", "-lc", "cat /app/solution/context.log /app/solution/likes.log /app/solution/log7r20.log /app/solution/log20r80.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/context.log /app/solution/likes.log /app/solution/log7r20.log /app/solution/log20r80.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ncat: /app/solution/likes.log: No such file or directory\ncat: /app/solution/log7r20.log: No such file or directory\ncat: /app/solution/log20r80.log: No such file or directory\n", "stderr": "", "aggregated_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ncat: /app/solution/likes.log: No such file or directory\ncat: /app/solution/log7r20.log: No such file or directory\ncat: /app/solution/log20r80.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5980 }, "formatted_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ncat: /app/solution/likes.log: No such file or directory\ncat: /app/solution/log7r20.log: No such file or directory\ncat: /app/solution/log20r80.log: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:31:03.587Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364103cc887d2a15de5f2173cae0a", "usage": { "input_tokens": 43251, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 56, "total_tokens": 43376 }, "turn_token_usage": { "input_tokens": 939680, "cached_input_tokens": 903424, "cache_write_input_tokens": 0, "output_tokens": 15966, "reasoning_output_tokens": 4996, "total_tokens": 955646 }, "thread_token_usage": { "input_tokens": 939680, "cached_input_tokens": 903424, "cache_write_input_tokens": 0, "output_tokens": 15966, "reasoning_output_tokens": 4996, "total_tokens": 955646 } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-23T05:31:06.298Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:31:06.298Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:31:08.899Z", "callId": "call_EzBhnJjyVUD5i33rMpNOamdN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode log --days 20 --power .25 --reg 20 > /app/solution/best_test.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:31:10.067Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"1d61e0\",\"wall_time_seconds\":1.000968968,\"session_id\":13734,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:31:08.994Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36417dcdc87d2bd8d4c1ca573e73b", "usage": { "input_tokens": 43531, "cached_input_tokens": 43136, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 26, "total_tokens": 43653 }, "turn_token_usage": { "input_tokens": 983211, "cached_input_tokens": 946560, "cache_write_input_tokens": 0, "output_tokens": 16088, "reasoning_output_tokens": 5022, "total_tokens": 999299 }, "thread_token_usage": { "input_tokens": 983211, "cached_input_tokens": 946560, "cache_write_input_tokens": 0, "output_tokens": 16088, "reasoning_output_tokens": 5022, "total_tokens": 999299 } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-23T05:31:27.369Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:31:27.369Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:31:56.146Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16d4925c-5bca-44e0-9c91-401f602b0371", "process_id": "13734", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode log --days 20 --power .25 --reg 20 > /app/solution/best_test.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_ease.py --stage test --mode log --days 20 --power .25 --reg 20 > /app/solution/best_test.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 47, "nanos": 81034489 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:31:58.990Z", "callId": "call_nDGDzR7HhmmGcYGYshUd1pmS", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/rerank.py <<'PY'\\nfrom common import *\\nfrom sklearn.ensemble import HistGradientBoostingRegressor\\nimport argparse,pickle,gc\\np=argparse.ArgumentParser();p.add_argument('--final',action='store_true');a=p.parse_args()\\ndef load(stage):\\n d=np.load(S+stage+'_features.npz');return {k:d[k] for k in d.files}\\ndef ev(z,d):\\n u=d['u'];y=d['y'];ID=d['ID'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];vals=[]\\n for k in range(len(U)):\\n if ID[k]<=0:continue\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(y[b:e][ix]@disc/ID[k])\\n return np.mean(vals)\\ndef normalized(z,u):\\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(z,starts).clip(1e-9);return z/mx[u]\\ndef save(z,d,name,stage):\\n u=d['u'];i=d['i'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\\n for k in range(len(U)):\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\\n out=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all()\\n df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet')\\npast=load('past');val=load('validation')\\nif not a.final:\\n base=val['base'][:,0]*.5+val['base'][:,1]*.5\\n print('base score',ev(base,val),flush=True)\\n best=(ev(base,val),None,0)\\n model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=100,max_leaf_nodes=23,min_samples_leaf=100,l2_regularization=10,max_bins=128,early_stopping=False,warm_start=True,random_state=42)\\n weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\\n for it in [100,200,350]:\\n model.set_params(max_iter=it);model.fit(past['F'],past['y'],sample_weight=weight);pred=model.predict(val['F']);pred=normalized(pred,val['u'])\\n for blend in [0,.25,.5,.75,1]:\\n score=ev(blend*pred+(1-blend)*base,val);print('SCORE',it,blend,score,flush=True)\\n if score>best[0]:\\n best=(score,it,blend);pickle.dump(model,open(S+'reranker.pkl','wb'));save(blend*pred+(1-blend)*base,val,'validation_reranked','validation');json.dump({'score':score,'iterations':it,'blend':blend},open(S+'reranker.json','w'))\\n print('BEST',best,flush=True)\\nelse:\\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+'reranker.pkl','rb'));model.set_params(warm_start=False,max_iter=conf['iterations']);F=np.concatenate([past['F'],val['F']]);y=np.r_[past['y'],val['y']];w=np.r_[1/np.maximum(past['ID'][past['u']],1),1/np.maximum(val['ID'][val['u']],1)];w/=w.mean();del past,val;gc.collect()\\n model.fit(F,y,sample_weight=w);pickle.dump(model,open(S+'final_reranker.pkl','wb'));del F,y,w;gc.collect();test=load('test');pred=normalized(model.predict(test['F']),test['u']);base=test['base'][:,0]*.5+test['base'][:,1]*.5;save(conf['blend']*pred+(1-conf['blend'])*base,test,'submission','test');print('FINAL SAVED',flush=True)\\nPY\\ncat /app/solution/context.log /app/solution/likes.log /app/solution/best_test.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:31:59.072Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5f6857\",\"wall_time_seconds\":0.00000396,\"exit_code\":0,\"original_token_count\":124,\"output\":\"done validation_context7 48.996716260910034\\nSCORE validation_context7 0.035041491135804764\\ndone validation_context30 74.92729592323303\\nSCORE validation_context30 0.03645826636235645\\nSCORE validation_likes0 0.025455425317474127\\ndone validation_likes0 29.13992977142334\\nSCORE validation_likes0.25 0.01863779781683568\\ndone validation_likes0.25 38.71142864227295\\ntest_log_20_20_0.25 matrix 5669201 time 6.269703149795532\\ninverse 25.97609567642212\\nfinished 44.2370171546936\\nsaved submission 845600\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:31:59.044Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8d40e117-9cd0-4030-b5a1-2c644a58fb2c", "process_id": "86905", "command": [ "/bin/sh", "-lc", "cat > /app/solution/rerank.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport argparse,pickle,gc\np=argparse.ArgumentParser();p.add_argument('--final',action='store_true');a=p.parse_args()\ndef load(stage):\n d=np.load(S+stage+'_features.npz');return {k:d[k] for k in d.files}\ndef ev(z,d):\n u=d['u'];y=d['y'];ID=d['ID'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];vals=[]\n for k in range(len(U)):\n if ID[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(y[b:e][ix]@disc/ID[k])\n return np.mean(vals)\ndef normalized(z,u):\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(z,starts).clip(1e-9);return z/mx[u]\ndef save(z,d,name,stage):\n u=d['u'];i=d['i'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\n for k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\n out=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all()\n df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet')\npast=load('past');val=load('validation')\nif not a.final:\n base=val['base'][:,0]*.5+val['base'][:,1]*.5\n print('base score',ev(base,val),flush=True)\n best=(ev(base,val),None,0)\n model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=100,max_leaf_nodes=23,min_samples_leaf=100,l2_regularization=10,max_bins=128,early_stopping=False,warm_start=True,random_state=42)\n weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\n for it in [100,200,350]:\n model.set_params(max_iter=it);model.fit(past['F'],past['y'],sample_weight=weight);pred=model.predict(val['F']);pred=normalized(pred,val['u'])\n for blend in [0,.25,.5,.75,1]:\n score=ev(blend*pred+(1-blend)*base,val);print('SCORE',it,blend,score,flush=True)\n if score>best[0]:\n best=(score,it,blend);pickle.dump(model,open(S+'reranker.pkl','wb'));save(blend*pred+(1-blend)*base,val,'validation_reranked','validation');json.dump({'score':score,'iterations':it,'blend':blend},open(S+'reranker.json','w'))\n print('BEST',best,flush=True)\nelse:\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+'reranker.pkl','rb'));model.set_params(warm_start=False,max_iter=conf['iterations']);F=np.concatenate([past['F'],val['F']]);y=np.r_[past['y'],val['y']];w=np.r_[1/np.maximum(past['ID'][past['u']],1),1/np.maximum(val['ID'][val['u']],1)];w/=w.mean();del past,val;gc.collect()\n model.fit(F,y,sample_weight=w);pickle.dump(model,open(S+'final_reranker.pkl','wb'));del F,y,w;gc.collect();test=load('test');pred=normalized(model.predict(test['F']),test['u']);base=test['base'][:,0]*.5+test['base'][:,1]*.5;save(conf['blend']*pred+(1-conf['blend'])*base,test,'submission','test');print('FINAL SAVED',flush=True)\nPY\ncat /app/solution/context.log /app/solution/likes.log /app/solution/best_test.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/rerank.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport argparse,pickle,gc\np=argparse.ArgumentParser();p.add_argument('--final',action='store_true');a=p.parse_args()\ndef load(stage):\n d=np.load(S+stage+'_features.npz');return {k:d[k] for k in d.files}\ndef ev(z,d):\n u=d['u'];y=d['y'];ID=d['ID'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];vals=[]\n for k in range(len(U)):\n if ID[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(y[b:e][ix]@disc/ID[k])\n return np.mean(vals)\ndef normalized(z,u):\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(z,starts).clip(1e-9);return z/mx[u]\ndef save(z,d,name,stage):\n u=d['u'];i=d['i'];starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\n for k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\n out=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all()\n df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet')\npast=load('past');val=load('validation')\nif not a.final:\n base=val['base'][:,0]*.5+val['base'][:,1]*.5\n print('base score',ev(base,val),flush=True)\n best=(ev(base,val),None,0)\n model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=100,max_leaf_nodes=23,min_samples_leaf=100,l2_regularization=10,max_bins=128,early_stopping=False,warm_start=True,random_state=42)\n weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\n for it in [100,200,350]:\n model.set_params(max_iter=it);model.fit(past['F'],past['y'],sample_weight=weight);pred=model.predict(val['F']);pred=normalized(pred,val['u'])\n for blend in [0,.25,.5,.75,1]:\n score=ev(blend*pred+(1-blend)*base,val);print('SCORE',it,blend,score,flush=True)\n if score>best[0]:\n best=(score,it,blend);pickle.dump(model,open(S+'reranker.pkl','wb'));save(blend*pred+(1-blend)*base,val,'validation_reranked','validation');json.dump({'score':score,'iterations':it,'blend':blend},open(S+'reranker.json','w'))\n print('BEST',best,flush=True)\nelse:\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+'reranker.pkl','rb'));model.set_params(warm_start=False,max_iter=conf['iterations']);F=np.concatenate([past['F'],val['F']]);y=np.r_[past['y'],val['y']];w=np.r_[1/np.maximum(past['ID'][past['u']],1),1/np.maximum(val['ID'][val['u']],1)];w/=w.mean();del past,val;gc.collect()\n model.fit(F,y,sample_weight=w);pickle.dump(model,open(S+'final_reranker.pkl','wb'));del F,y,w;gc.collect();test=load('test');pred=normalized(model.predict(test['F']),test['u']);base=test['base'][:,0]*.5+test['base'][:,1]*.5;save(conf['blend']*pred+(1-conf['blend'])*base,test,'submission','test');print('FINAL SAVED',flush=True)\nPY\ncat /app/solution/context.log /app/solution/likes.log /app/solution/best_test.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ndone validation_context30 74.92729592323303\nSCORE validation_context30 0.03645826636235645\nSCORE validation_likes0 0.025455425317474127\ndone validation_likes0 29.13992977142334\nSCORE validation_likes0.25 0.01863779781683568\ndone validation_likes0.25 38.71142864227295\ntest_log_20_20_0.25 matrix 5669201 time 6.269703149795532\ninverse 25.97609567642212\nfinished 44.2370171546936\nsaved submission 845600\n", "stderr": "", "aggregated_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ndone validation_context30 74.92729592323303\nSCORE validation_context30 0.03645826636235645\nSCORE validation_likes0 0.025455425317474127\ndone validation_likes0 29.13992977142334\nSCORE validation_likes0.25 0.01863779781683568\ndone validation_likes0.25 38.71142864227295\ntest_log_20_20_0.25 matrix 5669201 time 6.269703149795532\ninverse 25.97609567642212\nfinished 44.2370171546936\nsaved submission 845600\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3960 }, "formatted_output": "done validation_context7 48.996716260910034\nSCORE validation_context7 0.035041491135804764\ndone validation_context30 74.92729592323303\nSCORE validation_context30 0.03645826636235645\nSCORE validation_likes0 0.025455425317474127\ndone validation_likes0 29.13992977142334\nSCORE validation_likes0.25 0.01863779781683568\ndone validation_likes0.25 38.71142864227295\ntest_log_20_20_0.25 matrix 5669201 time 6.269703149795532\ninverse 25.97609567642212\nfinished 44.2370171546936\nsaved submission 845600\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:31:59.070Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3641e603087d2bee20b93f70aead5", "usage": { "input_tokens": 43712, "cached_input_tokens": 43392, "cache_write_input_tokens": 0, "output_tokens": 1581, "reasoning_output_tokens": 512, "total_tokens": 45293 }, "turn_token_usage": { "input_tokens": 1026923, "cached_input_tokens": 989952, "cache_write_input_tokens": 0, "output_tokens": 17669, "reasoning_output_tokens": 5534, "total_tokens": 1044592 }, "thread_token_usage": { "input_tokens": 1026923, "cached_input_tokens": 989952, "cache_write_input_tokens": 0, "output_tokens": 17669, "reasoning_output_tokens": 5534, "total_tokens": 1044592 } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-23T05:32:05.517Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:05.517Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:32:07.422Z", "text": "Using recent listening to query the longer-term collaborative model reached 0.03646 NDCG@10. The saved submission has also been upgraded and refit with validation history. Next, I’m testing a reranker trained on an earlier week, keeping the public validation outcomes reserved for evaluation.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:32:11.263Z", "callId": "call_N2wbrb6xWlEKvOnIgRb4N9wz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/log7r20.log /app/solution/log20r80.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:32:12.476Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b8cc95\",\"wall_time_seconds\":1.00049598,\"session_id\":42145,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"22c549\",\"wall_time_seconds\":0.000003921,\"exit_code\":1,\"original_token_count\":29,\"output\":\"validation_log7r20 matrix 5514505 time 8.365236520767212\\ncat: /app/solution/log20r80.log: No such file or directory\\n\"}\n{\"current_time\":\"2026-09-23 05:32:12 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:11.378Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3644f6d9487d28e9a97861669efa5", "usage": { "input_tokens": 45524, "cached_input_tokens": 43520, "cache_write_input_tokens": 0, "output_tokens": 307, "reasoning_output_tokens": 103, "total_tokens": 45831 }, "turn_token_usage": { "input_tokens": 1072447, "cached_input_tokens": 1033472, "cache_write_input_tokens": 0, "output_tokens": 17976, "reasoning_output_tokens": 5637, "total_tokens": 1090423 }, "thread_token_usage": { "input_tokens": 1072447, "cached_input_tokens": 1033472, "cache_write_input_tokens": 0, "output_tokens": 17976, "reasoning_output_tokens": 5637, "total_tokens": 1090423 } } }, { "kind": "context", "timestamp": "2026-09-23T05:32:12.466Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a207ee58-ca8e-4a34-b8db-91a241c30364", "process_id": "26090", "command": [ "/bin/sh", "-lc", "cat /app/solution/log7r20.log /app/solution/log20r80.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/log7r20.log /app/solution/log20r80.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "validation_log7r20 matrix 5514505 time 8.365236520767212\ncat: /app/solution/log20r80.log: No such file or directory\n", "stderr": "", "aggregated_output": "validation_log7r20 matrix 5514505 time 8.365236520767212\ncat: /app/solution/log20r80.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3921 }, "formatted_output": "validation_log7r20 matrix 5514505 time 8.365236520767212\ncat: /app/solution/log20r80.log: No such file or directory\n" } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-23T05:32:30.300Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:30.300Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:32:31.201Z", "callId": "call_j1Z7x8eSBZA6M3YgBmTlOCsl", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/log7r20.log /app/solution/log20r80.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:32:31.346Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"d83662\",\"wall_time_seconds\":0.00000428,\"exit_code\":0,\"original_token_count\":36,\"output\":\"validation_log7r20 matrix 5514505 time 8.365236520767212\\nfinished 31.432801246643066\\nSCORE validation_log7r20 0.033795986910253595\\n5410627584\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:31.246Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d2ec9c5-438e-45d8-ae0c-16c3a52acc65", "process_id": "64538", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/log7r20.log /app/solution/log20r80.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/log7r20.log /app/solution/log20r80.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log7r20 matrix 5514505 time 8.365236520767212\nfinished 31.432801246643066\nSCORE validation_log7r20 0.033795986910253595\n5410627584\n", "stderr": "", "aggregated_output": "validation_log7r20 matrix 5514505 time 8.365236520767212\nfinished 31.432801246643066\nSCORE validation_log7r20 0.033795986910253595\n5410627584\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4280 }, "formatted_output": "validation_log7r20 matrix 5514505 time 8.365236520767212\nfinished 31.432801246643066\nSCORE validation_log7r20 0.033795986910253595\n5410627584\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:32:31.265Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3645e476887d2923d1d4db07884e5", "usage": { "input_tokens": 45977, "cached_input_tokens": 45312, "cache_write_input_tokens": 0, "output_tokens": 523, "reasoning_output_tokens": 458, "total_tokens": 46500 }, "turn_token_usage": { "input_tokens": 1118424, "cached_input_tokens": 1078784, "cache_write_input_tokens": 0, "output_tokens": 18499, "reasoning_output_tokens": 6095, "total_tokens": 1136923 }, "thread_token_usage": { "input_tokens": 1118424, "cached_input_tokens": 1078784, "cache_write_input_tokens": 0, "output_tokens": 18499, "reasoning_output_tokens": 6095, "total_tokens": 1136923 } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-23T05:32:40.190Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:40.190Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:32:41.993Z", "callId": "call_Frgb5Xq0tcsFWeRbPDGbCJAG", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:57109,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/log20r80.log /app/solution/blend2.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:32:47.078Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\n{\"chunk_id\":\"c9a6a4\",\"wall_time_seconds\":5.001429462,\"session_id\":57109,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"037866\",\"wall_time_seconds\":0.00000595,\"exit_code\":0,\"original_token_count\":15,\"output\":\"validation_log20r80 matrix 5514505 time 5.180814266204834\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:42.088Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3646f97e087d29495ad58d7092991", "usage": { "input_tokens": 46607, "cached_input_tokens": 45824, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 179, "total_tokens": 46861 }, "turn_token_usage": { "input_tokens": 1165031, "cached_input_tokens": 1124608, "cache_write_input_tokens": 0, "output_tokens": 18753, "reasoning_output_tokens": 6274, "total_tokens": 1183784 }, "thread_token_usage": { "input_tokens": 1165031, "cached_input_tokens": 1124608, "cache_write_input_tokens": 0, "output_tokens": 18753, "reasoning_output_tokens": 6274, "total_tokens": 1183784 } } }, { "kind": "context", "timestamp": "2026-09-23T05:32:47.051Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d95d01d0-e6d0-438a-90fc-fe4ffa3272ec", "process_id": "41370", "command": [ "/bin/sh", "-lc", "cat /app/solution/log20r80.log /app/solution/blend2.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/log20r80.log /app/solution/blend2.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log20r80 matrix 5514505 time 5.180814266204834\n", "stderr": "", "aggregated_output": "validation_log20r80 matrix 5514505 time 5.180814266204834\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5950 }, "formatted_output": "validation_log20r80 matrix 5514505 time 5.180814266204834\n" } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-23T05:32:53.570Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:53.570Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:33:03.018Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba539686-c5ef-4c3f-8294-cd02909ca5b6", "process_id": "57109", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py > /app/solution/context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py > /app/solution/likes.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log7r20 --days 7 --like 0 --organic 1 --reg 20 > /app/solution/log7r20.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r80 --days 20 --like 0 --organic 1 --reg 80 > /app/solution/log20r80.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py > /app/solution/context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py > /app/solution/likes.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log7r20 --days 7 --like 0 --organic 1 --reg 20 > /app/solution/log7r20.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --label log20r80 --days 20 --like 0 --organic 1 --reg 80 > /app/solution/log20r80.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 189, "nanos": 415912922 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:33:04.420Z", "callId": "call_qPSfDpY5GYUc87itecYZWCWW", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"while ! rg -q SCORE /app/solution/log20r80.log; do sleep 2; done\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage past --contexts 30 > /app/solution/past_context.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --stage past --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/past_log.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage past > /app/solution/past_likes.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:33:05.602Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"7f114b\",\"wall_time_seconds\":1.000847708,\"session_id\":89481,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:33:04.485Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3647f451887d2868d3ce4f531f89b", "usage": { "input_tokens": 46972, "cached_input_tokens": 46464, "cache_write_input_tokens": 0, "output_tokens": 528, "reasoning_output_tokens": 150, "total_tokens": 47500 }, "turn_token_usage": { "input_tokens": 1212003, "cached_input_tokens": 1171072, "cache_write_input_tokens": 0, "output_tokens": 19281, "reasoning_output_tokens": 6424, "total_tokens": 1231284 }, "thread_token_usage": { "input_tokens": 1212003, "cached_input_tokens": 1171072, "cache_write_input_tokens": 0, "output_tokens": 19281, "reasoning_output_tokens": 6424, "total_tokens": 1231284 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-23T05:33:23.419Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:23.419Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:33:24.078Z", "callId": "call_a1UZlXA5iW2W18IEO0bThLXD", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/log20r80.log /app/solution/past_context.log\",\"max_output_tokens\":3000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:33:24.165Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"cb4c94\",\"wall_time_seconds\":0.000004051,\"exit_code\":0,\"original_token_count\":33,\"output\":\"validation_log20r80 matrix 5514505 time 5.180814266204834\\nfinished 27.125392198562622\\nSCORE validation_log20r80 0.03595748578799903\\n\"}\n{\"current_time\":\"2026-09-23 05:33:24 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:33:24.122Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36491f1dc87d2b605ce4a7403af15", "usage": { "input_tokens": 47558, "cached_input_tokens": 46848, "cache_write_input_tokens": 0, "output_tokens": 480, "reasoning_output_tokens": 417, "total_tokens": 48038 }, "turn_token_usage": { "input_tokens": 1259561, "cached_input_tokens": 1217920, "cache_write_input_tokens": 0, "output_tokens": 19761, "reasoning_output_tokens": 6841, "total_tokens": 1279322 }, "thread_token_usage": { "input_tokens": 1259561, "cached_input_tokens": 1217920, "cache_write_input_tokens": 0, "output_tokens": 19761, "reasoning_output_tokens": 6841, "total_tokens": 1279322 } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:24.134Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f1638243-214f-4a35-ab02-ddd82cd059d4", "process_id": "37628", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/log20r80.log /app/solution/past_context.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/log20r80.log /app/solution/past_context.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_log20r80 matrix 5514505 time 5.180814266204834\nfinished 27.125392198562622\nSCORE validation_log20r80 0.03595748578799903\n", "stderr": "", "aggregated_output": "validation_log20r80 matrix 5514505 time 5.180814266204834\nfinished 27.125392198562622\nSCORE validation_log20r80 0.03595748578799903\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4051 }, "formatted_output": "validation_log20r80 matrix 5514505 time 5.180814266204834\nfinished 27.125392198562622\nSCORE validation_log20r80 0.03595748578799903\n" } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-23T05:33:38.353Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:38.353Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:33:39.145Z", "callId": "call_2YRQinSiNd5dwCv4KZ2OdJ8Q", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/past_context.log; ls -lh /app/solution/blend_candidates.npz\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:33:39.233Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"18150e\",\"wall_time_seconds\":0.00000722,\"exit_code\":0,\"original_token_count\":20,\"output\":\"-rw-r--r-- 1 agent agent 133M Sep 23 05:29 /app/solution/blend_candidates.npz\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:33:39.189Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4aaa7d9f-980b-485c-be88-1981f5a9b204", "process_id": "89208", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/past_context.log; ls -lh /app/solution/blend_candidates.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/past_context.log; ls -lh /app/solution/blend_candidates.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 133M Sep 23 05:29 /app/solution/blend_candidates.npz\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 133M Sep 23 05:29 /app/solution/blend_candidates.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7220 }, "formatted_output": "-rw-r--r-- 1 agent agent 133M Sep 23 05:29 /app/solution/blend_candidates.npz\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:39.230Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364a4777c87d2acf3e6a0397cd480", "usage": { "input_tokens": 48159, "cached_input_tokens": 47360, "cache_write_input_tokens": 0, "output_tokens": 446, "reasoning_output_tokens": 388, "total_tokens": 48605 }, "turn_token_usage": { "input_tokens": 1307720, "cached_input_tokens": 1265280, "cache_write_input_tokens": 0, "output_tokens": 20207, "reasoning_output_tokens": 7229, "total_tokens": 1327927 }, "thread_token_usage": { "input_tokens": 1307720, "cached_input_tokens": 1265280, "cache_write_input_tokens": 0, "output_tokens": 20207, "reasoning_output_tokens": 7229, "total_tokens": 1327927 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-23T05:33:44.373Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:44.373Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:33:46.767Z", "callId": "call_6ETG9ioQer15BEgcoXy0orEw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -lh /app/solution/*context*.npy /app/solution/*likes*.npy; for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \\\"$p\\\"; done; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:33:46.862Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"cb38a6\",\"wall_time_seconds\":0.00000624,\"exit_code\":0,\"original_token_count\":207,\"output\":\"-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_context30.