{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "base-astra-high-cli-0-153-4-r04-20260923T050949Z", "taskChecksum": "5593a6fe96897a82023e4dcff504f4feffe02a3a77165ed8866705da5c1a5ea1", "source": { "format": "codex-rollout", "sha256": "41a6b9f1f67248ad8281e0a87839e5371742f455a08aeec1774c06b01a122614", "records": 614, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:AgentMessage": 18, "token_count": 76, "item_completed:Reasoning": 89, "task_complete": 1 } }, "sessionId": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "agentVersion": "0.153.4", "model": "gpt-6-astra", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-23T05:20:41.387Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:41.387Z", "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": "2026-09-23T05:20:42.724Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:42.724Z", "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": "2026-09-23T05:20:42.724Z", "text": "\n /app\n sh\n 2026-09-23\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-23T05:20:42.725Z", "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": "2026-09-23T05:20:42.726Z", "data": { "type": "turn_context", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "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": "2026-09-23T05:20:42.742Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:42.742Z", "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": "2026-09-23T05:20:45.570Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:20:45.570Z", "text": "I’ll inspect the data and evaluation code, save a valid baseline submission, then use the public window to tune the model before refitting on all observed history.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:20:47.256Z", "callId": "call_sNYGY3FjEN8mxfPsQCaQp1oU", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -lh /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py\",\"max_output_tokens\":10000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:20:47.323Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"09a639\",\"wall_time_seconds\":0.00