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:30 /app/solution/validation_context7.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.25.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.npy\\nName:\\tsh\\nPid:\\t1\\nVmRSS:\\t 1720 kB\\nName:\\tcodex-code-mode\\nPid:\\t205\\nVmRSS:\\t 23536 kB\\nName:\\tbash\\nPid:\\t56\\nVmRSS:\\t 3468 kB\\nName:\\tcodex\\nPid:\\t62\\nVmRSS:\\t 154896 kB\\nName:\\ttee\\nPid:\\t63\\nVmRSS:\\t 1572 kB\\nName:\\tsleep\\nPid:\\t7\\nVmRSS:\\t 1644 kB\\nName:\\tsh\\nPid:\\t768\\nVmRSS:\\t 1644 kB\\nName:\\tpython\\nPid:\\t769\\nVmRSS:\\t 4301160 kB\\nName:\\tsh\\nPid:\\t818\\nVmRSS:\\t 1688 kB\\nName:\\tpython\\nPid:\\t820\\nVmRSS:\\t 3922204 kB\\nName:\\tsh\\nPid:\\t852\\nVmRSS:\\t 1796 kB\\nlow 0\\nhigh 0\\nmax 16340\\noom 1\\noom_kill 1\\noom_group_kill 0\\nsock_throttled 8\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:33:46.822Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ea956fd9-92a6-4bb2-8644-deda56ead10c", "process_id": "48417", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/*context*.npy /app/solution/*likes*.npy; for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \"$p\"; done; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/*context*.npy /app/solution/*likes*.npy; for p in /proc/[0-9]*/status; do awk '/^(Name|Pid|VmRSS):/' \"$p\"; done; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_context30.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:30 /app/solution/validation_context7.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.npy\nName:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23536 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 154896 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\nName:\tsh\nPid:\t768\nVmRSS:\t 1644 kB\nName:\tpython\nPid:\t769\nVmRSS:\t 4301160 kB\nName:\tsh\nPid:\t818\nVmRSS:\t 1688 kB\nName:\tpython\nPid:\t820\nVmRSS:\t 3922204 kB\nName:\tsh\nPid:\t852\nVmRSS:\t 1796 kB\nlow 0\nhigh 0\nmax 16340\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_context30.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:30 /app/solution/validation_context7.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.npy\nName:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23536 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 154896 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\nName:\tsh\nPid:\t768\nVmRSS:\t 1644 kB\nName:\tpython\nPid:\t769\nVmRSS:\t 4301160 kB\nName:\tsh\nPid:\t818\nVmRSS:\t 1688 kB\nName:\tpython\nPid:\t820\nVmRSS:\t 3922204 kB\nName:\tsh\nPid:\t852\nVmRSS:\t 1796 kB\nlow 0\nhigh 0\nmax 16340\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6240 }, "formatted_output": "-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_context30.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:30 /app/solution/validation_context7.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:31 /app/solution/validation_likes0.npy\nName:\tsh\nPid:\t1\nVmRSS:\t 1720 kB\nName:\tcodex-code-mode\nPid:\t205\nVmRSS:\t 23536 kB\nName:\tbash\nPid:\t56\nVmRSS:\t 3468 kB\nName:\tcodex\nPid:\t62\nVmRSS:\t 154896 kB\nName:\ttee\nPid:\t63\nVmRSS:\t 1572 kB\nName:\tsleep\nPid:\t7\nVmRSS:\t 1644 kB\nName:\tsh\nPid:\t768\nVmRSS:\t 1644 kB\nName:\tpython\nPid:\t769\nVmRSS:\t 4301160 kB\nName:\tsh\nPid:\t818\nVmRSS:\t 1688 kB\nName:\tpython\nPid:\t820\nVmRSS:\t 3922204 kB\nName:\tsh\nPid:\t852\nVmRSS:\t 1796 kB\nlow 0\nhigh 0\nmax 16340\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:46.860Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364b3740887d2b81bfba188919039", "usage": { "input_tokens": 48690, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 209, "reasoning_output_tokens": 119, "total_tokens": 48899 }, "turn_token_usage": { "input_tokens": 1356410, "cached_input_tokens": 1313280, "cache_write_input_tokens": 0, "output_tokens": 20416, "reasoning_output_tokens": 7348, "total_tokens": 1376826 }, "thread_token_usage": { "input_tokens": 1356410, "cached_input_tokens": 1313280, "cache_write_input_tokens": 0, "output_tokens": 20416, "reasoning_output_tokens": 7348, "total_tokens": 1376826 } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-23T05:34:02.848Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:02.848Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:34:06.248Z", "callId": "call_QKqBD6TTuofzDsbnwln7XQAq", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nfor f in ['tune.py','make_features.py']:\\n p=Path('/app/solution')/f;s=p.read_text();s=s.replace('from common import *','from common import *\\\\nimport mmap')\\n if f=='tune.py':s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10;', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10;')\\n else:s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10')\\n p.write_text(s)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:34:07.004Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"d1bc06\",\"wall_time_seconds\":0.00000459,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:06.385Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1abe6d10-db2e-420b-8cf2-2a0a9a370f8d", "process_id": "86540", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nfor f in ['tune.py','make_features.py']:\n p=Path('/app/solution')/f;s=p.read_text();s=s.replace('from common import *','from common import *\\nimport mmap')\n if f=='tune.py':s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10;', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10;')\n else:s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10')\n p.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nfor f in ['tune.py','make_features.py']:\n p=Path('/app/solution')/f;s=p.read_text();s=s.replace('from common import *','from common import *\\nimport mmap')\n if f=='tune.py':s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10;', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10;')\n else:s=s.replace('z=np.array(mm[st:st+b]);z[~es]=-1e10', 'z=np.array(mm[st:st+b]);mm._mmap.madvise(mmap.MADV_DONTNEED);z[~es]=-1e10')\n p.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4590 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:34:07.002Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364bb258487d285277706e9aacc2f", "usage": { "input_tokens": 49354, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 535, "reasoning_output_tokens": 308, "total_tokens": 49889 }, "turn_token_usage": { "input_tokens": 1405764, "cached_input_tokens": 1361792, "cache_write_input_tokens": 0, "output_tokens": 20951, "reasoning_output_tokens": 7656, "total_tokens": 1426715 }, "thread_token_usage": { "input_tokens": 1405764, "cached_input_tokens": 1361792, "cache_write_input_tokens": 0, "output_tokens": 20951, "reasoning_output_tokens": 7656, "total_tokens": 1426715 } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-23T05:34:17.746Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:17.746Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:34:18.840Z", "text": "The earlier-week training pipeline is running. Its reranker uses collaborative scores, recent track popularity, organic completion rates, and like rates. I’ll keep it only if it improves the untouched public validation window.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:34:19.850Z", "callId": "call_pngeawFMw2OhWCvfcu24CfUD", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/past_context.log /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log\",\"max_output_tokens\":2500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:34:19.933Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"e0430f\",\"wall_time_seconds\":0.00000572,\"exit_code\":1,\"original_token_count\":54,\"output\":\"done past_context30 49.262330293655396\\npast_log20r20 matrix 5350589 time 7.547338008880615\\ncat: /app/solution/past_likes.log: No such file or directory\\ncat: /app/solution/past_features.log: No such file or directory\\n\"}\n{\"current_time\":\"2026-09-23 05:34:19 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:19.909Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1969f8db-65db-4c45-9a6b-f28ec97b9c2f", "process_id": "86319", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/past_context.log /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/past_context.log /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "done past_context30 49.262330293655396\npast_log20r20 matrix 5350589 time 7.547338008880615\ncat: /app/solution/past_likes.log: No such file or directory\ncat: /app/solution/past_features.log: No such file or directory\n", "stderr": "", "aggregated_output": "done past_context30 49.262330293655396\npast_log20r20 matrix 5350589 time 7.547338008880615\ncat: /app/solution/past_likes.log: No such file or directory\ncat: /app/solution/past_features.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5720 }, "formatted_output": "done past_context30 49.262330293655396\npast_log20r20 matrix 5350589 time 7.547338008880615\ncat: /app/solution/past_likes.log: No such file or directory\ncat: /app/solution/past_features.log: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:34:19.914Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364cf48bc87d2acc392ac69f8171c", "usage": { "input_tokens": 49946, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 367, "reasoning_output_tokens": 240, "total_tokens": 50313 }, "turn_token_usage": { "input_tokens": 1455710, "cached_input_tokens": 1410944, "cache_write_input_tokens": 0, "output_tokens": 21318, "reasoning_output_tokens": 7896, "total_tokens": 1477028 }, "thread_token_usage": { "input_tokens": 1455710, "cached_input_tokens": 1410944, "cache_write_input_tokens": 0, "output_tokens": 21318, "reasoning_output_tokens": 7896, "total_tokens": 1477028 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-23T05:34:32.910Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:32.910Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:34:39.603Z", "callId": "call_l7lRPw5ZWttiofvGCZfMNSj0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"while [ ! -f /app/solution/blend2.json ]; do sleep 2; done\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage test --contexts 30 > /app/solution/test_context.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage test > /app/solution/test_likes.log 2>&1\\nln -f /app/solution/test_log_20_20_0.25.npy /app/solution/test_log20r20.npy\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:34:40.789Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5f08dd\",\"wall_time_seconds\":1.001316243,\"session_id\":38851,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:39.738Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364dc53fc87d29e50f462a4596572", "usage": { "input_tokens": 50455, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 606, "reasoning_output_tokens": 371, "total_tokens": 51061 }, "turn_token_usage": { "input_tokens": 1506165, "cached_input_tokens": 1460736, "cache_write_input_tokens": 0, "output_tokens": 21924, "reasoning_output_tokens": 8267, "total_tokens": 1528089 }, "thread_token_usage": { "input_tokens": 1506165, "cached_input_tokens": 1460736, "cache_write_input_tokens": 0, "output_tokens": 21924, "reasoning_output_tokens": 8267, "total_tokens": 1528089 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-23T05:34:54.446Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:54.446Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:34:54.448Z", "callId": "call_w4aCGP3KBzsY7JvDC1wb5oX0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /proc/769/io /proc/769/status; cat /app/solution/blend2.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:34:54.624Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"f40b78\",\"wall_time_seconds\":0.00000368,\"exit_code\":0,\"original_token_count\":403,\"output\":\"rchar: 41163991\\nwchar: 0\\nsyscr: 2131\\nsyscw: 0\\nread_bytes: 14568108032\\nwrite_bytes: 0\\ncancelled_write_bytes: 0\\nName:\\tpython\\nUmask:\\t0022\\nState:\\tD (disk sleep)\\nTgid:\\t769\\nNgid:\\t0\\nPid:\\t769\\nPPid:\\t768\\nTracerPid:\\t0\\nUid:\\t1000\\t1000\\t1000\\t1000\\nGid:\\t1000\\t1000\\t1000\\t1000\\nFDSize:\\t64\\nGroups:\\t1000 \\nNStgid:\\t769\\nNSpid:\\t769\\nNSpgid:\\t768\\nNSsid:\\t768\\nKthread:\\t0\\nVmPeak:\\t 7352564 kB\\nVmSize:\\t 7065464 kB\\nVmLck:\\t 0 kB\\nVmPin:\\t 0 kB\\nVmHWM:\\t 5323744 kB\\nVmRSS:\\t 3519908 kB\\nRssAnon:\\t 880712 kB\\nRssFile:\\t 2639196 kB\\nRssShmem:\\t 0 kB\\nVmData:\\t 1373444 kB\\nVmStk:\\t 140 kB\\nVmExe:\\t 4 kB\\nVmLib:\\t 140624 kB\\nVmPTE:\\t 11944 kB\\nVmSwap:\\t 0 kB\\nHugetlbPages:\\t 0 kB\\nCoreDumping:\\t0\\nTHP_enabled:\\t1\\nuntag_mask:\\t0xffffffffffffffff\\nThreads:\\t13\\nSigQ:\\t0/56399\\nSigPnd:\\t0000000000000000\\nShdPnd:\\t0000000000000000\\nSigBlk:\\t0000000000000000\\nSigIgn:\\t0000000001001000\\nSigCgt:\\t0000000100000002\\nCapInh:\\t0000000000000000\\nCapPrm:\\t0000000000000000\\nCapEff:\\t0000000000000000\\nCapBnd:\\t00000000a80425fb\\nCapAmb:\\t0000000000000000\\nNoNewPrivs:\\t0\\nSeccomp:\\t2\\nSeccomp_filters:\\t1\\nSpeculation_Store_Bypass:\\tthread vulnerable\\nSpeculationIndirectBranch:\\tconditional enabled\\nCpus_allowed:\\tff\\nCpus_allowed_list:\\t0-7\\nMems_allowed:\\t00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000001\\nMems_allowed_list:\\t0\\nvoluntary_ctxt_switches:\\t144363\\nnonvoluntary_ctxt_switches:\\t1147\\nx86_Thread_features:\\t\\nx86_Thread_features_locked:\\t\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:54.455Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364f10a0887d295cde980b18f7d5b", "usage": { "input_tokens": 51119, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 51, "total_tokens": 51219 }, "turn_token_usage": { "input_tokens": 1557284, "cached_input_tokens": 1511040, "cache_write_input_tokens": 0, "output_tokens": 22024, "reasoning_output_tokens": 8318, "total_tokens": 1579308 }, "thread_token_usage": { "input_tokens": 1557284, "cached_input_tokens": 1511040, "cache_write_input_tokens": 0, "output_tokens": 22024, "reasoning_output_tokens": 8318, "total_tokens": 1579308 } } }, { "kind": "context", "timestamp": "2026-09-23T05:34:54.500Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8af528c7-e4f3-45ad-acef-594b468e8d1d", "process_id": "52053", "command": [ "/bin/sh", "-lc", "cat /proc/769/io /proc/769/status; cat /app/solution/blend2.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /proc/769/io /proc/769/status; cat /app/solution/blend2.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rchar: 41163991\nwchar: 0\nsyscr: 2131\nsyscw: 0\nread_bytes: 14568108032\nwrite_bytes: 0\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t769\nNgid:\t0\nPid:\t769\nPPid:\t768\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t769\nNSpid:\t769\nNSpgid:\t768\nNSsid:\t768\nKthread:\t0\nVmPeak:\t 7352564 kB\nVmSize:\t 7065464 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5323744 kB\nVmRSS:\t 3519908 kB\nRssAnon:\t 880712 kB\nRssFile:\t 2639196 kB\nRssShmem:\t 0 kB\nVmData:\t 1373444 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 11944 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\nCoreDumping:\t0\nTHP_enabled:\t1\nuntag_mask:\t0xffffffffffffffff\nThreads:\t13\nSigQ:\t0/56399\nSigPnd:\t0000000000000000\nShdPnd:\t0000000000000000\nSigBlk:\t0000000000000000\nSigIgn:\t0000000001001000\nSigCgt:\t0000000100000002\nCapInh:\t0000000000000000\nCapPrm:\t0000000000000000\nCapEff:\t0000000000000000\nCapBnd:\t00000000a80425fb\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t2\nSeccomp_filters:\t1\nSpeculation_Store_Bypass:\tthread vulnerable\nSpeculationIndirectBranch:\tconditional enabled\nCpus_allowed:\tff\nCpus_allowed_list:\t0-7\nMems_allowed:\t00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000001\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t144363\nnonvoluntary_ctxt_switches:\t1147\nx86_Thread_features:\t\nx86_Thread_features_locked:\t\n", "stderr": "", "aggregated_output": "rchar: 41163991\nwchar: 0\nsyscr: 2131\nsyscw: 0\nread_bytes: 14568108032\nwrite_bytes: 0\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t769\nNgid:\t0\nPid:\t769\nPPid:\t768\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t769\nNSpid:\t769\nNSpgid:\t768\nNSsid:\t768\nKthread:\t0\nVmPeak:\t 7352564 kB\nVmSize:\t 7065464 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5323744 kB\nVmRSS:\t 3519908 kB\nRssAnon:\t 880712 kB\nRssFile:\t 2639196 kB\nRssShmem:\t 0 kB\nVmData:\t 1373444 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 11944 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\nCoreDumping:\t0\nTHP_enabled:\t1\nuntag_mask:\t0xffffffffffffffff\nThreads:\t13\nSigQ:\t0/56399\nSigPnd:\t0000000000000000\nShdPnd:\t0000000000000000\nSigBlk:\t0000000000000000\nSigIgn:\t0000000001001000\nSigCgt:\t0000000100000002\nCapInh:\t0000000000000000\nCapPrm:\t0000000000000000\nCapEff:\t0000000000000000\nCapBnd:\t00000000a80425fb\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t2\nSeccomp_filters:\t1\nSpeculation_Store_Bypass:\tthread vulnerable\nSpeculationIndirectBranch:\tconditional enabled\nCpus_allowed:\tff\nCpus_allowed_list:\t0-7\nMems_allowed:\t00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000001\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t144363\nnonvoluntary_ctxt_switches:\t1147\nx86_Thread_features:\t\nx86_Thread_features_locked:\t\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3680 }, "formatted_output": "rchar: 41163991\nwchar: 0\nsyscr: 2131\nsyscw: 0\nread_bytes: 14568108032\nwrite_bytes: 0\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t769\nNgid:\t0\nPid:\t769\nPPid:\t768\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t769\nNSpid:\t769\nNSpgid:\t768\nNSsid:\t768\nKthread:\t0\nVmPeak:\t 7352564 kB\nVmSize:\t 7065464 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 5323744 kB\nVmRSS:\t 3519908 kB\nRssAnon:\t 880712 kB\nRssFile:\t 2639196 kB\nRssShmem:\t 0 kB\nVmData:\t 1373444 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 11944 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\nCoreDumping:\t0\nTHP_enabled:\t1\nuntag_mask:\t0xffffffffffffffff\nThreads:\t13\nSigQ:\t0/56399\nSigPnd:\t0000000000000000\nShdPnd:\t0000000000000000\nSigBlk:\t0000000000000000\nSigIgn:\t0000000001001000\nSigCgt:\t0000000100000002\nCapInh:\t0000000000000000\nCapPrm:\t0000000000000000\nCapEff:\t0000000000000000\nCapBnd:\t00000000a80425fb\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t2\nSeccomp_filters:\t1\nSpeculation_Store_Bypass:\tthread vulnerable\nSpeculationIndirectBranch:\tconditional enabled\nCpus_allowed:\tff\nCpus_allowed_list:\t0-7\nMems_allowed:\t00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000001\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t144363\nnonvoluntary_ctxt_switches:\t1147\nx86_Thread_features:\t\nx86_Thread_features_locked:\t\n" } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-23T05:35:13.852Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:13.852Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:35:23.766Z", "callId": "call_eNlIXcurlnQWgGxYXoI1WavC", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill 769\\npython - <<'PY'\\nfrom pathlib import Path\\nfor f in ['fit_ease.py','fit_models.py','fit_context.py','fit_likes.py','fit_knn.py','fit_item.py']:\\n p=Path('/app/solution')/f;s=p.read_text().replace(\\\"np.save(S+name+'.npy',scores)\\\",\\\"np.save(S+name+'.npy',np.ascontiguousarray(scores))\\\");p.write_text(s)\\nfor f in ['tune.py','make_features.py']:\\n p=Path('/app/solution')/f;s=p.read_text();needle=\\\"models=[np.load(\\\"\\n pos=s.index(needle)\\n code=\\\"for n in a.models:\\\\n path=S+\\\"+(\\\"a.stage\\\" if f=='make_features.py' else \\\"'validation'\\\")+\\\"+'_'+n+'.npy'\\\\n mm=np.load(path,mmap_mode='r');ff=mm.flags.f_contiguous;del mm\\\\n if ff:\\\\n mm=np.load(path);np.save(path+'.tmp.npy',np.ascontiguousarray(mm));del mm;os.replace(path+'.tmp.npy',path);gc.collect()\\\\n\\\"\\n if f=='tune.py':s=s.replace('import argparse','import argparse,gc')\\n s=s[:s.index(needle)]+code+s[s.index(needle):];p.write_text(s)\\nPY\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:35:24.935Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"bbd018\",\"wall_time_seconds\":1.001204768,\"session_id\":33085,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:35:23.853Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab364fee97087d2bce7fc49c4425819", "usage": { "input_tokens": 52026, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 910, "reasoning_output_tokens": 516, "total_tokens": 52936 }, "turn_token_usage": { "input_tokens": 1609310, "cached_input_tokens": 1561984, "cache_write_input_tokens": 0, "output_tokens": 22934, "reasoning_output_tokens": 8834, "total_tokens": 1632244 }, "thread_token_usage": { "input_tokens": 1609310, "cached_input_tokens": 1561984, "cache_write_input_tokens": 0, "output_tokens": 22934, "reasoning_output_tokens": 8834, "total_tokens": 1632244 } } }, { "kind": "context", "timestamp": "2026-09-23T05:35:23.861Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-94522523-ddab-4f3c-9ab3-fe0dfaef93bd", "process_id": "42145", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 192, "nanos": 426387321 }, "formatted_output": "" } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-23T05:35:33.475Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:33.475Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:35:34.445Z", "callId": "call_ioaJM1Lyy08ew8LMrTzdT3SZ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log /app/solution/blend2.log\",\"max_output_tokens\":3000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:35:34.695Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"10e28d\",\"wall_time_seconds\":0.00000351,\"exit_code\":0,\"original_token_count\":39,\"output\":\"past_log20r20 matrix 5350589 time 7.547338008880615\\nfinished 29.535582542419434\\ndone past_likes0 17.385621309280396\\ndone past_likes0.25 31.485761880874634\\n\"}\n{\"current_time\":\"2026-09-23 05:35:34 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:35:34.518Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3651d2eec87d298ae11f86f2db602", "usage": { "input_tokens": 52993, "cached_input_tokens": 51840, "cache_write_input_tokens": 0, "output_tokens": 243, "reasoning_output_tokens": 172, "total_tokens": 53236 }, "turn_token_usage": { "input_tokens": 1662303, "cached_input_tokens": 1613824, "cache_write_input_tokens": 0, "output_tokens": 23177, "reasoning_output_tokens": 9006, "total_tokens": 1685480 }, "thread_token_usage": { "input_tokens": 1662303, "cached_input_tokens": 1613824, "cache_write_input_tokens": 0, "output_tokens": 23177, "reasoning_output_tokens": 9006, "total_tokens": 1685480 } } }, { "kind": "context", "timestamp": "2026-09-23T05:35:34.566Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cef00592-4db0-4072-868f-97238d2254dd", "process_id": "88888", "command": [ "/bin/sh", "-lc", "cat /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log /app/solution/blend2.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/past_log.log /app/solution/past_likes.log /app/solution/past_features.log /app/solution/blend2.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "past_log20r20 matrix 5350589 time 7.547338008880615\nfinished 29.535582542419434\ndone past_likes0 17.385621309280396\ndone past_likes0.25 31.485761880874634\n", "stderr": "", "aggregated_output": "past_log20r20 matrix 5350589 time 7.547338008880615\nfinished 29.535582542419434\ndone past_likes0 17.385621309280396\ndone past_likes0.25 31.485761880874634\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3510 }, "formatted_output": "past_log20r20 matrix 5350589 time 7.547338008880615\nfinished 29.535582542419434\ndone past_likes0 17.385621309280396\ndone past_likes0.25 31.485761880874634\n" } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-23T05:36:00.143Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:00.143Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:00.631Z", "callId": "call_HOsg3jbmoV3ybxXNmsXFJlwg", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:36:00.690Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"f8e8f9\",\"wall_time_seconds\":0.00000408,\"exit_code\":1,\"original_token_count\":48,\"output\":\"cat: /app/solution/validation_features.log: No such file or directory\\ncat: /app/solution/rerank.log: No such file or directory\\ncat: /app/solution/test_context.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:00.678Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2aad5dcd-60f4-4044-b749-dab847ebc6bc", "process_id": "4617", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/validation_features.log: No such file or directory\ncat: /app/solution/rerank.log: No such file or directory\ncat: /app/solution/test_context.log: No such file or directory\n", "stderr": "", "aggregated_output": "cat: /app/solution/validation_features.log: No such file or directory\ncat: /app/solution/rerank.log: No such file or directory\ncat: /app/solution/test_context.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "cat: /app/solution/validation_features.log: No such file or directory\ncat: /app/solution/rerank.log: No such file or directory\ncat: /app/solution/test_context.log: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:00.685Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3652724c487d2b9edb49a4a146e12", "usage": { "input_tokens": 53372, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 584, "reasoning_output_tokens": 516, "total_tokens": 53956 }, "turn_token_usage": { "input_tokens": 1715675, "cached_input_tokens": 1666688, "cache_write_input_tokens": 0, "output_tokens": 23761, "reasoning_output_tokens": 9522, "total_tokens": 1739436 }, "thread_token_usage": { "input_tokens": 1715675, "cached_input_tokens": 1666688, "cache_write_input_tokens": 0, "output_tokens": 23761, "reasoning_output_tokens": 9522, "total_tokens": 1739436 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-23T05:36:07.314Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:07.314Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:36:08.609Z", "text": "I found and fixed a storage-layout issue that was slowing the large score comparisons. The saved submission is intact; the earlier-week reranker and the stronger model blend are still being evaluated.\n" } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-23T05:36:13.160Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:13.160Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:18.526Z", "callId": "call_1Pz279DmePYhBGGkXsUKpGEM", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,os\\nfor n in ['validation_context30','validation_context7','validation_log20r20','validation_likes0','validation_w60','past_context30','past_log20r20','past_likes0']:\\n p='/app/solution/'+n+'.npy';a=np.load(p,mmap_mode='r');print(n,'C',a.flags.c_contiguous)\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n x=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\\n if x.startswith('/opt/task-venv/bin/python'):print(p,x)\\n except OSError:pass\\nPY\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:36:19.259Z", "content": "Script completed\nWall time 0.7 seconds\nOutput:\n\n{\"chunk_id\":\"6b9c9a\",\"wall_time_seconds\":0.277127134,\"exit_code\":0,\"original_token_count\":114,\"output\":\"validation_context30 C True\\nvalidation_context7 C True\\nvalidation_log20r20 C True\\nvalidation_likes0 C True\\nvalidation_w60 C False\\npast_context30 C False\\npast_log20r20 C False\\npast_likes0 C False\\n932 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 \\n967 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 \\n1020 /opt/task-venv/bin/python - \\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:18.601Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36541017887d2b19d60cec843c2a5", "usage": { "input_tokens": 54064, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 462, "reasoning_output_tokens": 218, "total_tokens": 54526 }, "turn_token_usage": { "input_tokens": 1769739, "cached_input_tokens": 1719936, "cache_write_input_tokens": 0, "output_tokens": 24223, "reasoning_output_tokens": 9740, "total_tokens": 1793962 }, "thread_token_usage": { "input_tokens": 1769739, "cached_input_tokens": 1719936, "cache_write_input_tokens": 0, "output_tokens": 24223, "reasoning_output_tokens": 9740, "total_tokens": 1793962 } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:19.101Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-64e562fd-c572-46bf-8063-ac988d70ae69", "process_id": "3972", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,os\nfor n in ['validation_context30','validation_context7','validation_log20r20','validation_likes0','validation_w60','past_context30','past_log20r20','past_likes0']:\n p='/app/solution/'+n+'.npy';a=np.load(p,mmap_mode='r');print(n,'C',a.flags.c_contiguous)\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n x=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if x.startswith('/opt/task-venv/bin/python'):print(p,x)\n except OSError:pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,os\nfor n in ['validation_context30','validation_context7','validation_log20r20','validation_likes0','validation_w60','past_context30','past_log20r20','past_likes0']:\n p='/app/solution/'+n+'.npy';a=np.load(p,mmap_mode='r');print(n,'C',a.flags.c_contiguous)\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n x=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if x.startswith('/opt/task-venv/bin/python'):print(p,x)\n except OSError:pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_context30 C True\nvalidation_context7 C True\nvalidation_log20r20 C True\nvalidation_likes0 C True\nvalidation_w60 C False\npast_context30 C False\npast_log20r20 C False\npast_likes0 C False\n932 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 \n967 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 \n1020 /opt/task-venv/bin/python - \n", "stderr": "", "aggregated_output": "validation_context30 C True\nvalidation_context7 C True\nvalidation_log20r20 C True\nvalidation_likes0 C True\nvalidation_w60 C False\npast_context30 C False\npast_log20r20 C False\npast_likes0 C False\n932 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 \n967 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 \n1020 /opt/task-venv/bin/python - \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 277121154 }, "formatted_output": "validation_context30 C True\nvalidation_context7 C True\nvalidation_log20r20 C True\nvalidation_likes0 C True\nvalidation_w60 C False\npast_context30 C False\npast_log20r20 C False\npast_likes0 C False\n932 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 \n967 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 \n1020 /opt/task-venv/bin/python - \n" } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-23T05:36:26.048Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:26.048Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:31.404Z", "callId": "call_wVhn0zs5rT1LW5MCk5Vn5yXq", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill 818 932\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/blend2.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:36:32.909Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"3715a3\",\"wall_time_seconds\":1.000941077,\"session_id\":42531,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"ce7c49\",\"wall_time_seconds\":0.00000345,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:31.578Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36553a0e087d28d09047cfddc4c6b", "usage": { "input_tokens": 54719, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 345, "reasoning_output_tokens": 125, "total_tokens": 55064 }, "turn_token_usage": { "input_tokens": 1824458, "cached_input_tokens": 1773824, "cache_write_input_tokens": 0, "output_tokens": 24568, "reasoning_output_tokens": 9865, "total_tokens": 1849026 }, "thread_token_usage": { "input_tokens": 1824458, "cached_input_tokens": 1773824, "cache_write_input_tokens": 0, "output_tokens": 24568, "reasoning_output_tokens": 9865, "total_tokens": 1849026 } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:31.610Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-96deb54a-4353-4026-a161-5945716dc265", "process_id": "89481", "command": [ "/bin/sh", "-lc", "while ! rg -q SCORE /app/solution/log20r80.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage past --contexts 30 > /app/solution/past_context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --stage past --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/past_log.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage past > /app/solution/past_likes.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "while ! rg -q SCORE /app/solution/log20r80.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage past --contexts 30 > /app/solution/past_context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_models.py --stage past --label log20r20 --days 20 --like 0 --organic 1 --reg 20 > /app/solution/past_log.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage past > /app/solution/past_likes.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 207, "nanos": 9873068 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:32.798Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8ac341ee-3a27-405f-9c03-4c68894c485d", "process_id": "18550", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/blend2.log", "name": "blend2.log", "path": "/app/solution/blend2.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3450 }, "formatted_output": "" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-23T05:36:48.027Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:48.027Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:51.621Z", "callId": "call_OjV5F5oBCFbEkQrvIz9q4SWB", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/solution')\\nfrom common import *\\nsave_ranking(np.load(S+'validation_context30.npy',mmap_mode='r'),'validation','validation')\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:36:52.788Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5f5052\",\"wall_time_seconds\":1.001490232,\"session_id\":37173,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:51.699Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36561274c87d2965946239ed1ba43", "usage": { "input_tokens": 55156, "cached_input_tokens": 54528, "cache_write_input_tokens": 0, "output_tokens": 514, "reasoning_output_tokens": 379, "total_tokens": 55670 }, "turn_token_usage": { "input_tokens": 1879614, "cached_input_tokens": 1828352, "cache_write_input_tokens": 0, "output_tokens": 25082, "reasoning_output_tokens": 10244, "total_tokens": 1904696 }, "thread_token_usage": { "input_tokens": 1879614, "cached_input_tokens": 1828352, "cache_write_input_tokens": 0, "output_tokens": 25082, "reasoning_output_tokens": 10244, "total_tokens": 1904696 } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-23T05:36:56.742Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:56.742Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:58.330Z", "callId": "call_AGfehsjYSapfN58hcXH84lko", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:36:58.462Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2fd254\",\"wall_time_seconds\":0.00000369,\"exit_code\":0,\"original_token_count\":36,\"output\":\"cat: /app/solution/public_evaluation.json: No such file or directory\\nlow 0\\nhigh 0\\nmax 25744\\noom 1\\noom_kill 1\\noom_group_kill 0\\nsock_throttled 8\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:58.382Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5fef966d-ec9c-4bfd-bddf-f89dbdf99073", "process_id": "76995", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cat: /app/solution/public_evaluation.json: No such file or directory\nlow 0\nhigh 0\nmax 25744\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n", "stderr": "", "aggregated_output": "cat: /app/solution/public_evaluation.json: No such file or directory\nlow 0\nhigh 0\nmax 25744\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3690 }, "formatted_output": "cat: /app/solution/public_evaluation.json: No such file or directory\nlow 0\nhigh 0\nmax 25744\noom 1\noom_kill 1\noom_group_kill 0\nsock_throttled 8\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:58.454Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3657506ec87d2bf94d1c8e994e634", "usage": { "input_tokens": 55728, "cached_input_tokens": 55040, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 60, "total_tokens": 55851 }, "turn_token_usage": { "input_tokens": 1935342, "cached_input_tokens": 1883392, "cache_write_input_tokens": 0, "output_tokens": 25205, "reasoning_output_tokens": 10304, "total_tokens": 1960547 }, "thread_token_usage": { "input_tokens": 1935342, "cached_input_tokens": 1883392, "cache_write_input_tokens": 0, "output_tokens": 25205, "reasoning_output_tokens": 10304, "total_tokens": 1960547 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-23T05:37:09.935Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:09.935Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:37:10.610Z", "callId": "call_dJoE501yQAnmz4WRAyxqevUN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -lh /app/solution/*tmp* /app/solution/*features* /app/solution/blend2*; cat /proc/967/io /proc/967/status | head -40\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:37:25.232Z", "content": "Script completed\nWall time 14.5 seconds\nOutput:\n\n{\"chunk_id\":\"906bfb\",\"wall_time_seconds\":0.00000774,\"exit_code\":0,\"original_token_count\":242,\"output\":\"ls: cannot access '/app/solution/*tmp*': No such file or directory\\n-rw-r--r-- 1 agent agent 0 Sep 23 05:35 /app/solution/blend2.log\\n-rw-r--r-- 1 agent agent 4.2K Sep 23 05:35 /app/solution/make_features.py\\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\\nrchar: 5114827608\\nwchar: 5073600640\\nsyscr: 2166\\nsyscw: 15\\nread_bytes: 4705910784\\nwrite_bytes: 5073612800\\ncancelled_write_bytes: 0\\nName:\\tpython\\nUmask:\\t0022\\nState:\\tD (disk sleep)\\nTgid:\\t967\\nNgid:\\t0\\nPid:\\t967\\nPPid:\\t963\\nTracerPid:\\t0\\nUid:\\t1000\\t1000\\t1000\\t1000\\nGid:\\t1000\\t1000\\t1000\\t1000\\nFDSize:\\t64\\nGroups:\\t1000 \\nNStgid:\\t967\\nNSpid:\\t967\\nNSpgid:\\t963\\nNSsid:\\t963\\nKthread:\\t0\\nVmPeak:\\t 7351428 kB\\nVmSize:\\t 7076816 kB\\nVmLck:\\t 0 kB\\nVmPin:\\t 0 kB\\nVmHWM:\\t 2112596 kB\\nVmRSS:\\t 984816 kB\\nRssAnon:\\t 891976 kB\\nRssFile:\\t 92840 kB\\nRssShmem:\\t 0 kB\\nVmData:\\t 1386832 kB\\nVmStk:\\t 140 kB\\nVmExe:\\t 4 kB\\nVmLib:\\t 140624 kB\\nVmPTE:\\t 2332 kB\\nVmSwap:\\t 0 kB\\nHugetlbPages:\\t 0 kB\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:37:10.679Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3657aab4c87d2b13f609467ff3119", "usage": { "input_tokens": 55963, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 343, "reasoning_output_tokens": 274, "total_tokens": 56306 }, "turn_token_usage": { "input_tokens": 1991305, "cached_input_tokens": 1938944, "cache_write_input_tokens": 0, "output_tokens": 25548, "reasoning_output_tokens": 10578, "total_tokens": 2016853 }, "thread_token_usage": { "input_tokens": 1991305, "cached_input_tokens": 1938944, "cache_write_input_tokens": 0, "output_tokens": 25548, "reasoning_output_tokens": 10578, "total_tokens": 2016853 } } }, { "kind": "context", "timestamp": "2026-09-23T05:37:11.242Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-54596ecc-044c-484f-bfe1-bcd257c918b1", "process_id": "11501", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/*tmp* /app/solution/*features* /app/solution/blend2*; cat /proc/967/io /proc/967/status | head -40" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/*tmp* /app/solution/*features* /app/solution/blend2*; cat /proc/967/io /proc/967/status | head -40" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ls: cannot access '/app/solution/*tmp*': No such file or directory\n-rw-r--r-- 1 agent agent 0 Sep 23 05:35 /app/solution/blend2.log\n-rw-r--r-- 1 agent agent 4.2K Sep 23 05:35 /app/solution/make_features.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\nrchar: 5114827608\nwchar: 5073600640\nsyscr: 2166\nsyscw: 15\nread_bytes: 4705910784\nwrite_bytes: 5073612800\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t967\nNgid:\t0\nPid:\t967\nPPid:\t963\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t967\nNSpid:\t967\nNSpgid:\t963\nNSsid:\t963\nKthread:\t0\nVmPeak:\t 7351428 kB\nVmSize:\t 7076816 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 2112596 kB\nVmRSS:\t 984816 kB\nRssAnon:\t 891976 kB\nRssFile:\t 92840 kB\nRssShmem:\t 0 kB\nVmData:\t 1386832 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 2332 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\n", "stderr": "", "aggregated_output": "ls: cannot access '/app/solution/*tmp*': No such file or directory\n-rw-r--r-- 1 agent agent 0 Sep 23 05:35 /app/solution/blend2.log\n-rw-r--r-- 1 agent agent 4.2K Sep 23 05:35 /app/solution/make_features.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\nrchar: 5114827608\nwchar: 5073600640\nsyscr: 2166\nsyscw: 15\nread_bytes: 4705910784\nwrite_bytes: 5073612800\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t967\nNgid:\t0\nPid:\t967\nPPid:\t963\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t967\nNSpid:\t967\nNSpgid:\t963\nNSsid:\t963\nKthread:\t0\nVmPeak:\t 7351428 kB\nVmSize:\t 7076816 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 2112596 kB\nVmRSS:\t 984816 kB\nRssAnon:\t 891976 kB\nRssFile:\t 92840 kB\nRssShmem:\t 0 kB\nVmData:\t 1386832 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 2332 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7740 }, "formatted_output": "ls: cannot access '/app/solution/*tmp*': No such file or directory\n-rw-r--r-- 1 agent agent 0 Sep 23 05:35 /app/solution/blend2.log\n-rw-r--r-- 1 agent agent 4.2K Sep 23 05:35 /app/solution/make_features.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\nrchar: 5114827608\nwchar: 5073600640\nsyscr: 2166\nsyscw: 15\nread_bytes: 4705910784\nwrite_bytes: 5073612800\ncancelled_write_bytes: 0\nName:\tpython\nUmask:\t0022\nState:\tD (disk sleep)\nTgid:\t967\nNgid:\t0\nPid:\t967\nPPid:\t963\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t967\nNSpid:\t967\nNSpgid:\t963\nNSsid:\t963\nKthread:\t0\nVmPeak:\t 7351428 kB\nVmSize:\t 7076816 kB\nVmLck:\t 0 kB\nVmPin:\t 0 kB\nVmHWM:\t 2112596 kB\nVmRSS:\t 984816 kB\nRssAnon:\t 891976 kB\nRssFile:\t 92840 kB\nRssShmem:\t 0 kB\nVmData:\t 1386832 kB\nVmStk:\t 140 kB\nVmExe:\t 4 kB\nVmLib:\t 140624 kB\nVmPTE:\t 2332 kB\nVmSwap:\t 0 kB\nHugetlbPages:\t 0 kB\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:37:28.465Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3e058714-b482-448e-ab2b-84bfa8fb02b6", "process_id": "37173", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/app/solution')\nfrom common import *\nsave_ranking(np.load(S+'validation_context30.npy',mmap_mode='r'),'validation','validation')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/app/solution')\nfrom common import *\nsave_ranking(np.load(S+'validation_context30.npy',mmap_mode='r'),'validation','validation')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved validation 845600\n", "stderr": "", "aggregated_output": "saved validation 845600\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 670797446 }, "formatted_output": "saved validation 845600\n" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-23T05:37:46.417Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:46.417Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:37:55.999Z", "callId": "call_g41KfHGNusBwjcG1M0XEOr1f", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/rerank.py');s=p.read_text()\\ns=s.replace(\\\"base=val['base'][:,0]*.5+val['base'][:,1]*.5\\\\n print('base score',ev(base,val),flush=True)\\\",\\\"bestbase=(-1,None)\\\\n for w0 in [0,.25,.5,.75,1]:\\\\n for w2 in [0,.1,.25,.5]:\\\\n if w0+w2>1:continue\\\\n ww=np.array([w0,1-w0-w2,w2]);zz=val['base']@ww;vv=ev(zz,val)\\\\n if vv>bestbase[0]:bestbase=(vv,ww)\\\\n base=val['base']@bestbase[1]\\\\n json.dump({'weights':bestbase[1].tolist(),'score':bestbase[0]},open(S+'base_weights.json','w'))\\\\n save(base,val,'validation','validation')\\\\n print('base score',bestbase,flush=True)\\\")\\ns=s.replace(\\\"base=test['base'][:,0]*.5+test['base'][:,1]*.5\\\", \\\"base=test['base']@np.array(json.load(open(S+'base_weights.json'))['weights'])\\\")\\np.write_text(s)\\nPY\\ncat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:37:56.616Z", "content": "Script completed\nWall time 0.5 seconds\nOutput:\n\n{\"chunk_id\":\"22bd5d\",\"wall_time_seconds\":0.00000376,\"exit_code\":0,\"original_token_count\":32,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.036458,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:37:56.100Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36595c57c87d2840f7d71c7a90a3b", "usage": { "input_tokens": 56831, "cached_input_tokens": 55808, "cache_write_input_tokens": 0, "output_tokens": 866, "reasoning_output_tokens": 516, "total_tokens": 57697 }, "turn_token_usage": { "input_tokens": 2048136, "cached_input_tokens": 1994752, "cache_write_input_tokens": 0, "output_tokens": 26414, "reasoning_output_tokens": 11094, "total_tokens": 2074550 }, "thread_token_usage": { "input_tokens": 2048136, "cached_input_tokens": 1994752, "cache_write_input_tokens": 0, "output_tokens": 26414, "reasoning_output_tokens": 11094, "total_tokens": 2074550 } } }, { "kind": "context", "timestamp": "2026-09-23T05:37:56.494Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-30c623f9-e0a5-4468-978b-cd3b5b535abf", "process_id": "2980", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text()\ns=s.replace(\"base=val['base'][:,0]*.5+val['base'][:,1]*.5\\n print('base score',ev(base,val),flush=True)\",\"bestbase=(-1,None)\\n for w0 in [0,.25,.5,.75,1]:\\n for w2 in [0,.1,.25,.5]:\\n if w0+w2>1:continue\\n ww=np.array([w0,1-w0-w2,w2]);zz=val['base']@ww;vv=ev(zz,val)\\n if vv>bestbase[0]:bestbase=(vv,ww)\\n base=val['base']@bestbase[1]\\n json.dump({'weights':bestbase[1].tolist(),'score':bestbase[0]},open(S+'base_weights.json','w'))\\n save(base,val,'validation','validation')\\n print('base score',bestbase,flush=True)\")\ns=s.replace(\"base=test['base'][:,0]*.5+test['base'][:,1]*.5\", \"base=test['base']@np.array(json.load(open(S+'base_weights.json'))['weights'])\")\np.write_text(s)\nPY\ncat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text()\ns=s.replace(\"base=val['base'][:,0]*.5+val['base'][:,1]*.5\\n print('base score',ev(base,val),flush=True)\",\"bestbase=(-1,None)\\n for w0 in [0,.25,.5,.75,1]:\\n for w2 in [0,.1,.25,.5]:\\n if w0+w2>1:continue\\n ww=np.array([w0,1-w0-w2,w2]);zz=val['base']@ww;vv=ev(zz,val)\\n if vv>bestbase[0]:bestbase=(vv,ww)\\n base=val['base']@bestbase[1]\\n json.dump({'weights':bestbase[1].tolist(),'score':bestbase[0]},open(S+'base_weights.json','w'))\\n save(base,val,'validation','validation')\\n print('base score',bestbase,flush=True)\")\ns=s.replace(\"base=test['base'][:,0]*.5+test['base'][:,1]*.5\", \"base=test['base']@np.array(json.load(open(S+'base_weights.json'))['weights'])\")\np.write_text(s)\nPY\ncat /app/solution/blend2.log /app/solution/past_features.log /app/solution/public_evaluation.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036458,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036458,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3760 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036458,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-23T05:37:58.734Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:58.734Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:00.489Z", "callId": "call_R5PRd70Ann2Z5uCaMYXvYJH2", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/past_features.log /app/solution/blend2.log /app/solution/test_context.log /app/solution/validation_features.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:38:00.742Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"current_time\":\"2026-09-23 05:38:00 UTC\"}\n{\"chunk_id\":\"50fd8b\",\"wall_time_seconds\":0.00000565,\"exit_code\":1,\"original_token_count\":34,\"output\":\"cat: /app/solution/test_context.log: No such file or directory\\ncat: /app/solution/validation_features.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:38:00.596Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab365b4e60487d2855473dd3468f559", "usage": { "input_tokens": 57805, "cached_input_tokens": 56704, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 13, "total_tokens": 57887 }, "turn_token_usage": { "input_tokens": 2105941, "cached_input_tokens": 2051456, "cache_write_input_tokens": 0, "output_tokens": 26496, "reasoning_output_tokens": 11107, "total_tokens": 2132437 }, "thread_token_usage": { "input_tokens": 2105941, "cached_input_tokens": 2051456, "cache_write_input_tokens": 0, "output_tokens": 26496, "reasoning_output_tokens": 11107, "total_tokens": 2132437 } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:00.631Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d0eb357a-d579-4531-8eb4-e3bbf4600e6a", "process_id": "91768", "command": [ "/bin/sh", "-lc", "cat /app/solution/past_features.log /app/solution/blend2.log /app/solution/test_context.log /app/solution/validation_features.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/past_features.log /app/solution/blend2.log /app/solution/test_context.log /app/solution/validation_features.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/test_context.log: No such file or directory\ncat: /app/solution/validation_features.log: No such file or directory\n", "stderr": "", "aggregated_output": "cat: /app/solution/test_context.log: No such file or directory\ncat: /app/solution/validation_features.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5650 }, "formatted_output": "cat: /app/solution/test_context.log: No such file or directory\ncat: /app/solution/validation_features.log: No such file or directory\n" } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-23T05:38:16.577Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:16.577Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:17.886Z", "callId": "call_kDKMNHL2mK5cupDQcFfP0rKo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /proc/967/io; ls -lh /app/solution/past*; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:38:17.984Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"c823cf\",\"wall_time_seconds\":0.00000428,\"exit_code\":0,\"original_token_count\":203,\"output\":\"rchar: 5114827608\\nwchar: 5209211902\\nsyscr: 2166\\nsyscw: 50\\nread_bytes: 7452766208\\nwrite_bytes: 5209227264\\ncancelled_write_bytes: 0\\n-rw-r--r-- 1 agent agent 39 Sep 23 05:33 /app/solution/past_context.log\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_context30.npy\\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\\n-rw-r--r-- 1 agent agent 75 Sep 23 05:34 /app/solution/past_likes.log\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:34 /app/solution/past_likes0.25.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_likes0.npy\\n-rw-r--r-- 1 agent agent 80 Sep 23 05:34 /app/solution/past_log.log\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_log20r20.npy\\n-rw-r--r-- 1 agent agent 30M Sep 23 05:34 /app/solution/past_log20r20_X.npz\\n8417746944\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:38:17.938Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e66f66a2-8341-48b1-8898-087095bd57f2", "process_id": "93394", "command": [ "/bin/sh", "-lc", "cat /proc/967/io; ls -lh /app/solution/past*; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /proc/967/io; ls -lh /app/solution/past*; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rchar: 5114827608\nwchar: 5209211902\nsyscr: 2166\nsyscw: 50\nread_bytes: 7452766208\nwrite_bytes: 5209227264\ncancelled_write_bytes: 0\n-rw-r--r-- 1 agent agent 39 Sep 23 05:33 /app/solution/past_context.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_context30.npy\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\n-rw-r--r-- 1 agent agent 75 Sep 23 05:34 /app/solution/past_likes.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:34 /app/solution/past_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_likes0.npy\n-rw-r--r-- 1 agent agent 80 Sep 23 05:34 /app/solution/past_log.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_log20r20.npy\n-rw-r--r-- 1 agent agent 30M Sep 23 05:34 /app/solution/past_log20r20_X.npz\n8417746944\n", "stderr": "", "aggregated_output": "rchar: 5114827608\nwchar: 5209211902\nsyscr: 2166\nsyscw: 50\nread_bytes: 7452766208\nwrite_bytes: 5209227264\ncancelled_write_bytes: 0\n-rw-r--r-- 1 agent agent 39 Sep 23 05:33 /app/solution/past_context.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_context30.npy\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\n-rw-r--r-- 1 agent agent 75 Sep 23 05:34 /app/solution/past_likes.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:34 /app/solution/past_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_likes0.npy\n-rw-r--r-- 1 agent agent 80 Sep 23 05:34 /app/solution/past_log.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_log20r20.npy\n-rw-r--r-- 1 agent agent 30M Sep 23 05:34 /app/solution/past_log20r20_X.npz\n8417746944\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4280 }, "formatted_output": "rchar: 5114827608\nwchar: 5209211902\nsyscr: 2166\nsyscw: 50\nread_bytes: 7452766208\nwrite_bytes: 5209227264\ncancelled_write_bytes: 0\n-rw-r--r-- 1 agent agent 39 Sep 23 05:33 /app/solution/past_context.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_context30.npy\n-rw-r--r-- 1 agent agent 0 Sep 23 05:36 /app/solution/past_features.log\n-rw-r--r-- 1 agent agent 75 Sep 23 05:34 /app/solution/past_likes.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:34 /app/solution/past_likes0.25.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_likes0.npy\n-rw-r--r-- 1 agent agent 80 Sep 23 05:34 /app/solution/past_log.log\n-rw-r--r-- 1 agent agent 968M Sep 23 05:37 /app/solution/past_log20r20.npy\n-rw-r--r-- 1 agent agent 30M Sep 23 05:34 /app/solution/past_log20r20_X.npz\n8417746944\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:17.979Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab365b90b0087d2a5c55c48cf14877e", "usage": { "input_tokens": 57994, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 431, "reasoning_output_tokens": 376, "total_tokens": 58425 }, "turn_token_usage": { "input_tokens": 2163935, "cached_input_tokens": 2109056, "cache_write_input_tokens": 0, "output_tokens": 26927, "reasoning_output_tokens": 11483, "total_tokens": 2190862 }, "thread_token_usage": { "input_tokens": 2163935, "cached_input_tokens": 2109056, "cache_write_input_tokens": 0, "output_tokens": 26927, "reasoning_output_tokens": 11483, "total_tokens": 2190862 } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:23.715Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b88a75e5-60a6-4f3a-a222-408774e1510a", "process_id": "33085", "command": [ "/bin/sh", "-lc", "kill 769\npython - <<'PY'\nfrom pathlib import Path\nfor f in ['fit_ease.py','fit_models.py','fit_context.py','fit_likes.py','fit_knn.py','fit_item.py']:\n p=Path('/app/solution')/f;s=p.read_text().replace(\"np.save(S+name+'.npy',scores)\",\"np.save(S+name+'.npy',np.ascontiguousarray(scores))\");p.write_text(s)\nfor f in ['tune.py','make_features.py']:\n p=Path('/app/solution')/f;s=p.read_text();needle=\"models=[np.load(\"\n pos=s.index(needle)\n code=\"for n in a.models:\\n path=S+\"+(\"a.stage\" if f=='make_features.py' else \"'validation'\")+\"+'_'+n+'.npy'\\n mm=np.load(path,mmap_mode='r');ff=mm.flags.f_contiguous;del mm\\n if ff:\\n mm=np.load(path);np.save(path+'.tmp.npy',np.ascontiguousarray(mm));del mm;os.replace(path+'.tmp.npy',path);gc.collect()\\n\"\n if f=='tune.py':s=s.replace('import argparse','import argparse,gc')\n s=s[:s.index(needle)]+code+s[s.index(needle):];p.write_text(s)\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 769\npython - <<'PY'\nfrom pathlib import Path\nfor f in ['fit_ease.py','fit_models.py','fit_context.py','fit_likes.py','fit_knn.py','fit_item.py']:\n p=Path('/app/solution')/f;s=p.read_text().replace(\"np.save(S+name+'.npy',scores)\",\"np.save(S+name+'.npy',np.ascontiguousarray(scores))\");p.write_text(s)\nfor f in ['tune.py','make_features.py']:\n p=Path('/app/solution')/f;s=p.read_text();needle=\"models=[np.load(\"\n pos=s.index(needle)\n code=\"for n in a.models:\\n path=S+\"+(\"a.stage\" if f=='make_features.py' else \"'validation'\")+\"+'_'+n+'.npy'\\n mm=np.load(path,mmap_mode='r');ff=mm.flags.f_contiguous;del mm\\n if ff:\\n mm=np.load(path);np.save(path+'.tmp.npy',np.ascontiguousarray(mm));del mm;os.replace(path+'.tmp.npy',path);gc.collect()\\n\"\n if f=='tune.py':s=s.replace('import argparse','import argparse,gc')\n s=s[:s.index(needle)]+code+s[s.index(needle):];p.write_text(s)\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --models context30 context7 log20r20 likes0 w60 --out blend2 > /app/solution/blend2.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 179, "nanos": 780684224 }, "formatted_output": "" } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-23T05:38:40.082Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:40.082Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:38:41.248Z", "text": "The official evaluator confirms 0.036458 NDCG@10 across 4,263 users with eligible relevant tracks. The reranker’s features are being built from cutoff-correct histories; validation events are used only as outcomes in this backtest.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:42.000Z", "callId": "call_nyOs5eKuj12teAnRmYLifomP", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:38:42.129Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"26b29b\",\"wall_time_seconds\":0.000004981,\"exit_code\":1,\"original_token_count\":240,\"output\":\"context30 0.03645826636235647\\ncontext7 0.035041491135804764\\nlog20r20 0.0342380820872635\\nlikes0 0.025455425317474127\\nw60 0.027506390295302774\\nbest 0.036489992641997517 [0.90909091 0.09090909 0. 0. 0. ]\\nbest 0.0370514198851899 [0.72727273 0.27272727 0. 0. 0. ]\\nbest 0.03721672896974353 [0.66115702 0.24793388 0.09090909 0. 0. ]\\nbest 0.03737411540166128 [0.52892562 0.19834711 0.27272727 0. 0. ]\\nbest 0.03842343770172469 [0.48084147 0.18031555 0.24793388 0.09090909 0. ]\\nbest 0.03946552805564147 [0.38467318 0.14425244 0.19834711 0.27272727 0. ]\\nbest 0.03966610843793256 [0.34970289 0.22204767 0.18031555 0.24793388 0. ]\\nFINAL (np.float64(0.03966610843793256), array([0.34970289, 0.22204767, 0.18031555, 0.24793388, 0. ]))\\nfeatures past (2165425, 55) positive 7896 likes 1032 time 116.1450560092926\\ncat: /app/solution/rerank.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:38:42.077Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab365cb10c087d2b97b5bcaa3664fc8", "usage": { "input_tokens": 58820, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 642, "reasoning_output_tokens": 516, "total_tokens": 59462 }, "turn_token_usage": { "input_tokens": 2222755, "cached_input_tokens": 2166912, "cache_write_input_tokens": 0, "output_tokens": 27569, "reasoning_output_tokens": 11999, "total_tokens": 2250324 }, "thread_token_usage": { "input_tokens": 2222755, "cached_input_tokens": 2166912, "cache_write_input_tokens": 0, "output_tokens": 27569, "reasoning_output_tokens": 11999, "total_tokens": 2250324 } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:42.102Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a65caced-5caf-4410-814b-51a2ce4bb867", "process_id": "19888", "command": [ "/bin/sh", "-lc", "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/blend2.log /app/solution/past_features.log /app/solution/validation_features.log /app/solution/rerank.log /app/solution/test_context.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "context30 0.03645826636235647\ncontext7 0.035041491135804764\nlog20r20 0.0342380820872635\nlikes0 0.025455425317474127\nw60 0.027506390295302774\nbest 0.036489992641997517 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.0370514198851899 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.03721672896974353 [0.66115702 0.24793388 0.09090909 0. 0. ]\nbest 0.03737411540166128 [0.52892562 0.19834711 0.27272727 0. 0. ]\nbest 0.03842343770172469 [0.48084147 0.18031555 0.24793388 0.09090909 0. ]\nbest 0.03946552805564147 [0.38467318 0.14425244 0.19834711 0.27272727 0. ]\nbest 0.03966610843793256 [0.34970289 0.22204767 0.18031555 0.24793388 0. ]\nFINAL (np.float64(0.03966610843793256), array([0.34970289, 0.22204767, 0.18031555, 0.24793388, 0. ]))\nfeatures past (2165425, 55) positive 7896 likes 1032 time 116.1450560092926\ncat: /app/solution/rerank.log: No such file or directory\n", "stderr": "", "aggregated_output": "context30 0.03645826636235647\ncontext7 0.035041491135804764\nlog20r20 0.0342380820872635\nlikes0 0.025455425317474127\nw60 0.027506390295302774\nbest 0.036489992641997517 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.0370514198851899 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.03721672896974353 [0.66115702 0.24793388 0.09090909 0. 0. ]\nbest 0.03737411540166128 [0.52892562 0.19834711 0.27272727 0. 0. ]\nbest 0.03842343770172469 [0.48084147 0.18031555 0.24793388 0.09090909 0. ]\nbest 0.03946552805564147 [0.38467318 0.14425244 0.19834711 0.27272727 0. ]\nbest 0.03966610843793256 [0.34970289 0.22204767 0.18031555 0.24793388 0. ]\nFINAL (np.float64(0.03966610843793256), array([0.34970289, 0.22204767, 0.18031555, 0.24793388, 0. ]))\nfeatures past (2165425, 55) positive 7896 likes 1032 time 116.1450560092926\ncat: /app/solution/rerank.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4981 }, "formatted_output": "context30 0.03645826636235647\ncontext7 0.035041491135804764\nlog20r20 0.0342380820872635\nlikes0 0.025455425317474127\nw60 0.027506390295302774\nbest 0.036489992641997517 [0.90909091 0.09090909 0. 0. 0. ]\nbest 0.0370514198851899 [0.72727273 0.27272727 0. 0. 0. ]\nbest 0.03721672896974353 [0.66115702 0.24793388 0.09090909 0. 0. ]\nbest 0.03737411540166128 [0.52892562 0.19834711 0.27272727 0. 0. ]\nbest 0.03842343770172469 [0.48084147 0.18031555 0.24793388 0.09090909 0. ]\nbest 0.03946552805564147 [0.38467318 0.14425244 0.19834711 0.27272727 0. ]\nbest 0.03966610843793256 [0.34970289 0.22204767 0.18031555 0.24793388 0. ]\nFINAL (np.float64(0.03966610843793256), array([0.34970289, 0.22204767, 0.18031555, 0.24793388, 0. ]))\nfeatures past (2165425, 55) positive 7896 likes 1032 time 116.1450560092926\ncat: /app/solution/rerank.log: No such file or directory\n" } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-23T05:38:51.669Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:51.669Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:59.960Z", "callId": "call_qlBX8nPl6pKoBg5taSZldrye", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/rerank.py');s=p.read_text();s=s.replace(\\\"weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\\\", \\\"keep=past['ID'][past['u']]>0\\\\n trainF=past['F'][keep];trainy=past['y'][keep]\\\\n weight=1/np.maximum(past['ID'][past['u'][keep]],1);weight/=weight.mean()\\\")\\ns=s.replace(\\\"model.fit(past['F'],past['y'],sample_weight=weight)\\\", \\\"model.fit(trainF,trainy,sample_weight=weight)\\\")\\ns=s.replace(\\\"w/=w.mean();del past,val;gc.collect()\\\", \\\"keep=np.r_[past['ID'][past['u']]>0,val['ID'][val['u']]>0];F=F[keep];y=y[keep];w=w[keep];w/=w.mean();del past,val;gc.collect()\\\")\\np.write_text(s)\\nPY\\ncat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:39:00.148Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"a33699\",\"wall_time_seconds\":0.00000314,\"exit_code\":1,\"original_token_count\":15,\"output\":\"cat: /app/solution/rerank.log: No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:00.065Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab365e25cf887d28fdcba6e85e9b164", "usage": { "input_tokens": 59952, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 495, "reasoning_output_tokens": 209, "total_tokens": 60447 }, "turn_token_usage": { "input_tokens": 2282707, "cached_input_tokens": 2225536, "cache_write_input_tokens": 0, "output_tokens": 28064, "reasoning_output_tokens": 12208, "total_tokens": 2310771 }, "thread_token_usage": { "input_tokens": 2282707, "cached_input_tokens": 2225536, "cache_write_input_tokens": 0, "output_tokens": 28064, "reasoning_output_tokens": 12208, "total_tokens": 2310771 } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:00.123Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-551d4027-c5ec-4fc7-b64c-cf685933fcd7", "process_id": "15612", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text();s=s.replace(\"weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\", \"keep=past['ID'][past['u']]>0\\n trainF=past['F'][keep];trainy=past['y'][keep]\\n weight=1/np.maximum(past['ID'][past['u'][keep]],1);weight/=weight.mean()\")\ns=s.replace(\"model.fit(past['F'],past['y'],sample_weight=weight)\", \"model.fit(trainF,trainy,sample_weight=weight)\")\ns=s.replace(\"w/=w.mean();del past,val;gc.collect()\", \"keep=np.r_[past['ID'][past['u']]>0,val['ID'][val['u']]>0];F=F[keep];y=y[keep];w=w[keep];w/=w.mean();del past,val;gc.collect()\")\np.write_text(s)\nPY\ncat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text();s=s.replace(\"weight=1/np.maximum(past['ID'][past['u']],1);weight/=weight.mean()\", \"keep=past['ID'][past['u']]>0\\n trainF=past['F'][keep];trainy=past['y'][keep]\\n weight=1/np.maximum(past['ID'][past['u'][keep]],1);weight/=weight.mean()\")\ns=s.replace(\"model.fit(past['F'],past['y'],sample_weight=weight)\", \"model.fit(trainF,trainy,sample_weight=weight)\")\ns=s.replace(\"w/=w.mean();del past,val;gc.collect()\", \"keep=np.r_[past['ID'][past['u']]>0,val['ID'][val['u']]>0];F=F[keep];y=y[keep];w=w[keep];w/=w.mean();del past,val;gc.collect()\")\np.write_text(s)\nPY\ncat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/rerank.log: No such file or directory\n", "stderr": "", "aggregated_output": "cat: /app/solution/rerank.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3140 }, "formatted_output": "cat: /app/solution/rerank.log: No such file or directory\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-23T05:39:18.326Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:18.326Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:39:24.156Z", "callId": "call_8BkcUsWtaVZd49kA2eBfyh0b", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/fit_context.py');s=p.read_text().replace(\\\"p.add_argument('--stage',default='validation');\\\", \\\"p.add_argument('--stage',default='validation');p.add_argument('--suffix',default='');\\\").replace(\\\"name=f'{a.stage}_context{days:g}'\\\", \\\"name=f'{a.stage}_context{days:g}{a.suffix}'\\\");p.write_text(s)\\nPY\\nwhile ! rg -q done /app/solution/test_context.log; do sleep 2; done\\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:39:25.835Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\n{\"chunk_id\":\"e483c4\",\"wall_time_seconds\":1.001135188,\"session_id\":69862,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:39:25 UTC\"}\n{\"chunk_id\":\"232f5f\",\"wall_time_seconds\":0.000004631,\"exit_code\":0,\"original_token_count\":48,\"output\":\"done test_context30 56.97712755203247\\nfeatures validation (2180650, 55) positive 7517 likes 925 time 34.71589708328247\\nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:24.257Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab365f4595c87d28d1ef7b3b4d5d496", "usage": { "input_tokens": 60519, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 746, "reasoning_output_tokens": 484, "total_tokens": 61265 }, "turn_token_usage": { "input_tokens": 2343226, "cached_input_tokens": 2285312, "cache_write_input_tokens": 0, "output_tokens": 28810, "reasoning_output_tokens": 12692, "total_tokens": 2372036 }, "thread_token_usage": { "input_tokens": 2343226, "cached_input_tokens": 2285312, "cache_write_input_tokens": 0, "output_tokens": 28810, "reasoning_output_tokens": 12692, "total_tokens": 2372036 } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:25.724Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-10afd939-d35d-4194-a6e7-cd4a3f14e470", "process_id": "88590", "command": [ "/bin/sh", "-lc", "cat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/test_context.log /app/solution/validation_features.log /app/solution/rerank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "done test_context30 56.97712755203247\nfeatures validation (2180650, 55) positive 7517 likes 925 time 34.71589708328247\nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\n", "stderr": "", "aggregated_output": "done test_context30 56.97712755203247\nfeatures validation (2180650, 55) positive 7517 likes 925 time 34.71589708328247\nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4631 }, "formatted_output": "done test_context30 56.97712755203247\nfeatures validation (2180650, 55) positive 7517 likes 925 time 34.71589708328247\nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:29.090Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d85d68d3-4530-4ff7-85cf-9602c3038143", "process_id": "69862", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/fit_context.py');s=p.read_text().replace(\"p.add_argument('--stage',default='validation');\", \"p.add_argument('--stage',default='validation');p.add_argument('--suffix',default='');\").replace(\"name=f'{a.stage}_context{days:g}'\", \"name=f'{a.stage}_context{days:g}{a.suffix}'\");p.write_text(s)\nPY\nwhile ! rg -q done /app/solution/test_context.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/fit_context.py');s=p.read_text().replace(\"p.add_argument('--stage',default='validation');\", \"p.add_argument('--stage',default='validation');p.add_argument('--suffix',default='');\").replace(\"name=f'{a.stage}_context{days:g}'\", \"name=f'{a.stage}_context{days:g}{a.suffix}'\");p.write_text(s)\nPY\nwhile ! rg -q done /app/solution/test_context.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 4, "nanos": 370872887 }, "formatted_output": "" } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-23T05:39:33.818Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:33.818Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:39:39.810Z", "callId": "call_lvKGIAA3bx7vEiEhgmFVCGfz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os,signal\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n c=open('/proc/'+p+'/cmdline','rb').read()\\n if c.startswith(b'/opt/task-venv/bin/python\\\\0') and b'--suffix\\\\0r80\\\\0' in c:os.kill(int(p),signal.SIGTERM)\\n except OSError:pass\\nPY\\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:39:41.384Z", "content": "Script completed\nWall time 1.4 seconds\nOutput:\n\n{\"chunk_id\":\"e0dbc2\",\"wall_time_seconds\":1.002486589,\"session_id\":55378,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:39.988Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3660e2e6487d2b2c50958319cfa81", "usage": { "input_tokens": 61448, "cached_input_tokens": 60288, "cache_write_input_tokens": 0, "output_tokens": 409, "reasoning_output_tokens": 200, "total_tokens": 61857 }, "turn_token_usage": { "input_tokens": 2404674, "cached_input_tokens": 2345600, "cache_write_input_tokens": 0, "output_tokens": 29219, "reasoning_output_tokens": 12892, "total_tokens": 2433893 }, "thread_token_usage": { "input_tokens": 2404674, "cached_input_tokens": 2345600, "cache_write_input_tokens": 0, "output_tokens": 29219, "reasoning_output_tokens": 12892, "total_tokens": 2433893 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-23T05:39:59.287Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:59.287Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:39:59.617Z", "text": "The best blend now scores 0.03967 NDCG@10. A separate like-prediction model added useful signal to the listening models. I’m comparing that blend with the earlier-week reranker before choosing the final ranking.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:40:21.665Z", "callId": "call_ViIfVrThgPAduC0xsD5LEjQi", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/finalize.py <<'PY'\\nfrom common import *\\nimport argparse,pickle,mmap,gc\\np=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--mode',default='base');p.add_argument('--name',default='submission');p.add_argument('--config',default='blend2');a=p.parse_args()\\nif a.mode=='dense':\\n c=json.load(open(S+a.config+'.json'));E=eligible(a.stage);out=[]\\n models=[(np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r'),w) for n,w in zip(c['models'],c['weights']) if w>0]\\n for st in range(0,len(U),64):\\n es=E[st:st+64];z=np.zeros(es.shape,np.float32)\\n for mm,w in models:\\n v=np.array(mm[st:st+len(es)]);mm._mmap.madvise(mmap.MADV_DONTNEED);v[~es]=-1e10;z+=w*v/np.max(v,axis=1).clip(1e-9)[:,None]\\n z[~es]=-np.inf;ix=np.argpartition(z,-100,axis=1)[:,-100:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(z,ix,axis=1),axis=1),axis=1);out.append(ix)\\n out=np.concatenate(out)\\nelse:\\n d=np.load(S+a.stage+'_features.npz');u=d['u'];i=d['i'];base=d['base']@np.array(json.load(open(S+'base_weights.json'))['weights']);z=base\\n if a.mode=='rerank':\\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+('final_reranker.pkl' if a.stage=='test' else 'reranker.pkl'),'rb'));pred=model.predict(d['F']);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(pred,starts).clip(1e-9);z=conf['blend']*(pred/mx[u])+(1-conf['blend'])*base\\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\\n for k in range(len(U)):\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\\n out=np.stack(out)\\nassert eligible(a.stage)[np.arange(len(U))[:,None],out].all()\\ndf=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+a.name+'.tmp.parquet',index=False);os.replace(S+a.name+'.tmp.parquet',S+a.name+'.parquet')\\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\\nvalidate_ranking(df,set(map(int,U)));print('Saved and validated',a.name,len(df),flush=True)\\nPY\\ncat /app/solution/rerank.log /app/solution/test_likes.log /app/solution/test_features.log /app/solution/context80.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:40:22.113Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n{\"chunk_id\":\"109cc2\",\"wall_time_seconds\":0.00000516,\"exit_code\":0,\"original_token_count\":124,\"output\":\"base score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\\nSCORE 100 0 0.03841819541703751\\nSCORE 100 0.25 0.043991123461385506\\nSCORE 100 0.5 0.047698627923777215\\nSCORE 100 0.75 0.04980313975424695\\nSCORE 100 1 0.050409065347536745\\nSCORE 200 0 0.03841819541703751\\nSCORE 200 0.25 0.04407037831861042\\nSCORE 200 0.5 0.048477567338242676\\nSCORE 200 0.75 0.04955680359975647\\nSCORE 200 1 0.04996905870509821\\ndone test_likes0 29.744168758392334\\ndone test_likes0.25 40.988404273986816\\nKilled\\n\"}\n{\"current_time\":\"2026-09-23 05:40:22 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:40:21.787Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3661dbe6087d287cb6daae2f73a3d", "usage": { "input_tokens": 61915, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 1285, "reasoning_output_tokens": 450, "total_tokens": 63200 }, "turn_token_usage": { "input_tokens": 2466589, "cached_input_tokens": 2406912, "cache_write_input_tokens": 0, "output_tokens": 30504, "reasoning_output_tokens": 13342, "total_tokens": 2497093 }, "thread_token_usage": { "input_tokens": 2466589, "cached_input_tokens": 2406912, "cache_write_input_tokens": 0, "output_tokens": 30504, "reasoning_output_tokens": 13342, "total_tokens": 2497093 } } }, { "kind": "context", "timestamp": "2026-09-23T05:40:21.899Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f5c30ba9-356f-4a22-af3b-ae3fa6a6ebfe", "process_id": "35149", "command": [ "/bin/sh", "-lc", "cat > /app/solution/finalize.py <<'PY'\nfrom common import *\nimport argparse,pickle,mmap,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--mode',default='base');p.add_argument('--name',default='submission');p.add_argument('--config',default='blend2');a=p.parse_args()\nif a.mode=='dense':\n c=json.load(open(S+a.config+'.json'));E=eligible(a.stage);out=[]\n models=[(np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r'),w) for n,w in zip(c['models'],c['weights']) if w>0]\n for st in range(0,len(U),64):\n es=E[st:st+64];z=np.zeros(es.shape,np.float32)\n for mm,w in models:\n v=np.array(mm[st:st+len(es)]);mm._mmap.madvise(mmap.MADV_DONTNEED);v[~es]=-1e10;z+=w*v/np.max(v,axis=1).clip(1e-9)[:,None]\n z[~es]=-np.inf;ix=np.argpartition(z,-100,axis=1)[:,-100:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(z,ix,axis=1),axis=1),axis=1);out.append(ix)\n out=np.concatenate(out)\nelse:\n d=np.load(S+a.stage+'_features.npz');u=d['u'];i=d['i'];base=d['base']@np.array(json.load(open(S+'base_weights.json'))['weights']);z=base\n if a.mode=='rerank':\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+('final_reranker.pkl' if a.stage=='test' else 'reranker.pkl'),'rb'));pred=model.predict(d['F']);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(pred,starts).clip(1e-9);z=conf['blend']*(pred/mx[u])+(1-conf['blend'])*base\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\n for k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\n out=np.stack(out)\nassert eligible(a.stage)[np.arange(len(U))[:,None],out].all()\ndf=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+a.name+'.tmp.parquet',index=False);os.replace(S+a.name+'.tmp.parquet',S+a.name+'.parquet')\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nvalidate_ranking(df,set(map(int,U)));print('Saved and validated',a.name,len(df),flush=True)\nPY\ncat /app/solution/rerank.log /app/solution/test_likes.log /app/solution/test_features.log /app/solution/context80.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/finalize.py <<'PY'\nfrom common import *\nimport argparse,pickle,mmap,gc\np=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--mode',default='base');p.add_argument('--name',default='submission');p.add_argument('--config',default='blend2');a=p.parse_args()\nif a.mode=='dense':\n c=json.load(open(S+a.config+'.json'));E=eligible(a.stage);out=[]\n models=[(np.load(S+a.stage+'_'+n+'.npy',mmap_mode='r'),w) for n,w in zip(c['models'],c['weights']) if w>0]\n for st in range(0,len(U),64):\n es=E[st:st+64];z=np.zeros(es.shape,np.float32)\n for mm,w in models:\n v=np.array(mm[st:st+len(es)]);mm._mmap.madvise(mmap.MADV_DONTNEED);v[~es]=-1e10;z+=w*v/np.max(v,axis=1).clip(1e-9)[:,None]\n z[~es]=-np.inf;ix=np.argpartition(z,-100,axis=1)[:,-100:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(z,ix,axis=1),axis=1),axis=1);out.append(ix)\n out=np.concatenate(out)\nelse:\n d=np.load(S+a.stage+'_features.npz');u=d['u'];i=d['i'];base=d['base']@np.array(json.load(open(S+'base_weights.json'))['weights']);z=base\n if a.mode=='rerank':\n conf=json.load(open(S+'reranker.json'));model=pickle.load(open(S+('final_reranker.pkl' if a.stage=='test' else 'reranker.pkl'),'rb'));pred=model.predict(d['F']);starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1];mx=np.maximum.reduceat(pred,starts).clip(1e-9);z=conf['blend']*(pred/mx[u])+(1-conf['blend'])*base\n starts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\n for k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\n out=np.stack(out)\nassert eligible(a.stage)[np.arange(len(U))[:,None],out].all()\ndf=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+a.name+'.tmp.parquet',index=False);os.replace(S+a.name+'.tmp.parquet',S+a.name+'.parquet')\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nvalidate_ranking(df,set(map(int,U)));print('Saved and validated',a.name,len(df),flush=True)\nPY\ncat /app/solution/rerank.log /app/solution/test_likes.log /app/solution/test_features.log /app/solution/context80.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\ndone test_likes0 29.744168758392334\ndone test_likes0.25 40.988404273986816\nKilled\n", "stderr": "", "aggregated_output": "base score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\ndone test_likes0 29.744168758392334\ndone test_likes0.25 40.988404273986816\nKilled\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5160 }, "formatted_output": "base score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\ndone test_likes0 29.744168758392334\ndone test_likes0.25 40.988404273986816\nKilled\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-23T05:40:39.127Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:40:39.127Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:40:39.183Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-67ce6c6f-5a01-4278-ba8f-e6286a5ca1fd", "process_id": "42531", "command": [ "/bin/sh", "-lc", "kill 818 932\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 818 932\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage past --models context30 log20r20 likes0 > /app/solution/past_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 > /app/solution/validation_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py > /app/solution/rerank.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 247, "nanos": 425043244 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:40:40.465Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c9da3e0f-4a11-4d72-bfee-81e8fb63aa04", "process_id": "38851", "command": [ "/bin/sh", "-lc", "while [ ! -f /app/solution/blend2.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage test --contexts 30 > /app/solution/test_context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage test > /app/solution/test_likes.log 2>&1\nln -f /app/solution/test_log_20_20_0.25.npy /app/solution/test_log20r20.npy\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "while [ ! -f /app/solution/blend2.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_context.py --stage test --contexts 30 > /app/solution/test_context.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/fit_likes.py --stage test > /app/solution/test_likes.log 2>&1\nln -f /app/solution/test_log_20_20_0.25.npy /app/solution/test_log20r20.npy\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 360, "nanos": 678504254 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:40:42.567Z", "callId": "call_RzurVEtuCOm9EFL8YajVVDMt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\\n except OSError:pass\\nPY\\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":2500}));\n" }, "result": { "timestamp": "2026-09-23T05:40:42.712Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"eadbdb\",\"wall_time_seconds\":0.00000412,\"exit_code\":0,\"original_token_count\":412,\"output\":\"1296 /bin/sh -c python - <<'PY'\\nimport os,signal\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n c=open('/proc/'+p+'/cmdline','rb').read()\\n if c.startswith(b'/opt/task-venv/bin/python\\\\0') and b'--suffix\\\\0r80\\\\0' in c:os.kill(int(p),signal.SIGTERM)\\n except OSError:pass\\nPY\\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1 \\n1362 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 \\n1374 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\\nimport os\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\\n except OSError:pass\\nPY\\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events \\nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\\nSCORE 100 0 0.03841819541703751\\nSCORE 100 0.25 0.043991123461385506\\nSCORE 100 0.5 0.047698627923777215\\nSCORE 100 0.75 0.04980313975424695\\nSCORE 100 1 0.050409065347536745\\nSCORE 200 0 0.03841819541703751\\nSCORE 200 0.25 0.04407037831861042\\nSCORE 200 0.5 0.048477567338242676\\nSCORE 200 0.75 0.04955680359975647\\nSCORE 200 1 0.04996905870509821\\nSCORE 350 0 0.03841819541703751\\nSCORE 350 0.25 0.0439843111573992\\nSCORE 350 0.5 0.04838937999051994\\nSCORE 350 0.75 0.04937642739775165\\nSCORE 350 1 0.05024968716554557\\nBEST (np.float64(0.050409065347536745), 100, 1)\\nlow 0\\nhigh 0\\nmax 37637\\noom 3\\noom_kill 2\\noom_group_kill 0\\nsock_throttled 8\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:40:42.655Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f96c0a60-d627-4cf1-b70d-d1c49f2c1c8b", "process_id": "60830", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\n except OSError:pass\nPY\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\n except OSError:pass\nPY\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1296 /bin/sh -c python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read()\n if c.startswith(b'/opt/task-venv/bin/python\\0') and b'--suffix\\0r80\\0' in c:os.kill(int(p),signal.SIGTERM)\n except OSError:pass\nPY\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1 \n1362 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 \n1374 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\n except OSError:pass\nPY\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events \nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\nSCORE 350 0 0.03841819541703751\nSCORE 350 0.25 0.0439843111573992\nSCORE 350 0.5 0.04838937999051994\nSCORE 350 0.75 0.04937642739775165\nSCORE 350 1 0.05024968716554557\nBEST (np.float64(0.050409065347536745), 100, 1)\nlow 0\nhigh 0\nmax 37637\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n", "stderr": "", "aggregated_output": "1296 /bin/sh -c python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read()\n if c.startswith(b'/opt/task-venv/bin/python\\0') and b'--suffix\\0r80\\0' in c:os.kill(int(p),signal.SIGTERM)\n except OSError:pass\nPY\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1 \n1362 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 \n1374 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\n except OSError:pass\nPY\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events \nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\nSCORE 350 0 0.03841819541703751\nSCORE 350 0.25 0.0439843111573992\nSCORE 350 0.5 0.04838937999051994\nSCORE 350 0.75 0.04937642739775165\nSCORE 350 1 0.05024968716554557\nBEST (np.float64(0.050409065347536745), 100, 1)\nlow 0\nhigh 0\nmax 37637\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4120 }, "formatted_output": "1296 /bin/sh -c python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read()\n if c.startswith(b'/opt/task-venv/bin/python\\0') and b'--suffix\\0r80\\0' in c:os.kill(int(p),signal.SIGTERM)\n except OSError:pass\nPY\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1 \n1362 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 \n1374 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if 'fit_context.py --reg 80' in c: print(p,c[:600])\n except OSError:pass\nPY\ncat /app/solution/rerank.log; cat /sys/fs/cgroup/memory.events \nbase score (np.float64(0.03841819541703751), array([0.5 , 0.25, 0.25]))\nSCORE 100 0 0.03841819541703751\nSCORE 100 0.25 0.043991123461385506\nSCORE 100 0.5 0.047698627923777215\nSCORE 100 0.75 0.04980313975424695\nSCORE 100 1 0.050409065347536745\nSCORE 200 0 0.03841819541703751\nSCORE 200 0.25 0.04407037831861042\nSCORE 200 0.5 0.048477567338242676\nSCORE 200 0.75 0.04955680359975647\nSCORE 200 1 0.04996905870509821\nSCORE 350 0 0.03841819541703751\nSCORE 350 0.25 0.0439843111573992\nSCORE 350 0.5 0.04838937999051994\nSCORE 350 0.75 0.04937642739775165\nSCORE 350 1 0.05024968716554557\nBEST (np.float64(0.050409065347536745), 100, 1)\nlow 0\nhigh 0\nmax 37637\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:40:42.680Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab366467c3c87d2a3e689e274068c66", "usage": { "input_tokens": 63513, "cached_input_tokens": 61696, "cache_write_input_tokens": 0, "output_tokens": 616, "reasoning_output_tokens": 473, "total_tokens": 64129 }, "turn_token_usage": { "input_tokens": 2530102, "cached_input_tokens": 2468608, "cache_write_input_tokens": 0, "output_tokens": 31120, "reasoning_output_tokens": 13815, "total_tokens": 2561222 }, "thread_token_usage": { "input_tokens": 2530102, "cached_input_tokens": 2468608, "cache_write_input_tokens": 0, "output_tokens": 31120, "reasoning_output_tokens": 13815, "total_tokens": 2561222 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-23T05:41:01.506Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:01.506Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:41:06.889Z", "callId": "call_mORD93tKFox0IqZj724ABI3X", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill 1296 1362\\nrm -f /app/solution/test_features.npz\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py --final > /app/solution/final_train.log 2>&1\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation > /app/solution/finalize_val.log 2>&1\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:41:06.946Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\nexec_command failed: CreateProcess { message: \"Rejected(\\\"`/bin/sh -lc 'kill 1296 1362\\\\nrm -f /app/solution/test_features.npz\\\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\\\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py --final > /app/solution/final_train.log 2>&1\\\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation > /app/solution/finalize_val.log 2>&1\\\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json'` rejected: rm -f style commands are not permitted. Use a safer approach\\\")\" }", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:41:06.944Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3665b149087d2b95b4e5e9bc58f34", "usage": { "input_tokens": 64877, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 756, "reasoning_output_tokens": 516, "total_tokens": 65633 }, "turn_token_usage": { "input_tokens": 2594979, "cached_input_tokens": 2531968, "cache_write_input_tokens": 0, "output_tokens": 31876, "reasoning_output_tokens": 14331, "total_tokens": 2626855 }, "thread_token_usage": { "input_tokens": 2594979, "cached_input_tokens": 2531968, "cache_write_input_tokens": 0, "output_tokens": 31876, "reasoning_output_tokens": 14331, "total_tokens": 2626855 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-23T05:41:13.905Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:13.905Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:41:28.252Z", "text": "The earlier-week reranker improved validation NDCG@10 to 0.05041, comfortably ahead of the blend. I’m refitting it on both observed windows, then checking the final ranking’s coverage, ranks, duplicates, and eligibility.\n" }, { "kind": "context", "timestamp": "2026-09-23T05:41:33.836Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b8b0b92-21a0-4e97-ba2d-50f3301f3cb2", "process_id": "55378", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read()\n if c.startswith(b'/opt/task-venv/bin/python\\0') and b'--suffix\\0r80\\0' in c:os.kill(int(p),signal.SIGTERM)\n except OSError:pass\nPY\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read()\n if c.startswith(b'/opt/task-venv/bin/python\\0') and b'--suffix\\0r80\\0' in c:os.kill(int(p),signal.SIGTERM)\n except OSError:pass\nPY\nwhile [ ! -f /app/solution/test_features.npz ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/fit_context.py --reg 80 --contexts 30 --suffix r80 > /app/solution/context80.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 113, "nanos": 464621403 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:41:36.201Z", "callId": "call_xyvO2EfWclhRbqJSlur1TeHk", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill 1296 1362\\npython - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/test_features.npz')\\nif p.exists():p.rename('/app/solution/incomplete_test_features.npz')\\nPY\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py --final > /app/solution/final_train.log 2>&1\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation > /app/solution/finalize_val.log 2>&1\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:41:37.374Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b3e7fa\",\"wall_time_seconds\":1.001541263,\"session_id\":33572,\"original_token_count\":18,\"output\":\"/bin/sh: 1: kill: No such process\\n\\n/bin/sh: 1: kill: No such process\\n\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:41:36.335Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36681a09887d29be20e0781aba949", "usage": { "input_tokens": 65993, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 330, "reasoning_output_tokens": 0, "total_tokens": 66323 }, "turn_token_usage": { "input_tokens": 2660972, "cached_input_tokens": 2561536, "cache_write_input_tokens": 0, "output_tokens": 32206, "reasoning_output_tokens": 14331, "total_tokens": 2693178 }, "thread_token_usage": { "input_tokens": 2660972, "cached_input_tokens": 2561536, "cache_write_input_tokens": 0, "output_tokens": 32206, "reasoning_output_tokens": 14331, "total_tokens": 2693178 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-23T05:41:58.455Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:58.455Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:42:15.499Z", "callId": "call_IFDGdtIenz18PdIK6X97nKF3", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune_reranker.py <<'PY'\\nfrom common import *\\nfrom sklearn.ensemble import HistGradientBoostingRegressor\\nimport pickle,gc\\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0;F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];w/=w.mean();VF=v['F'];vu=v['u'];vi=v['i'];vy=v['y'];vid=v['ID'];starts=np.r_[0,np.flatnonzero(vu[1:]!=vu[:-1])+1,len(vu)]\\ndef ev(z):\\n vals=[]\\n for k in range(len(U)):\\n if vid[k]<=0:continue\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(vy[b:e][ix]@disc/vid[k])\\n return np.mean(vals)\\nbest=json.load(open(S+'reranker.json'))['score']\\nfor leaves,it,reg,minleaf in [(7,200,10,150),(15,150,10,150),(31,100,100,150),(15,100,100,300)]:\\n start=time.time();m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42)\\n m.fit(F,y,sample_weight=w);pred=m.predict(VF);score=ev(pred);print('TRIAL',leaves,it,reg,minleaf,score,time.time()-start,flush=True)\\n if score>best:\\n best=score;pickle.dump(m,open(S+'trial_reranker.pkl','wb'));json.dump({'score':score,'iterations':it,'blend':1,'leaves':leaves,'reg':reg,'minleaf':minleaf},open(S+'trial_reranker.json','w'))\\nPY\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_reranker.py > /app/solution/tune_reranker.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/test_features.log /app/solution/final_train.log /app/solution/context80.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:42:16.727Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"431212\",\"wall_time_seconds\":1.001415963,\"session_id\":64534,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:42:16 UTC\"}\n{\"chunk_id\":\"606f7b\",\"wall_time_seconds\":0.000002949,\"exit_code\":0,\"original_token_count\":42,\"output\":\"features test (2186637, 55) positive 0 likes 0 time 14.483092308044434\\ndone validation_context30r80 48.46379327774048\\nSCORE validation_context30r80 0.03792916223440174\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:42:15.622Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3669193c887d280bf136c7ef5c965", "usage": { "input_tokens": 66408, "cached_input_tokens": 65792, "cache_write_input_tokens": 0, "output_tokens": 1123, "reasoning_output_tokens": 516, "total_tokens": 67531 }, "turn_token_usage": { "input_tokens": 2727380, "cached_input_tokens": 2627328, "cache_write_input_tokens": 0, "output_tokens": 33329, "reasoning_output_tokens": 14847, "total_tokens": 2760709 }, "thread_token_usage": { "input_tokens": 2727380, "cached_input_tokens": 2627328, "cache_write_input_tokens": 0, "output_tokens": 33329, "reasoning_output_tokens": 14847, "total_tokens": 2760709 } } }, { "kind": "context", "timestamp": "2026-09-23T05:42:16.713Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fe0408fb-f8b9-472f-ad74-dcbd7030c1b7", "process_id": "78863", "command": [ "/bin/sh", "-lc", "cat /app/solution/test_features.log /app/solution/final_train.log /app/solution/context80.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/test_features.log /app/solution/final_train.log /app/solution/context80.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features test (2186637, 55) positive 0 likes 0 time 14.483092308044434\ndone validation_context30r80 48.46379327774048\nSCORE validation_context30r80 0.03792916223440174\n", "stderr": "", "aggregated_output": "features test (2186637, 55) positive 0 likes 0 time 14.483092308044434\ndone validation_context30r80 48.46379327774048\nSCORE validation_context30r80 0.03792916223440174\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2949 }, "formatted_output": "features test (2186637, 55) positive 0 likes 0 time 14.483092308044434\ndone validation_context30r80 48.46379327774048\nSCORE validation_context30r80 0.03792916223440174\n" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-23T05:42:36.385Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:42:36.385Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:42:52.168Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6fab6eec-f35b-4f84-912a-0348c332f6d1", "process_id": "33572", "command": [ "/bin/sh", "-lc", "kill 1296 1362\npython - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/test_features.npz')\nif p.exists():p.rename('/app/solution/incomplete_test_features.npz')\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py --final > /app/solution/final_train.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation > /app/solution/finalize_val.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 1296 1362\npython - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/test_features.npz')\nif p.exists():p.rename('/app/solution/incomplete_test_features.npz')\nPY\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 > /app/solution/test_features.log 2>&1\nOPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/rerank.py --final > /app/solution/final_train.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation > /app/solution/finalize_val.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_evaluation.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 75, "nanos": 796500542 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:43:07.420Z", "callId": "call_QyAETyaenUfALid3YRQ4uWBF", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/build.py <<'PY'\\n\\\"\\\"\\\"Rebuild the selected ranker and its cutoff-correct submission from public inputs.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport os,sys,subprocess,json\\nS=Path(__file__).resolve().parent\\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\\ndef run(script,*args):\\n subprocess.run([sys.executable,str(S/script),*map(str,args)],cwd=S,env=os.environ,check=True)\\nrun('inspect_data.py')\\nrun('common.py') # Save a valid popularity fallback before fitting.\\nfor stage in ['past','validation','test']:\\n run('fit_context.py','--stage',stage,'--contexts',30)\\n run('fit_models.py','--stage',stage,'--label','log20r20','--days',20,'--like',0,'--organic',1,'--reg',20)\\n run('fit_likes.py','--stage',stage)\\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0')\\n# Recreate the selected hyperparameters, then fit on both observed windows.\\nrun('train_selected.py')\\nrun('rerank.py','--final')\\nrun('finalize.py','--stage','validation','--mode','rerank','--name','validation')\\nrun('finalize.py','--stage','test','--mode','rerank','--name','submission')\\nsubprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True)\\nPY\\ncat > /app/solution/train_selected.py <<'PY'\\nfrom common import *\\nfrom sklearn.ensemble import HistGradientBoostingRegressor\\nimport pickle\\nconf=json.load(open(S+'selected_config.json'))\\nd=np.load(S+'past_features.npz');u=d['u'];ID=d['ID'];keep=ID[u]>0;F=d['F'][keep];y=d['y'][keep];w=1/ID[u[keep]];w/=w.mean()\\nm=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=conf['iterations'],max_leaf_nodes=conf['leaves'],min_samples_leaf=conf['minleaf'],l2_regularization=conf['reg'],max_bins=128,early_stopping=False,random_state=42)\\nm.fit(F,y,sample_weight=w);pickle.dump(m,open(S+'reranker.pkl','wb'))\\njson.dump({'iterations':conf['iterations'],'blend':1,'score':conf['validation_score']},open(S+'reranker.json','w'))\\njson.dump({'weights':[.5,.25,.25]},open(S+'base_weights.json','w'))\\nPY\\ncat > /app/solution/selected_config.json <<'JSON'\\n{\\\"iterations\\\":100,\\\"leaves\\\":23,\\\"minleaf\\\":100,\\\"reg\\\":10,\\\"validation_score\\\":0.050409065347536745}\\nJSON\\ncat > /app/solution/README.md <<'MD'\\n# Music discovery ranker\\n\\nDeliverable: `submission.parquet` — 100 eligible, distinct tracks per target user, with ranks 1–100.\\n\\nRebuild with the supplied environment:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/build.py\\n```\\n\\nThe model combines three collaborative predictors: a regularized linear track model with a 20-day history decay; a 30-day user context projected through a 60-day collaborative model; and a separate ridge predictor for explicit likes. A Poisson gradient-boosted reranker uses their scores and ranks plus cutoff-correct track popularity, trends, organic completion rates, and user/track like statistics.\\n\\nThe reranker is first trained on a seven-day historical holdout ending at the training cutoff, after a 30-minute gap. Public validation features use training events only. Model selection uses graded organic NDCG@10 on the provided eligible candidates. After selection, the collaborative models use train + validation history, and the final reranker is refit on both observed training examples and public-validation examples. Test eligibility is applied again before writing the artifact.\\n\\nSelected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`. The validation result is an offline association metric, not an estimate of causal lift. No hidden interactions, external data, network calls, or extra packages are used.\\nMD\\ncat /app/solution/final_train.log /app/solution/tune_reranker.log /app/solution/public_evaluation.json\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:43:07.580Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"29db61\",\"wall_time_seconds\":0.0000022,\"exit_code\":0,\"original_token_count\":35,\"output\":\"FINAL SAVED\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.050409,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:43:07.468Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cd17af14-521e-44e7-a96b-2e28a41b3916", "process_id": "41058", "command": [ "/bin/sh", "-lc", "cat > /app/solution/build.py <<'PY'\n\"\"\"Rebuild the selected ranker and its cutoff-correct submission from public inputs.\"\"\"\nfrom pathlib import Path\nimport os,sys,subprocess,json\nS=Path(__file__).resolve().parent\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\ndef run(script,*args):\n subprocess.run([sys.executable,str(S/script),*map(str,args)],cwd=S,env=os.environ,check=True)\nrun('inspect_data.py')\nrun('common.py') # Save a valid popularity fallback before fitting.\nfor stage in ['past','validation','test']:\n run('fit_context.py','--stage',stage,'--contexts',30)\n run('fit_models.py','--stage',stage,'--label','log20r20','--days',20,'--like',0,'--organic',1,'--reg',20)\n run('fit_likes.py','--stage',stage)\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0')\n# Recreate the selected hyperparameters, then fit on both observed windows.\nrun('train_selected.py')\nrun('rerank.py','--final')\nrun('finalize.py','--stage','validation','--mode','rerank','--name','validation')\nrun('finalize.py','--stage','test','--mode','rerank','--name','submission')\nsubprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True)\nPY\ncat > /app/solution/train_selected.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle\nconf=json.load(open(S+'selected_config.json'))\nd=np.load(S+'past_features.npz');u=d['u'];ID=d['ID'];keep=ID[u]>0;F=d['F'][keep];y=d['y'][keep];w=1/ID[u[keep]];w/=w.mean()\nm=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=conf['iterations'],max_leaf_nodes=conf['leaves'],min_samples_leaf=conf['minleaf'],l2_regularization=conf['reg'],max_bins=128,early_stopping=False,random_state=42)\nm.fit(F,y,sample_weight=w);pickle.dump(m,open(S+'reranker.pkl','wb'))\njson.dump({'iterations':conf['iterations'],'blend':1,'score':conf['validation_score']},open(S+'reranker.json','w'))\njson.dump({'weights':[.5,.25,.25]},open(S+'base_weights.json','w'))\nPY\ncat > /app/solution/selected_config.json <<'JSON'\n{\"iterations\":100,\"leaves\":23,\"minleaf\":100,\"reg\":10,\"validation_score\":0.050409065347536745}\nJSON\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\nDeliverable: `submission.parquet` — 100 eligible, distinct tracks per target user, with ranks 1–100.\n\nRebuild with the supplied environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nThe model combines three collaborative predictors: a regularized linear track model with a 20-day history decay; a 30-day user context projected through a 60-day collaborative model; and a separate ridge predictor for explicit likes. A Poisson gradient-boosted reranker uses their scores and ranks plus cutoff-correct track popularity, trends, organic completion rates, and user/track like statistics.\n\nThe reranker is first trained on a seven-day historical holdout ending at the training cutoff, after a 30-minute gap. Public validation features use training events only. Model selection uses graded organic NDCG@10 on the provided eligible candidates. After selection, the collaborative models use train + validation history, and the final reranker is refit on both observed training examples and public-validation examples. Test eligibility is applied again before writing the artifact.\n\nSelected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`. The validation result is an offline association metric, not an estimate of causal lift. No hidden interactions, external data, network calls, or extra packages are used.\nMD\ncat /app/solution/final_train.log /app/solution/tune_reranker.log /app/solution/public_evaluation.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/build.py <<'PY'\n\"\"\"Rebuild the selected ranker and its cutoff-correct submission from public inputs.\"\"\"\nfrom pathlib import Path\nimport os,sys,subprocess,json\nS=Path(__file__).resolve().parent\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\ndef run(script,*args):\n subprocess.run([sys.executable,str(S/script),*map(str,args)],cwd=S,env=os.environ,check=True)\nrun('inspect_data.py')\nrun('common.py') # Save a valid popularity fallback before fitting.\nfor stage in ['past','validation','test']:\n run('fit_context.py','--stage',stage,'--contexts',30)\n run('fit_models.py','--stage',stage,'--label','log20r20','--days',20,'--like',0,'--organic',1,'--reg',20)\n run('fit_likes.py','--stage',stage)\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0')\n# Recreate the selected hyperparameters, then fit on both observed windows.\nrun('train_selected.py')\nrun('rerank.py','--final')\nrun('finalize.py','--stage','validation','--mode','rerank','--name','validation')\nrun('finalize.py','--stage','test','--mode','rerank','--name','submission')\nsubprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True)\nPY\ncat > /app/solution/train_selected.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle\nconf=json.load(open(S+'selected_config.json'))\nd=np.load(S+'past_features.npz');u=d['u'];ID=d['ID'];keep=ID[u]>0;F=d['F'][keep];y=d['y'][keep];w=1/ID[u[keep]];w/=w.mean()\nm=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=conf['iterations'],max_leaf_nodes=conf['leaves'],min_samples_leaf=conf['minleaf'],l2_regularization=conf['reg'],max_bins=128,early_stopping=False,random_state=42)\nm.fit(F,y,sample_weight=w);pickle.dump(m,open(S+'reranker.pkl','wb'))\njson.dump({'iterations':conf['iterations'],'blend':1,'score':conf['validation_score']},open(S+'reranker.json','w'))\njson.dump({'weights':[.5,.25,.25]},open(S+'base_weights.json','w'))\nPY\ncat > /app/solution/selected_config.json <<'JSON'\n{\"iterations\":100,\"leaves\":23,\"minleaf\":100,\"reg\":10,\"validation_score\":0.050409065347536745}\nJSON\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\nDeliverable: `submission.parquet` — 100 eligible, distinct tracks per target user, with ranks 1–100.\n\nRebuild with the supplied environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nThe model combines three collaborative predictors: a regularized linear track model with a 20-day history decay; a 30-day user context projected through a 60-day collaborative model; and a separate ridge predictor for explicit likes. A Poisson gradient-boosted reranker uses their scores and ranks plus cutoff-correct track popularity, trends, organic completion rates, and user/track like statistics.\n\nThe reranker is first trained on a seven-day historical holdout ending at the training cutoff, after a 30-minute gap. Public validation features use training events only. Model selection uses graded organic NDCG@10 on the provided eligible candidates. After selection, the collaborative models use train + validation history, and the final reranker is refit on both observed training examples and public-validation examples. Test eligibility is applied again before writing the artifact.\n\nSelected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`. The validation result is an offline association metric, not an estimate of causal lift. No hidden interactions, external data, network calls, or extra packages are used.\nMD\ncat /app/solution/final_train.log /app/solution/tune_reranker.log /app/solution/public_evaluation.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "FINAL SAVED\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050409,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "FINAL SAVED\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050409,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2200 }, "formatted_output": "FINAL SAVED\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050409,\n \"quality_users\": 4263\n }\n}\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:43:07.578Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab366b8efd487d2b75b9d376da5f53c", "usage": { "input_tokens": 67697, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 1545, "reasoning_output_tokens": 516, "total_tokens": 69242 }, "turn_token_usage": { "input_tokens": 2795077, "cached_input_tokens": 2693504, "cache_write_input_tokens": 0, "output_tokens": 34874, "reasoning_output_tokens": 15363, "total_tokens": 2829951 }, "thread_token_usage": { "input_tokens": 2795077, "cached_input_tokens": 2693504, "cache_write_input_tokens": 0, "output_tokens": 34874, "reasoning_output_tokens": 15363, "total_tokens": 2829951 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-23T05:43:26.114Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:43:26.114Z", "text": "" } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-23T05:43:33.255Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:43:33.255Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:43:44.318Z", "callId": "call_ARkA0YsRI9DXBPlzwdtTEl82", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/make_features.py');s=p.read_text().replace(\\\"p.add_argument('--stage',default='validation');\\\",\\\"p.add_argument('--stage',default='validation');p.add_argument('--tag',default='');\\\").replace(\\\"S+a.stage+'_features.npz'\\\",\\\"S+a.stage+'_features'+a.tag+'.npz'\\\");p.write_text(s)\\np=Path('/app/solution/finalize.py');s=p.read_text().replace(\\\"p.add_argument('--stage',default='test');\\\",\\\"p.add_argument('--stage',default='test');p.add_argument('--tag',default='');\\\").replace(\\\"S+a.stage+'_features.npz'\\\",\\\"S+a.stage+'_features'+a.tag+'.npz'\\\");p.write_text(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/validation_wide.log 2>&1\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation_wide --tag _wide > /app/solution/finalize_wide.log 2>&1\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_wide.parquet > /app/solution/public_wide.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/tune_reranker.log /app/solution/finalize_val.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:43:45.555Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"cb2eff\",\"wall_time_seconds\":1.001155998,\"session_id\":74721,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"adaa6f\",\"wall_time_seconds\":0.00000488,\"exit_code\":0,\"original_token_count\":43,\"output\":\"TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\\nSaved and validated validation 845600\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-23 05:43:45 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:43:44.428Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab366ebfa0c87d2ba9450aa51191b72", "usage": { "input_tokens": 69353, "cached_input_tokens": 67456, "cache_write_input_tokens": 0, "output_tokens": 1157, "reasoning_output_tokens": 731, "total_tokens": 70510 }, "turn_token_usage": { "input_tokens": 2864430, "cached_input_tokens": 2760960, "cache_write_input_tokens": 0, "output_tokens": 36031, "reasoning_output_tokens": 16094, "total_tokens": 2900461 }, "thread_token_usage": { "input_tokens": 2864430, "cached_input_tokens": 2760960, "cache_write_input_tokens": 0, "output_tokens": 36031, "reasoning_output_tokens": 16094, "total_tokens": 2900461 } } }, { "kind": "context", "timestamp": "2026-09-23T05:43:45.537Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a55ecf92-1c9f-496b-9727-f88891cbd245", "process_id": "87681", "command": [ "/bin/sh", "-lc", "cat /app/solution/tune_reranker.log /app/solution/finalize_val.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/tune_reranker.log /app/solution/finalize_val.log; ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nSaved and validated validation 845600\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nSaved and validated validation 845600\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4880 }, "formatted_output": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nSaved and validated validation 845600\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-23T05:43:49.264Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:43:49.264Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:43:51.018Z", "text": "The refitted submission is saved, and the official public score is 0.050409. I’m checking a few smaller reranker settings and a wider candidate pool; the current validated submission remains in place while those tests run.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:43:58.650Z", "callId": "call_LtN53xj2fGz4YJz6q64mW9y1", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/audit.py <<'PY'\\nimport sys,json\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,validate_ranking,read_ranking\\nimport pandas as pd,numpy as np\\np='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\\nr={'rows':len(f),'users':len(targets),'rows_per_user':100,'all_test_eligible':ok,'ranks':'1-100, unique','duplicate_items':0};json.dump(r,open('/app/solution/submission_audit.json','w'),indent=2);print(r)\\nPY\\n/opt/task-venv/bin/python /app/solution/audit.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:43:59.550Z", "content": "Script completed\nWall time 0.9 seconds\nOutput:\n\n{\"chunk_id\":\"da75e2\",\"wall_time_seconds\":0.712899774,\"exit_code\":1,\"original_token_count\":207,\"output\":\"Traceback (most recent call last):\\n File \\\"/app/solution/audit.py\\\", line 5, in \\n p='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\\n ^^^^^^^^^^^^^^^\\n File \\\"/task/ranking_evaluation.py\\\", line 98, in read_ranking\\n return pd.read_parquet(path) if path.suffix == \\\".parquet\\\" else pd.read_csv(path)\\n ^^^^^^^^^^^\\nAttributeError: 'str' object has no attribute 'suffix'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:43:58.742Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36711c15c87d292008bf7d347eb6b", "usage": { "input_tokens": 70684, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 346, "reasoning_output_tokens": 30, "total_tokens": 71030 }, "turn_token_usage": { "input_tokens": 2935114, "cached_input_tokens": 2830080, "cache_write_input_tokens": 0, "output_tokens": 36377, "reasoning_output_tokens": 16124, "total_tokens": 2971491 }, "thread_token_usage": { "input_tokens": 2935114, "cached_input_tokens": 2830080, "cache_write_input_tokens": 0, "output_tokens": 36377, "reasoning_output_tokens": 16124, "total_tokens": 2971491 } } }, { "kind": "context", "timestamp": "2026-09-23T05:43:59.544Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-950138b9-044e-4d0f-af08-ab2c643e707c", "process_id": "84459", "command": [ "/bin/sh", "-lc", "cat > /app/solution/audit.py <<'PY'\nimport sys,json\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking,read_ranking\nimport pandas as pd,numpy as np\np='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\nr={'rows':len(f),'users':len(targets),'rows_per_user':100,'all_test_eligible':ok,'ranks':'1-100, unique','duplicate_items':0};json.dump(r,open('/app/solution/submission_audit.json','w'),indent=2);print(r)\nPY\n/opt/task-venv/bin/python /app/solution/audit.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/audit.py <<'PY'\nimport sys,json\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking,read_ranking\nimport pandas as pd,numpy as np\np='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\nr={'rows':len(f),'users':len(targets),'rows_per_user':100,'all_test_eligible':ok,'ranks':'1-100, unique','duplicate_items':0};json.dump(r,open('/app/solution/submission_audit.json','w'),indent=2);print(r)\nPY\n/opt/task-venv/bin/python /app/solution/audit.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/audit.py\", line 5, in \n p='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\n ^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/audit.py\", line 5, in \n p='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\n ^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 712893744 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/audit.py\", line 5, in \n p='/app/solution/submission.parquet';targets=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid));f=validate_ranking(read_ranking(p),targets);e=CandidateSet('/task/data/test_eligible_candidates.npz');ok=all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));assert ok\n ^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:44:05.856Z", "callId": "call_FlhBOrNwJa1C8JUl5ndz6oZY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/audit.py');s=p.read_text().replace('import sys,json','import sys,json\\\\nfrom pathlib import Path').replace(\\\"p='/app/solution/submission.parquet';\\\", \\\"p=Path('/app/solution/submission.parquet');\\\");p.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/audit.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:44:07.034Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"23eaf4\",\"wall_time_seconds\":1.00085035,\"session_id\":94809,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:44:05.989Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3671fca3887d2a25061a9ecd64dc7", "usage": { "input_tokens": 71282, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 128, "reasoning_output_tokens": 0, "total_tokens": 71410 }, "turn_token_usage": { "input_tokens": 3006396, "cached_input_tokens": 2900608, "cache_write_input_tokens": 0, "output_tokens": 36505, "reasoning_output_tokens": 16124, "total_tokens": 3042901 }, "thread_token_usage": { "input_tokens": 3006396, "cached_input_tokens": 2900608, "cache_write_input_tokens": 0, "output_tokens": 36505, "reasoning_output_tokens": 16124, "total_tokens": 3042901 } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:08.176Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-99bc32bc-52e2-4dc2-a310-9f19d5418227", "process_id": "94809", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/audit.py');s=p.read_text().replace('import sys,json','import sys,json\\nfrom pathlib import Path').replace(\"p='/app/solution/submission.parquet';\", \"p=Path('/app/solution/submission.parquet');\");p.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/audit.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/audit.py');s=p.read_text().replace('import sys,json','import sys,json\\nfrom pathlib import Path').replace(\"p='/app/solution/submission.parquet';\", \"p=Path('/app/solution/submission.parquet');\");p.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/audit.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 143813789 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n" } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-23T05:44:26.346Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:44:26.346Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:44:27.192Z", "callId": "call_qUal85KYIFFZYQ1JgLaCUB5T", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/tune_reranker.log /app/solution/validation_wide.log /app/solution/public_wide.json /app/solution/submission_audit.json\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:44:27.306Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"704c62\",\"wall_time_seconds\":0.00000305,\"exit_code\":1,\"original_token_count\":95,\"output\":\"TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\\ncat: /app/solution/public_wide.json: No such file or directory\\n{\\n \\\"rows\\\": 845600,\\n \\\"users\\\": 8456,\\n \\\"rows_per_user\\\": 100,\\n \\\"all_test_eligible\\\": true,\\n \\\"ranks\\\": \\\"1-100, unique\\\",\\n \\\"duplicate_items\\\": 0\\n}\"}\n{\"current_time\":\"2026-09-23 05:44:27 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:44:27.246Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c1076927-f8c0-49d6-9ea8-de94e731ac90", "process_id": "65873", "command": [ "/bin/sh", "-lc", "cat /app/solution/tune_reranker.log /app/solution/validation_wide.log /app/solution/public_wide.json /app/solution/submission_audit.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/tune_reranker.log /app/solution/validation_wide.log /app/solution/public_wide.json /app/solution/submission_audit.json" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\ncat: /app/solution/public_wide.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}", "stderr": "", "aggregated_output": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\ncat: /app/solution/public_wide.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}", "exit_code": 1, "duration": { "secs": 0, "nanos": 3050 }, "formatted_output": "TRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\ncat: /app/solution/public_wide.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}" } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:27.303Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367273b6087d291745b2862309685", "usage": { "input_tokens": 71468, "cached_input_tokens": 71040, "cache_write_input_tokens": 0, "output_tokens": 590, "reasoning_output_tokens": 516, "total_tokens": 72058 }, "turn_token_usage": { "input_tokens": 3077864, "cached_input_tokens": 2971648, "cache_write_input_tokens": 0, "output_tokens": 37095, "reasoning_output_tokens": 16640, "total_tokens": 3114959 }, "thread_token_usage": { "input_tokens": 3077864, "cached_input_tokens": 2971648, "cache_write_input_tokens": 0, "output_tokens": 37095, "reasoning_output_tokens": 16640, "total_tokens": 3114959 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-23T05:44:50.099Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:44:50.099Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:44:51.342Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c4031e98-4310-4ce9-b2e1-f76e9a33185a", "process_id": "64534", "command": [ "/bin/sh", "-lc", "cat > /app/solution/tune_reranker.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle,gc\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0;F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];w/=w.mean();VF=v['F'];vu=v['u'];vi=v['i'];vy=v['y'];vid=v['ID'];starts=np.r_[0,np.flatnonzero(vu[1:]!=vu[:-1])+1,len(vu)]\ndef ev(z):\n vals=[]\n for k in range(len(U)):\n if vid[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(vy[b:e][ix]@disc/vid[k])\n return np.mean(vals)\nbest=json.load(open(S+'reranker.json'))['score']\nfor leaves,it,reg,minleaf in [(7,200,10,150),(15,150,10,150),(31,100,100,150),(15,100,100,300)]:\n start=time.time();m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42)\n m.fit(F,y,sample_weight=w);pred=m.predict(VF);score=ev(pred);print('TRIAL',leaves,it,reg,minleaf,score,time.time()-start,flush=True)\n if score>best:\n best=score;pickle.dump(m,open(S+'trial_reranker.pkl','wb'));json.dump({'score':score,'iterations':it,'blend':1,'leaves':leaves,'reg':reg,'minleaf':minleaf},open(S+'trial_reranker.json','w'))\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_reranker.py > /app/solution/tune_reranker.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/tune_reranker.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle,gc\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0;F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];w/=w.mean();VF=v['F'];vu=v['u'];vi=v['i'];vy=v['y'];vid=v['ID'];starts=np.r_[0,np.flatnonzero(vu[1:]!=vu[:-1])+1,len(vu)]\ndef ev(z):\n vals=[]\n for k in range(len(U)):\n if vid[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(vy[b:e][ix]@disc/vid[k])\n return np.mean(vals)\nbest=json.load(open(S+'reranker.json'))['score']\nfor leaves,it,reg,minleaf in [(7,200,10,150),(15,150,10,150),(31,100,100,150),(15,100,100,300)]:\n start=time.time();m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42)\n m.fit(F,y,sample_weight=w);pred=m.predict(VF);score=ev(pred);print('TRIAL',leaves,it,reg,minleaf,score,time.time()-start,flush=True)\n if score>best:\n best=score;pickle.dump(m,open(S+'trial_reranker.pkl','wb'));json.dump({'score':score,'iterations':it,'blend':1,'leaves':leaves,'reg':reg,'minleaf':minleaf},open(S+'trial_reranker.json','w'))\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_reranker.py > /app/solution/tune_reranker.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 155, "nanos": 668694757 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:45:00.980Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-11b47181-8588-407b-ac5a-135d95f30ab2", "process_id": "74721", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/make_features.py');s=p.read_text().replace(\"p.add_argument('--stage',default='validation');\",\"p.add_argument('--stage',default='validation');p.add_argument('--tag',default='');\").replace(\"S+a.stage+'_features.npz'\",\"S+a.stage+'_features'+a.tag+'.npz'\");p.write_text(s)\np=Path('/app/solution/finalize.py');s=p.read_text().replace(\"p.add_argument('--stage',default='test');\",\"p.add_argument('--stage',default='test');p.add_argument('--tag',default='');\").replace(\"S+a.stage+'_features.npz'\",\"S+a.stage+'_features'+a.tag+'.npz'\");p.write_text(s)\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/validation_wide.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation_wide --tag _wide > /app/solution/finalize_wide.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_wide.parquet > /app/solution/public_wide.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/make_features.py');s=p.read_text().replace(\"p.add_argument('--stage',default='validation');\",\"p.add_argument('--stage',default='validation');p.add_argument('--tag',default='');\").replace(\"S+a.stage+'_features.npz'\",\"S+a.stage+'_features'+a.tag+'.npz'\");p.write_text(s)\np=Path('/app/solution/finalize.py');s=p.read_text().replace(\"p.add_argument('--stage',default='test');\",\"p.add_argument('--stage',default='test');p.add_argument('--tag',default='');\").replace(\"S+a.stage+'_features.npz'\",\"S+a.stage+'_features'+a.tag+'.npz'\");p.write_text(s)\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage validation --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/validation_wide.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage validation --mode rerank --name validation_wide --tag _wide > /app/solution/finalize_wide.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_wide.parquet > /app/solution/public_wide.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 76, "nanos": 489587310 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:45:19.049Z", "callId": "call_zrnU55ClqZMaK5wCKh7dmRcl", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/ensemble.py <<'PY'\\nfrom common import *\\nfrom sklearn.ensemble import HistGradientBoostingRegressor\\nimport pickle,sys,gc\\nmode=sys.argv[1] if len(sys.argv)>1 else 'validation'\\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0\\nif mode=='validation':F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];target=v\\nelse:\\n vu=v['u'];vi=v['ID'];vk=vi[vu]>0;F=np.concatenate([p['F'][keep],v['F'][vk]]);y=np.r_[p['y'][keep],v['y'][vk]];w=np.r_[1/ID[u[keep]],1/vi[vu[vk]]];target=np.load(S+'test_features.npz')\\nw/=w.mean();TF=target['F'];tu=target['u'];ti=target['i'];ty=target['y'];tid=target['ID'];starts=np.r_[0,np.flatnonzero(tu[1:]!=tu[:-1])+1,len(tu)]\\nbase=pickle.load(open(S+('reranker.pkl' if mode=='validation' else 'final_reranker.pkl'),'rb'));preds=[base.predict(TF)]\\nfor j,(leaves,it,reg,minleaf) in enumerate([(7,200,10,150),(31,100,100,150)]):\\n m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42);m.fit(F,y,sample_weight=w);pickle.dump(m,open(S+mode+f'_ensemble{j}.pkl','wb'));preds.append(m.predict(TF));print('fit',mode,j,flush=True)\\ndef ev(z):\\n vals=[]\\n for k in range(len(U)):\\n if tid[k]<=0:continue\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(ty[b:e][ix]@disc/tid[k])\\n return np.mean(vals)\\nif mode=='validation':\\n best=(-1,None)\\n for ww in [[1,0,0],[0,1,0],[0,0,1],[.5,.5,0],[.5,0,.5],[0,.5,.5],[1/3]*3,[.5,.25,.25]]:\\n z=sum(w*p for w,p in zip(ww,preds));sc=ev(z);print('ENSEMBLE',ww,sc,flush=True)\\n if sc>best[0]:best=(sc,ww)\\n json.dump({'score':best[0],'weights':best[1]},open(S+'ensemble_config.json','w'));ww=best[1]\\nelse:ww=json.load(open(S+'ensemble_config.json'))['weights']\\nz=sum(w*p for w,p in zip(ww,preds));out=[]\\nfor k in range(len(U)):\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(ti[b:e][ix])\\nout=np.stack(out);assert eligible(mode if mode=='validation' else 'test')[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});name='validation_ensemble' if mode=='validation' else 'submission';df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble.py validation > /app/solution/ensemble.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:45:20.224Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"516fac\",\"wall_time_seconds\":1.002665704,\"session_id\":26918,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:45:19.184Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3673b8f8887d2a3b53d547d66faec", "usage": { "input_tokens": 72294, "cached_input_tokens": 71296, "cache_write_input_tokens": 0, "output_tokens": 1490, "reasoning_output_tokens": 516, "total_tokens": 73784 }, "turn_token_usage": { "input_tokens": 3150158, "cached_input_tokens": 3042944, "cache_write_input_tokens": 0, "output_tokens": 38585, "reasoning_output_tokens": 17156, "total_tokens": 3188743 }, "thread_token_usage": { "input_tokens": 3150158, "cached_input_tokens": 3042944, "cache_write_input_tokens": 0, "output_tokens": 38585, "reasoning_output_tokens": 17156, "total_tokens": 3188743 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-23T05:45:26.109Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:45:26.109Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:45:27.780Z", "callId": "call_pXzbBee4O1i0BHVLRQVexg8l", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/public_wide.json /app/solution/validation_wide.log /app/solution/tune_reranker.log /app/solution/ensemble.log; ls -lh /app/solution/validation_features_wide.npz\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:45:27.903Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"0a2a38\",\"wall_time_seconds\":0.00000482,\"exit_code\":0,\"original_token_count\":134,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation_wide.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.050787,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nfeatures validation (5313823, 55) positive 11710 likes 1452 time 46.47226810455322\\nTRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\\nTRIAL 15 100 100 300 0.05059082209702879 29.47474765777588\\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:44 /app/solution/validation_features_wide.npz\\n\"}\n{\"current_time\":\"2026-09-23 05:45:27 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:45:27.829Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9bf6b291-c2ed-4f52-bc7b-c48f4ca77c00", "process_id": "11572", "command": [ "/bin/sh", "-lc", "cat /app/solution/public_wide.json /app/solution/validation_wide.log /app/solution/tune_reranker.log /app/solution/ensemble.log; ls -lh /app/solution/validation_features_wide.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/public_wide.json /app/solution/validation_wide.log /app/solution/tune_reranker.log /app/solution/ensemble.log; ls -lh /app/solution/validation_features_wide.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050787,\n \"quality_users\": 4263\n }\n}\nfeatures validation (5313823, 55) positive 11710 likes 1452 time 46.47226810455322\nTRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\nTRIAL 15 100 100 300 0.05059082209702879 29.47474765777588\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:44 /app/solution/validation_features_wide.npz\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050787,\n \"quality_users\": 4263\n }\n}\nfeatures validation (5313823, 55) positive 11710 likes 1452 time 46.47226810455322\nTRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\nTRIAL 15 100 100 300 0.05059082209702879 29.47474765777588\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:44 /app/solution/validation_features_wide.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4820 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050787,\n \"quality_users\": 4263\n }\n}\nfeatures validation (5313823, 55) positive 11710 likes 1452 time 46.47226810455322\nTRIAL 7 200 10 150 0.050407598053797686 50.31549000740051\nTRIAL 15 150 10 150 0.04972316347493722 37.7367103099823\nTRIAL 31 100 100 150 0.05037899995182974 35.4628427028656\nTRIAL 15 100 100 300 0.05059082209702879 29.47474765777588\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:44 /app/solution/validation_features_wide.npz\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:45:27.901Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3677060ac87d2a3b42a9290b65e39", "usage": { "input_tokens": 73840, "cached_input_tokens": 72064, "cache_write_input_tokens": 0, "output_tokens": 171, "reasoning_output_tokens": 84, "total_tokens": 74011 }, "turn_token_usage": { "input_tokens": 3223998, "cached_input_tokens": 3115008, "cache_write_input_tokens": 0, "output_tokens": 38756, "reasoning_output_tokens": 17240, "total_tokens": 3262754 }, "thread_token_usage": { "input_tokens": 3223998, "cached_input_tokens": 3115008, "cache_write_input_tokens": 0, "output_tokens": 38756, "reasoning_output_tokens": 17240, "total_tokens": 3262754 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-23T05:45:52.706Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:45:52.706Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:45:54.206Z", "text": "The final artifact passes all structural checks: 845,600 rows for 8,456 users, with 100 distinct eligible tracks each. Widening the reranker’s candidate pool gave a small validation gain to 0.050787. I’m checking whether averaging complementary rerankers improves it further.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:07.942Z", "callId": "call_Tq4aznCsXKcfNGrxFGt0kW6a", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/test_wide.log 2>&1\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage test --mode rerank --name submission --tag _wide > /app/solution/finalize_test_wide.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/apply_ensemble.py <<'PY'\\nfrom common import *\\nimport pickle,sys,gc\\nstage=sys.argv[1];tag=sys.argv[2] if len(sys.argv)>2 else '';name=sys.argv[3] if len(sys.argv)>3 else ('validation_ensemble'+tag if stage=='validation' else 'submission')\\nd=np.load(S+stage+'_features'+tag+'.npz');F=d['F'];u=d['u'];i=d['i'];weights=json.load(open(S+'ensemble_config.json'))['weights'];files=['reranker.pkl','validation_ensemble0.pkl','validation_ensemble1.pkl'] if stage=='validation' else ['final_reranker.pkl','test_ensemble0.pkl','test_ensemble1.pkl'];z=np.zeros(len(u),np.float64)\\nfor w,f in zip(weights,files):\\n if w:\\n m=pickle.load(open(S+f,'rb'))\\n for st in range(0,len(u),200000):z[st:st+200000]+=w*m.predict(F[st:st+200000])\\nstarts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\\nfor k in range(len(U)):\\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\\nout=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:46:09.169Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"fdb7dc\",\"wall_time_seconds\":1.000957119,\"session_id\":93961,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"3990e6\",\"wall_time_seconds\":0.000004901,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:08.070Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3677821ec87d28a695e9e007ca325", "usage": { "input_tokens": 74314, "cached_input_tokens": 73600, "cache_write_input_tokens": 0, "output_tokens": 1183, "reasoning_output_tokens": 516, "total_tokens": 75497 }, "turn_token_usage": { "input_tokens": 3298312, "cached_input_tokens": 3188608, "cache_write_input_tokens": 0, "output_tokens": 39939, "reasoning_output_tokens": 17756, "total_tokens": 3338251 }, "thread_token_usage": { "input_tokens": 3298312, "cached_input_tokens": 3188608, "cache_write_input_tokens": 0, "output_tokens": 39939, "reasoning_output_tokens": 17756, "total_tokens": 3338251 } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:09.154Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf90e899-fee3-420b-b227-42fc72bc0a56", "process_id": "35193", "command": [ "/bin/sh", "-lc", "cat > /app/solution/apply_ensemble.py <<'PY'\nfrom common import *\nimport pickle,sys,gc\nstage=sys.argv[1];tag=sys.argv[2] if len(sys.argv)>2 else '';name=sys.argv[3] if len(sys.argv)>3 else ('validation_ensemble'+tag if stage=='validation' else 'submission')\nd=np.load(S+stage+'_features'+tag+'.npz');F=d['F'];u=d['u'];i=d['i'];weights=json.load(open(S+'ensemble_config.json'))['weights'];files=['reranker.pkl','validation_ensemble0.pkl','validation_ensemble1.pkl'] if stage=='validation' else ['final_reranker.pkl','test_ensemble0.pkl','test_ensemble1.pkl'];z=np.zeros(len(u),np.float64)\nfor w,f in zip(weights,files):\n if w:\n m=pickle.load(open(S+f,'rb'))\n for st in range(0,len(u),200000):z[st:st+200000]+=w*m.predict(F[st:st+200000])\nstarts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\nfor k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\nout=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/apply_ensemble.py <<'PY'\nfrom common import *\nimport pickle,sys,gc\nstage=sys.argv[1];tag=sys.argv[2] if len(sys.argv)>2 else '';name=sys.argv[3] if len(sys.argv)>3 else ('validation_ensemble'+tag if stage=='validation' else 'submission')\nd=np.load(S+stage+'_features'+tag+'.npz');F=d['F'];u=d['u'];i=d['i'];weights=json.load(open(S+'ensemble_config.json'))['weights'];files=['reranker.pkl','validation_ensemble0.pkl','validation_ensemble1.pkl'] if stage=='validation' else ['final_reranker.pkl','test_ensemble0.pkl','test_ensemble1.pkl'];z=np.zeros(len(u),np.float64)\nfor w,f in zip(weights,files):\n if w:\n m=pickle.load(open(S+f,'rb'))\n for st in range(0,len(u),200000):z[st:st+200000]+=w*m.predict(F[st:st+200000])\nstarts=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];out=[]\nfor k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(i[b:e][ix])\nout=np.stack(out);assert eligible(stage)[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4901 }, "formatted_output": "" } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-23T05:46:11.905Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:46:11.905Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:13.448Z", "callId": "call_GjxJ2FgQG5KhodfiM96iChsN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ensemble.log /app/solution/ensemble_config.json /app/solution/test_wide.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:46:13.563Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"1745df\",\"wall_time_seconds\":0.00000488,\"exit_code\":1,\"original_token_count\":17,\"output\":\"cat: /app/solution/ensemble_config.json: No such file or directory\\n\"}\n{\"current_time\":\"2026-09-23 05:46:13 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:13.508Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f1c702e6-e1c0-4868-bd09-8fe6028b4a4a", "process_id": "21339", "command": [ "/bin/sh", "-lc", "cat /app/solution/ensemble.log /app/solution/ensemble_config.json /app/solution/test_wide.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ensemble.log /app/solution/ensemble_config.json /app/solution/test_wide.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/ensemble_config.json: No such file or directory\n", "stderr": "", "aggregated_output": "cat: /app/solution/ensemble_config.json: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4880 }, "formatted_output": "cat: /app/solution/ensemble_config.json: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:13.561Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367a171a487d29fc5b6cd2e4e8f2a", "usage": { "input_tokens": 75589, "cached_input_tokens": 74112, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 31, "total_tokens": 75681 }, "turn_token_usage": { "input_tokens": 3373901, "cached_input_tokens": 3262720, "cache_write_input_tokens": 0, "output_tokens": 40031, "reasoning_output_tokens": 17787, "total_tokens": 3413932 }, "thread_token_usage": { "input_tokens": 3373901, "cached_input_tokens": 3262720, "cache_write_input_tokens": 0, "output_tokens": 40031, "reasoning_output_tokens": 17787, "total_tokens": 3413932 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-23T05:46:32.007Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:46:32.007Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:40.406Z", "callId": "call_iD1GtoPqBTIsd7w9BS2x2JC3", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\\\"else 'submission';df.to_parquet\\\", \\\"else 'submission_ensemble';df.to_parquet\\\");p.write_text(s)\\nPY\\nwhile [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/ensemble.py test > /app/solution/final_ensemble.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"while [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/apply_ensemble.py validation _wide > /app/solution/ensemble_wide.log 2>&1\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_wide.parquet > /app/solution/public_ensemble_wide.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:46:43.323Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"1fd2f2\",\"wall_time_seconds\":1.001273593,\"session_id\":15459,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"9d4019\",\"wall_time_seconds\":1.000960827,\"session_id\":25523,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:40.890Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367a5d68087d2b22d4e12bc9ecebe", "usage": { "input_tokens": 75771, "cached_input_tokens": 75392, "cache_write_input_tokens": 0, "output_tokens": 804, "reasoning_output_tokens": 516, "total_tokens": 76575 }, "turn_token_usage": { "input_tokens": 3449672, "cached_input_tokens": 3338112, "cache_write_input_tokens": 0, "output_tokens": 40835, "reasoning_output_tokens": 18303, "total_tokens": 3490507 }, "thread_token_usage": { "input_tokens": 3449672, "cached_input_tokens": 3338112, "cache_write_input_tokens": 0, "output_tokens": 40835, "reasoning_output_tokens": 18303, "total_tokens": 3490507 } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:56.666Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cb60a9f8-d3ae-4762-90a5-faa3b5ce502c", "process_id": "26918", "command": [ "/bin/sh", "-lc", "cat > /app/solution/ensemble.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle,sys,gc\nmode=sys.argv[1] if len(sys.argv)>1 else 'validation'\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0\nif mode=='validation':F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];target=v\nelse:\n vu=v['u'];vi=v['ID'];vk=vi[vu]>0;F=np.concatenate([p['F'][keep],v['F'][vk]]);y=np.r_[p['y'][keep],v['y'][vk]];w=np.r_[1/ID[u[keep]],1/vi[vu[vk]]];target=np.load(S+'test_features.npz')\nw/=w.mean();TF=target['F'];tu=target['u'];ti=target['i'];ty=target['y'];tid=target['ID'];starts=np.r_[0,np.flatnonzero(tu[1:]!=tu[:-1])+1,len(tu)]\nbase=pickle.load(open(S+('reranker.pkl' if mode=='validation' else 'final_reranker.pkl'),'rb'));preds=[base.predict(TF)]\nfor j,(leaves,it,reg,minleaf) in enumerate([(7,200,10,150),(31,100,100,150)]):\n m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42);m.fit(F,y,sample_weight=w);pickle.dump(m,open(S+mode+f'_ensemble{j}.pkl','wb'));preds.append(m.predict(TF));print('fit',mode,j,flush=True)\ndef ev(z):\n vals=[]\n for k in range(len(U)):\n if tid[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(ty[b:e][ix]@disc/tid[k])\n return np.mean(vals)\nif mode=='validation':\n best=(-1,None)\n for ww in [[1,0,0],[0,1,0],[0,0,1],[.5,.5,0],[.5,0,.5],[0,.5,.5],[1/3]*3,[.5,.25,.25]]:\n z=sum(w*p for w,p in zip(ww,preds));sc=ev(z);print('ENSEMBLE',ww,sc,flush=True)\n if sc>best[0]:best=(sc,ww)\n json.dump({'score':best[0],'weights':best[1]},open(S+'ensemble_config.json','w'));ww=best[1]\nelse:ww=json.load(open(S+'ensemble_config.json'))['weights']\nz=sum(w*p for w,p in zip(ww,preds));out=[]\nfor k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(ti[b:e][ix])\nout=np.stack(out);assert eligible(mode if mode=='validation' else 'test')[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});name='validation_ensemble' if mode=='validation' else 'submission';df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble.py validation > /app/solution/ensemble.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/ensemble.py <<'PY'\nfrom common import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport pickle,sys,gc\nmode=sys.argv[1] if len(sys.argv)>1 else 'validation'\np=np.load(S+'past_features.npz');v=np.load(S+'validation_features.npz');u=p['u'];ID=p['ID'];keep=ID[u]>0\nif mode=='validation':F=p['F'][keep];y=p['y'][keep];w=1/ID[u[keep]];target=v\nelse:\n vu=v['u'];vi=v['ID'];vk=vi[vu]>0;F=np.concatenate([p['F'][keep],v['F'][vk]]);y=np.r_[p['y'][keep],v['y'][vk]];w=np.r_[1/ID[u[keep]],1/vi[vu[vk]]];target=np.load(S+'test_features.npz')\nw/=w.mean();TF=target['F'];tu=target['u'];ti=target['i'];ty=target['y'];tid=target['ID'];starts=np.r_[0,np.flatnonzero(tu[1:]!=tu[:-1])+1,len(tu)]\nbase=pickle.load(open(S+('reranker.pkl' if mode=='validation' else 'final_reranker.pkl'),'rb'));preds=[base.predict(TF)]\nfor j,(leaves,it,reg,minleaf) in enumerate([(7,200,10,150),(31,100,100,150)]):\n m=HistGradientBoostingRegressor(loss='poisson',learning_rate=.06,max_iter=it,max_leaf_nodes=leaves,min_samples_leaf=minleaf,l2_regularization=reg,max_bins=128,early_stopping=False,random_state=42);m.fit(F,y,sample_weight=w);pickle.dump(m,open(S+mode+f'_ensemble{j}.pkl','wb'));preds.append(m.predict(TF));print('fit',mode,j,flush=True)\ndef ev(z):\n vals=[]\n for k in range(len(U)):\n if tid[k]<=0:continue\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-10)[-10:];ix=ix[np.argsort(-z[b:e][ix])];vals.append(ty[b:e][ix]@disc/tid[k])\n return np.mean(vals)\nif mode=='validation':\n best=(-1,None)\n for ww in [[1,0,0],[0,1,0],[0,0,1],[.5,.5,0],[.5,0,.5],[0,.5,.5],[1/3]*3,[.5,.25,.25]]:\n z=sum(w*p for w,p in zip(ww,preds));sc=ev(z);print('ENSEMBLE',ww,sc,flush=True)\n if sc>best[0]:best=(sc,ww)\n json.dump({'score':best[0],'weights':best[1]},open(S+'ensemble_config.json','w'));ww=best[1]\nelse:ww=json.load(open(S+'ensemble_config.json'))['weights']\nz=sum(w*p for w,p in zip(ww,preds));out=[]\nfor k in range(len(U)):\n b,e=starts[k:k+2];ix=np.argpartition(z[b:e],-100)[-100:];ix=ix[np.argsort(-z[b:e][ix])];out.append(ti[b:e][ix])\nout=np.stack(out);assert eligible(mode if mode=='validation' else 'test')[np.arange(len(U))[:,None],out].all();df=pd.DataFrame({'uid':np.repeat(U,100),'item_id':I[out.ravel()],'rank':np.tile(np.arange(1,101),len(U))});name='validation_ensemble' if mode=='validation' else 'submission';df.to_parquet(S+name+'.tmp.parquet',index=False);os.replace(S+name+'.tmp.parquet',S+name+'.parquet');print('SAVED',name,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble.py validation > /app/solution/ensemble.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 97, "nanos": 445905883 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:06.864Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-78a2ac0b-7d50-4fed-a80c-73f4faf0b74b", "process_id": "93961", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/test_wide.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage test --mode rerank --name submission --tag _wide > /app/solution/finalize_test_wide.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_features.py --stage test --models context30 log20r20 likes0 --top 300 --tag _wide > /app/solution/test_wide.log 2>&1\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py --stage test --mode rerank --name submission --tag _wide > /app/solution/finalize_test_wide.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 58, "nanos": 750535635 }, "formatted_output": "" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-23T05:47:07.510Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:07.510Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:47:11.531Z", "callId": "call_yWAtjkIy5JnzOTzZh4U1TGUa", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\\\"p=np.load(S+'past_features.npz');\\\", \\\"if mode!='validation':\\\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\\\np=np.load(S+'past_features.npz');\\\");p.write_text(s)\\nPY\\ncat /app/solution/ensemble.log /app/solution/test_wide.log /app/solution/final_ensemble.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:47:11.890Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n{\"chunk_id\":\"885814\",\"wall_time_seconds\":0.00000234,\"exit_code\":0,\"original_token_count\":130,\"output\":\"fit validation 0\\nfit validation 1\\nENSEMBLE [1, 0, 0] 0.050409065347536745\\nENSEMBLE [0, 1, 0] 0.050407598053797686\\nENSEMBLE [0, 0, 1] 0.05037899995182974\\nENSEMBLE [0.5, 0.5, 0] 0.050340823002640374\\nENSEMBLE [0.5, 0, 0.5] 0.05061996421887208\\nENSEMBLE [0, 0.5, 0.5] 0.05070159633267858\\nENSEMBLE [0.3333333333333333, 0.3333333333333333, 0.3333333333333333] 0.05047444453057841\\nENSEMBLE [0.5, 0.25, 0.25] 0.050320041750982425\\nSAVED validation_ensemble\\nfeatures test (5327883, 55) positive 0 likes 0 time 34.00972080230713\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:47:11.675Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367c3ab8c87d2b25e5815f2d9bb29", "usage": { "input_tokens": 76669, "cached_input_tokens": 75648, "cache_write_input_tokens": 0, "output_tokens": 567, "reasoning_output_tokens": 422, "total_tokens": 77236 }, "turn_token_usage": { "input_tokens": 3526341, "cached_input_tokens": 3413760, "cache_write_input_tokens": 0, "output_tokens": 41402, "reasoning_output_tokens": 18725, "total_tokens": 3567743 }, "thread_token_usage": { "input_tokens": 3526341, "cached_input_tokens": 3413760, "cache_write_input_tokens": 0, "output_tokens": 41402, "reasoning_output_tokens": 18725, "total_tokens": 3567743 } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:11.734Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e87c074-958f-4fb9-aaa8-a39efd73ac6d", "process_id": "40094", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"p=np.load(S+'past_features.npz');\", \"if mode!='validation':\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\np=np.load(S+'past_features.npz');\");p.write_text(s)\nPY\ncat /app/solution/ensemble.log /app/solution/test_wide.log /app/solution/final_ensemble.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"p=np.load(S+'past_features.npz');\", \"if mode!='validation':\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\np=np.load(S+'past_features.npz');\");p.write_text(s)\nPY\ncat /app/solution/ensemble.log /app/solution/test_wide.log /app/solution/final_ensemble.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit validation 0\nfit validation 1\nENSEMBLE [1, 0, 0] 0.050409065347536745\nENSEMBLE [0, 1, 0] 0.050407598053797686\nENSEMBLE [0, 0, 1] 0.05037899995182974\nENSEMBLE [0.5, 0.5, 0] 0.050340823002640374\nENSEMBLE [0.5, 0, 0.5] 0.05061996421887208\nENSEMBLE [0, 0.5, 0.5] 0.05070159633267858\nENSEMBLE [0.3333333333333333, 0.3333333333333333, 0.3333333333333333] 0.05047444453057841\nENSEMBLE [0.5, 0.25, 0.25] 0.050320041750982425\nSAVED validation_ensemble\nfeatures test (5327883, 55) positive 0 likes 0 time 34.00972080230713\n", "stderr": "", "aggregated_output": "fit validation 0\nfit validation 1\nENSEMBLE [1, 0, 0] 0.050409065347536745\nENSEMBLE [0, 1, 0] 0.050407598053797686\nENSEMBLE [0, 0, 1] 0.05037899995182974\nENSEMBLE [0.5, 0.5, 0] 0.050340823002640374\nENSEMBLE [0.5, 0, 0.5] 0.05061996421887208\nENSEMBLE [0, 0.5, 0.5] 0.05070159633267858\nENSEMBLE [0.3333333333333333, 0.3333333333333333, 0.3333333333333333] 0.05047444453057841\nENSEMBLE [0.5, 0.25, 0.25] 0.050320041750982425\nSAVED validation_ensemble\nfeatures test (5327883, 55) positive 0 likes 0 time 34.00972080230713\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2340 }, "formatted_output": "fit validation 0\nfit validation 1\nENSEMBLE [1, 0, 0] 0.050409065347536745\nENSEMBLE [0, 1, 0] 0.050407598053797686\nENSEMBLE [0, 0, 1] 0.05037899995182974\nENSEMBLE [0.5, 0.5, 0] 0.050340823002640374\nENSEMBLE [0.5, 0, 0.5] 0.05061996421887208\nENSEMBLE [0, 0.5, 0.5] 0.05070159633267858\nENSEMBLE [0.3333333333333333, 0.3333333333333333, 0.3333333333333333] 0.05047444453057841\nENSEMBLE [0.5, 0.25, 0.25] 0.050320041750982425\nSAVED validation_ensemble\nfeatures test (5327883, 55) positive 0 likes 0 time 34.00972080230713\n" } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-23T05:47:30.739Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:30.739Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:47:32.672Z", "callId": "call_FAwi0grbEccw8l2JROd5wALZ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\\\"if mode!='validation':\\\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\\\n\\\",'');p.write_text(s)\\nPY\\ncat /app/solution/public_ensemble_wide.json /app/solution/final_ensemble.log /app/solution/finalize_test_wide.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:47:32.908Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"c33056\",\"wall_time_seconds\":0.046287396,\"exit_code\":1,\"original_token_count\":28,\"output\":\"cat: /app/solution/public_ensemble_wide.json: No such file or directory\\nSaved and validated submission 845600\\n\"}\n{\"current_time\":\"2026-09-23 05:47:32 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:47:32.795Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367e0394c87d28bc9984cd70eeb47", "usage": { "input_tokens": 77547, "cached_input_tokens": 76544, "cache_write_input_tokens": 0, "output_tokens": 641, "reasoning_output_tokens": 501, "total_tokens": 78188 }, "turn_token_usage": { "input_tokens": 3603888, "cached_input_tokens": 3490304, "cache_write_input_tokens": 0, "output_tokens": 42043, "reasoning_output_tokens": 19226, "total_tokens": 3645931 }, "thread_token_usage": { "input_tokens": 3603888, "cached_input_tokens": 3490304, "cache_write_input_tokens": 0, "output_tokens": 42043, "reasoning_output_tokens": 19226, "total_tokens": 3645931 } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:32.893Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a570ba1a-4c82-422f-894e-a997524b159a", "process_id": "28127", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"if mode!='validation':\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\n\",'');p.write_text(s)\nPY\ncat /app/solution/public_ensemble_wide.json /app/solution/final_ensemble.log /app/solution/finalize_test_wide.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"if mode!='validation':\\n while 'features test' not in open(S+'test_wide.log').read():time.sleep(1)\\n\",'');p.write_text(s)\nPY\ncat /app/solution/public_ensemble_wide.json /app/solution/final_ensemble.log /app/solution/finalize_test_wide.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nSaved and validated submission 845600\n", "stderr": "", "aggregated_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nSaved and validated submission 845600\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 46287526 }, "formatted_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nSaved and validated submission 845600\n" } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-23T05:47:48.502Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:48.502Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:47:50.496Z", "text": "The saved submission now uses the wider candidate pool. Two rerankers with different tree settings gave a small additional gain when averaged on validation; their final refits are running. I’ll use that ensemble only if it also improves the wider-pool validation result.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:47:51.192Z", "callId": "call_W2GWkifyH8oLJRCFDbr1WLVJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log /app/solution/final_ensemble.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:47:51.293Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2a5f1b\",\"wall_time_seconds\":0.000005271,\"exit_code\":0,\"original_token_count\":37,\"output\":\"cat: /app/solution/public_ensemble_wide.json: No such file or directory\\nlow 0\\nhigh 0\\nmax 46055\\noom 3\\noom_kill 2\\noom_group_kill 0\\nsock_throttled 8\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:47:51.241Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-abe9943d-6250-43dd-bf6c-00af523e8ea4", "process_id": "59742", "command": [ "/bin/sh", "-lc", "cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log /app/solution/final_ensemble.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log /app/solution/final_ensemble.log; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nlow 0\nhigh 0\nmax 46055\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n", "stderr": "", "aggregated_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nlow 0\nhigh 0\nmax 46055\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5271 }, "formatted_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\nlow 0\nhigh 0\nmax 46055\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 8\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:51.291Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab367f517c487d2a7624501bcfe85ff", "usage": { "input_tokens": 78288, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 482, "reasoning_output_tokens": 356, "total_tokens": 78770 }, "turn_token_usage": { "input_tokens": 3682176, "cached_input_tokens": 3567616, "cache_write_input_tokens": 0, "output_tokens": 42525, "reasoning_output_tokens": 19582, "total_tokens": 3724701 }, "thread_token_usage": { "input_tokens": 3682176, "cached_input_tokens": 3567616, "cache_write_input_tokens": 0, "output_tokens": 42525, "reasoning_output_tokens": 19582, "total_tokens": 3724701 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-23T05:48:11.222Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:11.222Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:48:24.769Z", "callId": "call_Z2MzKA2dbRW0LsJOOrOMyqpC", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/select_final.py <<'PY'\\nfrom pathlib import Path\\nimport json,subprocess,sys,os,shutil\\nS=Path('/app/solution');base=json.load(open(S/'public_wide.json'))['scores']['quality_ndcg@10'];ens=json.load(open(S/'public_ensemble_wide.json'))['scores']['quality_ndcg@10']\\nif ens>base:\\n subprocess.run([sys.executable,str(S/'apply_ensemble.py'),'test','_wide','submission'],check=True)\\n shutil.copyfile(S/'validation_ensemble_wide.parquet',S/'validation.parquet');score=ens;family='ensemble';report='public_ensemble_wide.json'\\nelse:\\n subprocess.run([sys.executable,str(S/'finalize.py'),'--stage','test','--mode','rerank','--name','submission','--tag','_wide'],check=True)\\n shutil.copyfile(S/'validation_wide.parquet',S/'validation.parquet');score=base;family='single';report='public_wide.json'\\nr=json.load(open(S/report));r['model']=str(S/'validation.parquet');json.dump(r,open(S/'public_evaluation.json','w'),indent=2)\\njson.dump({'family':family,'candidate_top_per_model':300,'validation_score':score,'ensemble_weights':json.load(open(S/'ensemble_config.json'))['weights']},open(S/'final_selection.json','w'),indent=2)\\nsubprocess.run([sys.executable,str(S/'audit.py')],check=True)\\nprint('SELECTED',family,score,flush=True)\\nPY\\nwhile [ ! -f /app/solution/public_ensemble_wide.json ] || [ ! -f /app/solution/test_ensemble1.pkl ]; do sleep 1; done\\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/select_final.py > /app/solution/selection.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/final_ensemble.log /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:48:26.035Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"97464b\",\"wall_time_seconds\":1.000442632,\"session_id\":57296,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"de1a31\",\"wall_time_seconds\":0.00000519,\"exit_code\":1,\"original_token_count\":18,\"output\":\"cat: /app/solution/public_ensemble_wide.json: No such file or directory\\n\"}\n{\"current_time\":\"2026-09-23 05:48:26 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:48:24.879Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab368078bf487d284a9a00c76057415", "usage": { "input_tokens": 78887, "cached_input_tokens": 78080, "cache_write_input_tokens": 0, "output_tokens": 1028, "reasoning_output_tokens": 516, "total_tokens": 79915 }, "turn_token_usage": { "input_tokens": 3761063, "cached_input_tokens": 3645696, "cache_write_input_tokens": 0, "output_tokens": 43553, "reasoning_output_tokens": 20098, "total_tokens": 3804616 }, "thread_token_usage": { "input_tokens": 3761063, "cached_input_tokens": 3645696, "cache_write_input_tokens": 0, "output_tokens": 43553, "reasoning_output_tokens": 20098, "total_tokens": 3804616 } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:26.000Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31ed65f0-398a-41a7-a309-d8110e2b6f62", "process_id": "41880", "command": [ "/bin/sh", "-lc", "cat /app/solution/final_ensemble.log /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/final_ensemble.log /app/solution/public_ensemble_wide.json /app/solution/ensemble_wide.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\n", "stderr": "", "aggregated_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5190 }, "formatted_output": "cat: /app/solution/public_ensemble_wide.json: No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:31.120Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8028c9e5-3884-49eb-9fe0-593a34db5787", "process_id": "25523", "command": [ "/bin/sh", "-lc", "while [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/apply_ensemble.py validation _wide > /app/solution/ensemble_wide.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_wide.parquet > /app/solution/public_ensemble_wide.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "while [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/apply_ensemble.py validation _wide > /app/solution/ensemble_wide.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_wide.parquet > /app/solution/public_ensemble_wide.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 108, "nanos": 803815227 }, "formatted_output": "" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-23T05:48:32.603Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:32.603Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:48:37.683Z", "callId": "call_TnP9Mrt1XkQ9u1aFGm4UjTzg", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os\\nfor p in os.listdir('/proc'):\\n if p.isdigit():\\n try:\\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\\n if c.startswith('/opt/task-venv/bin/python'):\\n print(p,c)\\n print(''.join(x for x in open('/proc/'+p+'/status') if x.startswith(('State:','VmRSS:','RssAnon:'))))\\n except OSError:pass\\nPY\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:48:37.803Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"1a1586\",\"wall_time_seconds\":0.00000478,\"exit_code\":0,\"original_token_count\":55,\"output\":\"1676 /opt/task-venv/bin/python /app/solution/ensemble.py test \\nState:\\tS (sleeping)\\nVmRSS:\\t 2456832 kB\\nRssAnon:\\t 2368096 kB\\n\\n1770 /opt/task-venv/bin/python - \\nState:\\tR (running)\\nVmRSS:\\t 9992 kB\\nRssAnon:\\t 3944 kB\\n\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:48:37.743Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2acf1da9-376d-44e6-ba8e-fc20e95a5543", "process_id": "84344", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if c.startswith('/opt/task-venv/bin/python'):\n print(p,c)\n print(''.join(x for x in open('/proc/'+p+'/status') if x.startswith(('State:','VmRSS:','RssAnon:'))))\n except OSError:pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n try:\n c=open('/proc/'+p+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if c.startswith('/opt/task-venv/bin/python'):\n print(p,c)\n print(''.join(x for x in open('/proc/'+p+'/status') if x.startswith(('State:','VmRSS:','RssAnon:'))))\n except OSError:pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1676 /opt/task-venv/bin/python /app/solution/ensemble.py test \nState:\tS (sleeping)\nVmRSS:\t 2456832 kB\nRssAnon:\t 2368096 kB\n\n1770 /opt/task-venv/bin/python - \nState:\tR (running)\nVmRSS:\t 9992 kB\nRssAnon:\t 3944 kB\n\n", "stderr": "", "aggregated_output": "1676 /opt/task-venv/bin/python /app/solution/ensemble.py test \nState:\tS (sleeping)\nVmRSS:\t 2456832 kB\nRssAnon:\t 2368096 kB\n\n1770 /opt/task-venv/bin/python - \nState:\tR (running)\nVmRSS:\t 9992 kB\nRssAnon:\t 3944 kB\n\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4780 }, "formatted_output": "1676 /opt/task-venv/bin/python /app/solution/ensemble.py test \nState:\tS (sleeping)\nVmRSS:\t 2456832 kB\nRssAnon:\t 2368096 kB\n\n1770 /opt/task-venv/bin/python - \nState:\tR (running)\nVmRSS:\t 9992 kB\nRssAnon:\t 3944 kB\n\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:37.800Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3682a3ba487d2af8aaf4f144e47a4", "usage": { "input_tokens": 80042, "cached_input_tokens": 78720, "cache_write_input_tokens": 0, "output_tokens": 274, "reasoning_output_tokens": 116, "total_tokens": 80316 }, "turn_token_usage": { "input_tokens": 3841105, "cached_input_tokens": 3724416, "cache_write_input_tokens": 0, "output_tokens": 43827, "reasoning_output_tokens": 20214, "total_tokens": 3884932 }, "thread_token_usage": { "input_tokens": 3841105, "cached_input_tokens": 3724416, "cache_write_input_tokens": 0, "output_tokens": 43827, "reasoning_output_tokens": 20214, "total_tokens": 3884932 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-23T05:48:43.664Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:43.664Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:48:46.594Z", "callId": "call_SmBvv44bzEMIT2qwk3XJRVuI", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:25523,chars:\"\",yield_time_ms:1000,max_output_tokens:2000}));\ntext(await tools.write_stdin({session_id:15459,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"ls -lh /app/solution/*ensemble* /app/solution/public*; cat /app/solution/final_ensemble.log /app/solution/ensemble_wide.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:48:51.668Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\n{\"chunk_id\":\"c74be8\",\"wall_time_seconds\":0.00000169,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"9b7c06\",\"wall_time_seconds\":5.000559052,\"session_id\":15459,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"540ada\",\"wall_time_seconds\":0.00000479,\"exit_code\":0,\"original_token_count\":305,\"output\":\"-rw-r--r-- 1 agent agent 1.2K Sep 23 05:46 /app/solution/apply_ensemble.py\\n-rw-r--r-- 1 agent agent 447 Sep 23 05:46 /app/solution/ensemble.log\\n-rw-r--r-- 1 agent agent 2.4K Sep 23 05:47 /app/solution/ensemble.py\\n-rw-r--r-- 1 agent agent 56 Sep 23 05:46 /app/solution/ensemble_config.json\\n-rw-r--r-- 1 agent agent 31 Sep 23 05:48 /app/solution/ensemble_wide.log\\n-rw-r--r-- 1 agent agent 11 Sep 23 05:48 /app/solution/final_ensemble.log\\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\\n-rw-r--r-- 1 agent agent 128 Sep 23 05:42 /app/solution/public_evaluation.json\\n-rw-r--r-- 1 agent agent 133 Sep 23 05:45 /app/solution/public_wide.json\\n-rw-r--r-- 1 agent agent 225K Sep 23 05:48 /app/solution/test_ensemble0.pkl\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/validation_ensemble.parquet\\n-rw-r--r-- 1 agent agent 224K Sep 23 05:46 /app/solution/validation_ensemble0.pkl\\n-rw-r--r-- 1 agent agent 402K Sep 23 05:46 /app/solution/validation_ensemble1.pkl\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:48 /app/solution/validation_ensemble_wide.parquet\\nfit test 0\\nSAVED validation_ensemble_wide\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:48:46.668Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36836088487d2b116a82af4d07c4e", "usage": { "input_tokens": 80470, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 220, "reasoning_output_tokens": 98, "total_tokens": 80690 }, "turn_token_usage": { "input_tokens": 3921575, "cached_input_tokens": 3804288, "cache_write_input_tokens": 0, "output_tokens": 44047, "reasoning_output_tokens": 20312, "total_tokens": 3965622 }, "thread_token_usage": { "input_tokens": 3921575, "cached_input_tokens": 3804288, "cache_write_input_tokens": 0, "output_tokens": 44047, "reasoning_output_tokens": 20312, "total_tokens": 3965622 } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:51.653Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c388b2d9-ee41-4aea-8917-108ce70b525b", "process_id": "1152", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/*ensemble* /app/solution/public*; cat /app/solution/final_ensemble.log /app/solution/ensemble_wide.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/*ensemble* /app/solution/public*; cat /app/solution/final_ensemble.log /app/solution/ensemble_wide.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 1.2K Sep 23 05:46 /app/solution/apply_ensemble.py\n-rw-r--r-- 1 agent agent 447 Sep 23 05:46 /app/solution/ensemble.log\n-rw-r--r-- 1 agent agent 2.4K Sep 23 05:47 /app/solution/ensemble.py\n-rw-r--r-- 1 agent agent 56 Sep 23 05:46 /app/solution/ensemble_config.json\n-rw-r--r-- 1 agent agent 31 Sep 23 05:48 /app/solution/ensemble_wide.log\n-rw-r--r-- 1 agent agent 11 Sep 23 05:48 /app/solution/final_ensemble.log\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 128 Sep 23 05:42 /app/solution/public_evaluation.json\n-rw-r--r-- 1 agent agent 133 Sep 23 05:45 /app/solution/public_wide.json\n-rw-r--r-- 1 agent agent 225K Sep 23 05:48 /app/solution/test_ensemble0.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/validation_ensemble.parquet\n-rw-r--r-- 1 agent agent 224K Sep 23 05:46 /app/solution/validation_ensemble0.pkl\n-rw-r--r-- 1 agent agent 402K Sep 23 05:46 /app/solution/validation_ensemble1.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:48 /app/solution/validation_ensemble_wide.parquet\nfit test 0\nSAVED validation_ensemble_wide\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 1.2K Sep 23 05:46 /app/solution/apply_ensemble.py\n-rw-r--r-- 1 agent agent 447 Sep 23 05:46 /app/solution/ensemble.log\n-rw-r--r-- 1 agent agent 2.4K Sep 23 05:47 /app/solution/ensemble.py\n-rw-r--r-- 1 agent agent 56 Sep 23 05:46 /app/solution/ensemble_config.json\n-rw-r--r-- 1 agent agent 31 Sep 23 05:48 /app/solution/ensemble_wide.log\n-rw-r--r-- 1 agent agent 11 Sep 23 05:48 /app/solution/final_ensemble.log\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 128 Sep 23 05:42 /app/solution/public_evaluation.json\n-rw-r--r-- 1 agent agent 133 Sep 23 05:45 /app/solution/public_wide.json\n-rw-r--r-- 1 agent agent 225K Sep 23 05:48 /app/solution/test_ensemble0.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/validation_ensemble.parquet\n-rw-r--r-- 1 agent agent 224K Sep 23 05:46 /app/solution/validation_ensemble0.pkl\n-rw-r--r-- 1 agent agent 402K Sep 23 05:46 /app/solution/validation_ensemble1.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:48 /app/solution/validation_ensemble_wide.parquet\nfit test 0\nSAVED validation_ensemble_wide\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4790 }, "formatted_output": "-rw-r--r-- 1 agent agent 1.2K Sep 23 05:46 /app/solution/apply_ensemble.py\n-rw-r--r-- 1 agent agent 447 Sep 23 05:46 /app/solution/ensemble.log\n-rw-r--r-- 1 agent agent 2.4K Sep 23 05:47 /app/solution/ensemble.py\n-rw-r--r-- 1 agent agent 56 Sep 23 05:46 /app/solution/ensemble_config.json\n-rw-r--r-- 1 agent agent 31 Sep 23 05:48 /app/solution/ensemble_wide.log\n-rw-r--r-- 1 agent agent 11 Sep 23 05:48 /app/solution/final_ensemble.log\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 142 Sep 23 05:48 /app/solution/public_ensemble_wide.json\n-rw-r--r-- 1 agent agent 128 Sep 23 05:42 /app/solution/public_evaluation.json\n-rw-r--r-- 1 agent agent 133 Sep 23 05:45 /app/solution/public_wide.json\n-rw-r--r-- 1 agent agent 225K Sep 23 05:48 /app/solution/test_ensemble0.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/validation_ensemble.parquet\n-rw-r--r-- 1 agent agent 224K Sep 23 05:46 /app/solution/validation_ensemble0.pkl\n-rw-r--r-- 1 agent agent 402K Sep 23 05:46 /app/solution/validation_ensemble1.pkl\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:48 /app/solution/validation_ensemble_wide.parquet\nfit test 0\nSAVED validation_ensemble_wide\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-23T05:48:56.049Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:56.049Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:48:57.725Z", "callId": "call_1gmM7KE9MAo0HExhbHQloUQw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_config.json /app/solution/final_ensemble.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:48:57.855Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5f261d\",\"wall_time_seconds\":0.00000484,\"exit_code\":0,\"original_token_count\":53,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation_ensemble_wide.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.051067,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n{\\\"score\\\": 0.05070159633267858, \\\"weights\\\": [0, 0.5, 0.5]}fit test 0\\n\"}\n{\"current_time\":\"2026-09-23 05:48:57 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:48:57.769Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-68ed2b91-4650-4513-8352-058e58b6cea7", "process_id": "2963", "command": [ "/bin/sh", "-lc", "cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_config.json /app/solution/final_ensemble.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/public_ensemble_wide.json /app/solution/ensemble_config.json /app/solution/final_ensemble.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_ensemble_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{\"score\": 0.05070159633267858, \"weights\": [0, 0.5, 0.5]}fit test 0\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_ensemble_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{\"score\": 0.05070159633267858, \"weights\": [0, 0.5, 0.5]}fit test 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4840 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_ensemble_wide.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{\"score\": 0.05070159633267858, \"weights\": [0, 0.5, 0.5]}fit test 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:57.852Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36843dd5487d29f335a1e249af72f", "usage": { "input_tokens": 81286, "cached_input_tokens": 80256, "cache_write_input_tokens": 0, "output_tokens": 77, "reasoning_output_tokens": 12, "total_tokens": 81363 }, "turn_token_usage": { "input_tokens": 4002861, "cached_input_tokens": 3884544, "cache_write_input_tokens": 0, "output_tokens": 44124, "reasoning_output_tokens": 20324, "total_tokens": 4046985 }, "thread_token_usage": { "input_tokens": 4002861, "cached_input_tokens": 3884544, "cache_write_input_tokens": 0, "output_tokens": 44124, "reasoning_output_tokens": 20324, "total_tokens": 4046985 } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-23T05:49:15.394Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:15.394Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:49:20.600Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21ba5e47-7079-4dbd-9bc5-5b124f09cd1f", "process_id": "15459", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"else 'submission';df.to_parquet\", \"else 'submission_ensemble';df.to_parquet\");p.write_text(s)\nPY\nwhile [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/ensemble.py test > /app/solution/final_ensemble.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/ensemble.py');s=p.read_text().replace(\"else 'submission';df.to_parquet\", \"else 'submission_ensemble';df.to_parquet\");p.write_text(s)\nPY\nwhile [ ! -f /app/solution/ensemble_config.json ]; do sleep 2; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/ensemble.py test > /app/solution/final_ensemble.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 159, "nanos": 443652126 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:49:28.616Z", "callId": "call_p8vUc0fS5jxvzKh4HjLcU6Bi", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/build.py');s=p.read_text();s=s[:s.index(\\\"run('finalize.py'\\\")]+'''for stage in ['validation','test']:\\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0','--top',300,'--tag','_wide')\\nrun('ensemble.py','validation')\\nrun('ensemble.py','test')\\nrun('apply_ensemble.py','validation','_wide','validation')\\nrun('apply_ensemble.py','test','_wide','submission')\\nrun('audit.py')\\nr=subprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True,capture_output=True,text=True)\\n(S/'public_evaluation.json').write_text(r.stdout)\\nprint(r.stdout)\\n''';p.write_text(s)\\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker uses','Two Poisson gradient-boosted rerankers, averaged with equal weights, use').replace('their scores and ranks plus cutoff-correct','their scores and ranks plus cutoff-correct').replace('The reranker is first trained','The rerankers are first trained').replace('the final reranker is refit','the final rerankers are refit').replace('Selected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`.','The final ensemble uses 200 trees with 7 leaves and 100 trees with 31 leaves. It reranks the union of each collaborative model’s top 300 candidates (training uses the top 120). Selection details are in `final_selection.json`. Public validation NDCG@10 is **0.051067** across 4,263 scored users; the official report is `public_evaluation.json`.');p.write_text(s)\\nimport ast\\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\\nprint('All source files parse successfully')\\nPY\\ncat /app/solution/selection.log /app/solution/final_ensemble.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:49:28.812Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"c4a18f\",\"wall_time_seconds\":0.00000577,\"exit_code\":0,\"original_token_count\":21,\"output\":\"All source files parse successfully\\nfit test 0\\nfit test 1\\nSAVED submission_ensemble\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:28.733Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3684a15b487d291322e6538819a8c", "usage": { "input_tokens": 81527, "cached_input_tokens": 81152, "cache_write_input_tokens": 0, "output_tokens": 883, "reasoning_output_tokens": 375, "total_tokens": 82410 }, "turn_token_usage": { "input_tokens": 4084388, "cached_input_tokens": 3965696, "cache_write_input_tokens": 0, "output_tokens": 45007, "reasoning_output_tokens": 20699, "total_tokens": 4129395 }, "thread_token_usage": { "input_tokens": 4084388, "cached_input_tokens": 3965696, "cache_write_input_tokens": 0, "output_tokens": 45007, "reasoning_output_tokens": 20699, "total_tokens": 4129395 } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:28.799Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17712ef1-dbbd-4fe4-85a7-fef71c9416f2", "process_id": "96701", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s=s[:s.index(\"run('finalize.py'\")]+'''for stage in ['validation','test']:\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0','--top',300,'--tag','_wide')\nrun('ensemble.py','validation')\nrun('ensemble.py','test')\nrun('apply_ensemble.py','validation','_wide','validation')\nrun('apply_ensemble.py','test','_wide','submission')\nrun('audit.py')\nr=subprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True,capture_output=True,text=True)\n(S/'public_evaluation.json').write_text(r.stdout)\nprint(r.stdout)\n''';p.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker uses','Two Poisson gradient-boosted rerankers, averaged with equal weights, use').replace('their scores and ranks plus cutoff-correct','their scores and ranks plus cutoff-correct').replace('The reranker is first trained','The rerankers are first trained').replace('the final reranker is refit','the final rerankers are refit').replace('Selected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`.','The final ensemble uses 200 trees with 7 leaves and 100 trees with 31 leaves. It reranks the union of each collaborative model’s top 300 candidates (training uses the top 120). Selection details are in `final_selection.json`. Public validation NDCG@10 is **0.051067** across 4,263 scored users; the official report is `public_evaluation.json`.');p.write_text(s)\nimport ast\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\nprint('All source files parse successfully')\nPY\ncat /app/solution/selection.log /app/solution/final_ensemble.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s=s[:s.index(\"run('finalize.py'\")]+'''for stage in ['validation','test']:\n run('make_features.py','--stage',stage,'--models','context30','log20r20','likes0','--top',300,'--tag','_wide')\nrun('ensemble.py','validation')\nrun('ensemble.py','test')\nrun('apply_ensemble.py','validation','_wide','validation')\nrun('apply_ensemble.py','test','_wide','submission')\nrun('audit.py')\nr=subprocess.run([sys.executable,'/task/evaluate_public.py',str(S/'validation.parquet')],check=True,capture_output=True,text=True)\n(S/'public_evaluation.json').write_text(r.stdout)\nprint(r.stdout)\n''';p.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker uses','Two Poisson gradient-boosted rerankers, averaged with equal weights, use').replace('their scores and ranks plus cutoff-correct','their scores and ranks plus cutoff-correct').replace('The reranker is first trained','The rerankers are first trained').replace('the final reranker is refit','the final rerankers are refit').replace('Selected settings are recorded in `selected_config.json`; the official public score is in `public_evaluation.json`.','The final ensemble uses 200 trees with 7 leaves and 100 trees with 31 leaves. It reranks the union of each collaborative model’s top 300 candidates (training uses the top 120). Selection details are in `final_selection.json`. Public validation NDCG@10 is **0.051067** across 4,263 scored users; the official report is `public_evaluation.json`.');p.write_text(s)\nimport ast\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\nprint('All source files parse successfully')\nPY\ncat /app/solution/selection.log /app/solution/final_ensemble.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "All source files parse successfully\nfit test 0\nfit test 1\nSAVED submission_ensemble\n", "stderr": "", "aggregated_output": "All source files parse successfully\nfit test 0\nfit test 1\nSAVED submission_ensemble\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5770 }, "formatted_output": "All source files parse successfully\nfit test 0\nfit test 1\nSAVED submission_ensemble\n" } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-23T05:49:32.173Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:32.173Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:49:33.789Z", "text": "The final ensemble scored 0.051067 NDCG@10 on the official public evaluator. Both rerankers have been refit using train plus validation history. I’m applying them to the test-eligible pool and running the final artifact audit.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:49:35.349Z", "callId": "call_pyh2A7bTy9cOGcLsH5tZLNzo", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/selection.log /app/solution/final_selection.json; du -sh /app/solution\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:49:35.467Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"current_time\":\"2026-09-23 05:49:35 UTC\"}\n{\"chunk_id\":\"9ee42f\",\"wall_time_seconds\":0.00000415,\"exit_code\":0,\"original_token_count\":22,\"output\":\"cat: /app/solution/final_selection.json: No such file or directory\\n33G\\t/app/solution\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:35.399Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-895b9553-2524-41e1-b4bc-74e302808a12", "process_id": "60972", "command": [ "/bin/sh", "-lc", "cat /app/solution/selection.log /app/solution/final_selection.json; du -sh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/selection.log /app/solution/final_selection.json; du -sh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cat: /app/solution/final_selection.json: No such file or directory\n33G\t/app/solution\n", "stderr": "", "aggregated_output": "cat: /app/solution/final_selection.json: No such file or directory\n33G\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4150 }, "formatted_output": "cat: /app/solution/final_selection.json: No such file or directory\n33G\t/app/solution\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:35.464Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36868fc3c87d286e36294b83ef731", "usage": { "input_tokens": 82489, "cached_input_tokens": 81408, "cache_write_input_tokens": 0, "output_tokens": 163, "reasoning_output_tokens": 44, "total_tokens": 82652 }, "turn_token_usage": { "input_tokens": 4166877, "cached_input_tokens": 4047104, "cache_write_input_tokens": 0, "output_tokens": 45170, "reasoning_output_tokens": 20743, "total_tokens": 4212047 }, "thread_token_usage": { "input_tokens": 4166877, "cached_input_tokens": 4047104, "cache_write_input_tokens": 0, "output_tokens": 45170, "reasoning_output_tokens": 20743, "total_tokens": 4212047 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-23T05:49:52.085Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:52.085Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:49:56.930Z", "callId": "call_cGHjCBsiF3S2TsUHXCViLhRQ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\ns=Path('/app/solution');removed=0\\nfor p in list(s.glob('*.npy'))+list(s.glob('*_X.npz'))+list(s.glob('*_B.npz')):\\n removed+=p.stat().st_size;p.unlink()\\np=s/'incomplete_test_features.npz'\\nif p.exists():removed+=p.stat().st_size;p.unlink()\\nprint('Removed generated matrix intermediates:',round(removed/1e9,2),'GB')\\nPY\\ncat /app/solution/selection.log /app/solution/final_selection.json /app/solution/submission_audit.json\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:49:57.337Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n{\"chunk_id\":\"acabfe\",\"wall_time_seconds\":0.221454781,\"exit_code\":1,\"original_token_count\":65,\"output\":\"Removed generated matrix intermediates: 30.33 GB\\ncat: /app/solution/final_selection.json: No such file or directory\\n{\\n \\\"rows\\\": 845600,\\n \\\"users\\\": 8456,\\n \\\"rows_per_user\\\": 100,\\n \\\"all_test_eligible\\\": true,\\n \\\"ranks\\\": \\\"1-100, unique\\\",\\n \\\"duplicate_items\\\": 0\\n}\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:57.043Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3686fc6c887d2ab04ab9836bd4b01", "usage": { "input_tokens": 82750, "cached_input_tokens": 82304, "cache_write_input_tokens": 0, "output_tokens": 658, "reasoning_output_tokens": 488, "total_tokens": 83408 }, "turn_token_usage": { "input_tokens": 4249627, "cached_input_tokens": 4129408, "cache_write_input_tokens": 0, "output_tokens": 45828, "reasoning_output_tokens": 21231, "total_tokens": 4295455 }, "thread_token_usage": { "input_tokens": 4249627, "cached_input_tokens": 4129408, "cache_write_input_tokens": 0, "output_tokens": 45828, "reasoning_output_tokens": 21231, "total_tokens": 4295455 } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:57.324Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5d478eac-1c4e-42c3-ba80-f059c3a3ece9", "process_id": "88637", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\ns=Path('/app/solution');removed=0\nfor p in list(s.glob('*.npy'))+list(s.glob('*_X.npz'))+list(s.glob('*_B.npz')):\n removed+=p.stat().st_size;p.unlink()\np=s/'incomplete_test_features.npz'\nif p.exists():removed+=p.stat().st_size;p.unlink()\nprint('Removed generated matrix intermediates:',round(removed/1e9,2),'GB')\nPY\ncat /app/solution/selection.log /app/solution/final_selection.json /app/solution/submission_audit.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\ns=Path('/app/solution');removed=0\nfor p in list(s.glob('*.npy'))+list(s.glob('*_X.npz'))+list(s.glob('*_B.npz')):\n removed+=p.stat().st_size;p.unlink()\np=s/'incomplete_test_features.npz'\nif p.exists():removed+=p.stat().st_size;p.unlink()\nprint('Removed generated matrix intermediates:',round(removed/1e9,2),'GB')\nPY\ncat /app/solution/selection.log /app/solution/final_selection.json /app/solution/submission_audit.json" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Removed generated matrix intermediates: 30.33 GB\ncat: /app/solution/final_selection.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}", "stderr": "", "aggregated_output": "Removed generated matrix intermediates: 30.33 GB\ncat: /app/solution/final_selection.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}", "exit_code": 1, "duration": { "secs": 0, "nanos": 221444720 }, "formatted_output": "Removed generated matrix intermediates: 30.33 GB\ncat: /app/solution/final_selection.json: No such file or directory\n{\n \"rows\": 845600,\n \"users\": 8456,\n \"rows_per_user\": 100,\n \"all_test_eligible\": true,\n \"ranks\": \"1-100, unique\",\n \"duplicate_items\": 0\n}" } } }, { "kind": "context", "timestamp": "2026-09-23T05:50:00.995Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1d48aded-e1ac-4379-8002-7aee2e0bd01e", "process_id": "57296", "command": [ "/bin/sh", "-lc", "cat > /app/solution/select_final.py <<'PY'\nfrom pathlib import Path\nimport json,subprocess,sys,os,shutil\nS=Path('/app/solution');base=json.load(open(S/'public_wide.json'))['scores']['quality_ndcg@10'];ens=json.load(open(S/'public_ensemble_wide.json'))['scores']['quality_ndcg@10']\nif ens>base:\n subprocess.run([sys.executable,str(S/'apply_ensemble.py'),'test','_wide','submission'],check=True)\n shutil.copyfile(S/'validation_ensemble_wide.parquet',S/'validation.parquet');score=ens;family='ensemble';report='public_ensemble_wide.json'\nelse:\n subprocess.run([sys.executable,str(S/'finalize.py'),'--stage','test','--mode','rerank','--name','submission','--tag','_wide'],check=True)\n shutil.copyfile(S/'validation_wide.parquet',S/'validation.parquet');score=base;family='single';report='public_wide.json'\nr=json.load(open(S/report));r['model']=str(S/'validation.parquet');json.dump(r,open(S/'public_evaluation.json','w'),indent=2)\njson.dump({'family':family,'candidate_top_per_model':300,'validation_score':score,'ensemble_weights':json.load(open(S/'ensemble_config.json'))['weights']},open(S/'final_selection.json','w'),indent=2)\nsubprocess.run([sys.executable,str(S/'audit.py')],check=True)\nprint('SELECTED',family,score,flush=True)\nPY\nwhile [ ! -f /app/solution/public_ensemble_wide.json ] || [ ! -f /app/solution/test_ensemble1.pkl ]; do sleep 1; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/select_final.py > /app/solution/selection.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/select_final.py <<'PY'\nfrom pathlib import Path\nimport json,subprocess,sys,os,shutil\nS=Path('/app/solution');base=json.load(open(S/'public_wide.json'))['scores']['quality_ndcg@10'];ens=json.load(open(S/'public_ensemble_wide.json'))['scores']['quality_ndcg@10']\nif ens>base:\n subprocess.run([sys.executable,str(S/'apply_ensemble.py'),'test','_wide','submission'],check=True)\n shutil.copyfile(S/'validation_ensemble_wide.parquet',S/'validation.parquet');score=ens;family='ensemble';report='public_ensemble_wide.json'\nelse:\n subprocess.run([sys.executable,str(S/'finalize.py'),'--stage','test','--mode','rerank','--name','submission','--tag','_wide'],check=True)\n shutil.copyfile(S/'validation_wide.parquet',S/'validation.parquet');score=base;family='single';report='public_wide.json'\nr=json.load(open(S/report));r['model']=str(S/'validation.parquet');json.dump(r,open(S/'public_evaluation.json','w'),indent=2)\njson.dump({'family':family,'candidate_top_per_model':300,'validation_score':score,'ensemble_weights':json.load(open(S/'ensemble_config.json'))['weights']},open(S/'final_selection.json','w'),indent=2)\nsubprocess.run([sys.executable,str(S/'audit.py')],check=True)\nprint('SELECTED',family,score,flush=True)\nPY\nwhile [ ! -f /app/solution/public_ensemble_wide.json ] || [ ! -f /app/solution/test_ensemble1.pkl ]; do sleep 1; done\nOPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/select_final.py > /app/solution/selection.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 96, "nanos": 51710581 }, "formatted_output": "" } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-23T05:50:07.558Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:50:07.558Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:50:09.638Z", "callId": "call_H0hXMfzhNrMErC0VoWI3H5fo", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:57296,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/selection.log /app/solution/final_selection.json /app/solution/public_evaluation.json\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:50:09.754Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"402c4c\",\"wall_time_seconds\":0.00000161,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"c38aa3\",\"wall_time_seconds\":0.00000725,\"exit_code\":0,\"original_token_count\":112,\"output\":\"SAVED submission\\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\\nSELECTED ensemble 0.051067\\n{\\n \\\"family\\\": \\\"ensemble\\\",\\n \\\"candidate_top_per_model\\\": 300,\\n \\\"validation_score\\\": 0.051067,\\n \\\"ensemble_weights\\\": [\\n 0,\\n 0.5,\\n 0.5\\n ]\\n}{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.051067,\\n \\\"quality_users\\\": 4263\\n }\\n}\"}\n{\"current_time\":\"2026-09-23 05:50:09 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:50:09.676Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5898ed9f-cb5c-4d14-8cff-e6aed52205f3", "process_id": "15854", "command": [ "/bin/sh", "-lc", "cat /app/solution/selection.log /app/solution/final_selection.json /app/solution/public_evaluation.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/selection.log /app/solution/final_selection.json /app/solution/public_evaluation.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "SAVED submission\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\nSELECTED ensemble 0.051067\n{\n \"family\": \"ensemble\",\n \"candidate_top_per_model\": 300,\n \"validation_score\": 0.051067,\n \"ensemble_weights\": [\n 0,\n 0.5,\n 0.5\n ]\n}{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}", "stderr": "", "aggregated_output": "SAVED submission\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\nSELECTED ensemble 0.051067\n{\n \"family\": \"ensemble\",\n \"candidate_top_per_model\": 300,\n \"validation_score\": 0.051067,\n \"ensemble_weights\": [\n 0,\n 0.5,\n 0.5\n ]\n}{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}", "exit_code": 0, "duration": { "secs": 0, "nanos": 7250 }, "formatted_output": "SAVED submission\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\nSELECTED ensemble 0.051067\n{\n \"family\": \"ensemble\",\n \"candidate_top_per_model\": 300,\n \"validation_score\": 0.051067,\n \"ensemble_weights\": [\n 0,\n 0.5,\n 0.5\n ]\n}{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}" } } }, { "kind": "context", "timestamp": "2026-09-23T05:50:09.752Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab36885830c87d2893ebc79cb6dfce9", "usage": { "input_tokens": 83553, "cached_input_tokens": 82560, "cache_write_input_tokens": 0, "output_tokens": 320, "reasoning_output_tokens": 230, "total_tokens": 83873 }, "turn_token_usage": { "input_tokens": 4333180, "cached_input_tokens": 4211968, "cache_write_input_tokens": 0, "output_tokens": 46148, "reasoning_output_tokens": 21461, "total_tokens": 4379328 }, "thread_token_usage": { "input_tokens": 4333180, "cached_input_tokens": 4211968, "cache_write_input_tokens": 0, "output_tokens": 46148, "reasoning_output_tokens": 21461, "total_tokens": 4379328 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-23T05:50:27.988Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:50:27.988Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:50:36.278Z", "callId": "call_mU6iKRpHB92W8USIvtVF5tHc", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport hashlib,json\\ns=Path('/app/solution')\\nfor p in s.glob('*.npz'):p.unlink()\\nfor n in ['train_mapped.parquet','val_mapped.parquet']:\\n p=s/n\\n if p.exists():p.unlink()\\np=s/'submission.parquet';digest=hashlib.sha256(p.read_bytes()).hexdigest();(s/'submission.sha256').write_text(digest+' submission.parquet\\\\n')\\nr=s/'README.md';r.write_text(r.read_text()+'\\\\nLarge intermediate matrices and feature tables are regenerated by `build.py`; they are not retained in the final directory. `submission_audit.json` records the completed structural and test-eligibility checks.\\\\n')\\nprint('Final submission bytes:',p.stat().st_size,'SHA256:',digest)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n/opt/task-venv/bin/python /app/solution/audit.py\\ndu -sh /app/solution\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:50:37.451Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b243fb\",\"wall_time_seconds\":1.00154217,\"session_id\":97164,\"original_token_count\":27,\"output\":\"Final submission bytes: 1803370 SHA256: 76e0e4b41f84988495191bad48fcd5c7c8a0ae9f29a828b01f97139f6480e804\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:50:36.420Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab3689213c087d2bed084e6d3534659", "usage": { "input_tokens": 84158, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 787, "reasoning_output_tokens": 516, "total_tokens": 84945 }, "turn_token_usage": { "input_tokens": 4417338, "cached_input_tokens": 4295296, "cache_write_input_tokens": 0, "output_tokens": 46935, "reasoning_output_tokens": 21977, "total_tokens": 4464273 }, "thread_token_usage": { "input_tokens": 4417338, "cached_input_tokens": 4295296, "cache_write_input_tokens": 0, "output_tokens": 46935, "reasoning_output_tokens": 21977, "total_tokens": 4464273 } } }, { "kind": "context", "timestamp": "2026-09-23T05:50:41.091Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eac131ae-455b-4aa6-bef7-7e79541380b9", "process_id": "97164", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\ns=Path('/app/solution')\nfor p in s.glob('*.npz'):p.unlink()\nfor n in ['train_mapped.parquet','val_mapped.parquet']:\n p=s/n\n if p.exists():p.unlink()\np=s/'submission.parquet';digest=hashlib.sha256(p.read_bytes()).hexdigest();(s/'submission.sha256').write_text(digest+' submission.parquet\\n')\nr=s/'README.md';r.write_text(r.read_text()+'\\nLarge intermediate matrices and feature tables are regenerated by `build.py`; they are not retained in the final directory. `submission_audit.json` records the completed structural and test-eligibility checks.\\n')\nprint('Final submission bytes:',p.stat().st_size,'SHA256:',digest)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n/opt/task-venv/bin/python /app/solution/audit.py\ndu -sh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\ns=Path('/app/solution')\nfor p in s.glob('*.npz'):p.unlink()\nfor n in ['train_mapped.parquet','val_mapped.parquet']:\n p=s/n\n if p.exists():p.unlink()\np=s/'submission.parquet';digest=hashlib.sha256(p.read_bytes()).hexdigest();(s/'submission.sha256').write_text(digest+' submission.parquet\\n')\nr=s/'README.md';r.write_text(r.read_text()+'\\nLarge intermediate matrices and feature tables are regenerated by `build.py`; they are not retained in the final directory. `submission_audit.json` records the completed structural and test-eligibility checks.\\n')\nprint('Final submission bytes:',p.stat().st_size,'SHA256:',digest)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n/opt/task-venv/bin/python /app/solution/audit.py\ndu -sh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Final submission bytes: 1803370 SHA256: 76e0e4b41f84988495191bad48fcd5c7c8a0ae9f29a828b01f97139f6480e804\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n15M\t/app/solution\n", "stderr": "", "aggregated_output": "Final submission bytes: 1803370 SHA256: 76e0e4b41f84988495191bad48fcd5c7c8a0ae9f29a828b01f97139f6480e804\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n15M\t/app/solution\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 642779841 }, "formatted_output": "Final submission bytes: 1803370 SHA256: 76e0e4b41f84988495191bad48fcd5c7c8a0ae9f29a828b01f97139f6480e804\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051067,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\n15M\t/app/solution\n" } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-23T05:50:44.045Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:50:44.045Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:50:44.500Z", "callId": "call_bFoxvFitfDsyx55dbUT3C0ki", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:97164,chars:\"\",yield_time_ms:1000,max_output_tokens:2000}));\n" }, "result": { "timestamp": "2026-09-23T05:50:44.626Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"a57178\",\"wall_time_seconds\":0.00000237,\"exit_code\":0,\"original_token_count\":69,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.051067,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n{'rows': 845600, 'users': 8456, 'rows_per_user': 100, 'all_test_eligible': True, 'ranks': '1-100, unique', 'duplicate_items': 0}\\n15M\\t/app/solution\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:50:44.623Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab368ada34487d2b3672f638548c669", "usage": { "input_tokens": 85053, "cached_input_tokens": 83968, "cache_write_input_tokens": 0, "output_tokens": 110, "reasoning_output_tokens": 69, "total_tokens": 85163 }, "turn_token_usage": { "input_tokens": 4502391, "cached_input_tokens": 4379264, "cache_write_input_tokens": 0, "output_tokens": 47045, "reasoning_output_tokens": 22046, "total_tokens": 4549436 }, "thread_token_usage": { "input_tokens": 4502391, "cached_input_tokens": 4379264, "cache_write_input_tokens": 0, "output_tokens": 47045, "reasoning_output_tokens": 22046, "total_tokens": 4549436 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-23T05:50:48.803Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:50:48.803Z", "text": "Created [submission.parquet](/app/solution/submission.parquet).\n\n- Public validation NDCG@10: **0.051067**\n- Final models refit using train + validation history.\n- Verified all **845,600 rows**: 100 unique, eligible tracks per user.\n\nRebuild code and instructions: [README.md](/app/solution/README.md)." }, { "kind": "context", "timestamp": "2026-09-23T05:50:48.865Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "session_id": "01a0ccb6-531a-73b1-ae5c-d4f7e8920871", "root_turn_id": "01a0ccb6-5329-71b3-8812-8f474500cbf5", "response_id": "resp_0a1ce710c3d5675e016ab368b4d97c87d29bb911ed0758f245", "usage": { "input_tokens": 85326, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 0, "total_tokens": 85408 }, "turn_token_usage": { "input_tokens": 4587717, "cached_input_tokens": 4464128, "cache_write_input_tokens": 0, "output_tokens": 47127, "reasoning_output_tokens": 22046, "total_tokens": 4634844 }, "thread_token_usage": { "input_tokens": 4587717, "cached_input_tokens": 4464128, "cache_write_input_tokens": 0, "output_tokens": 47127, "reasoning_output_tokens": 22046, "total_tokens": 4634844 } } } ] } ] }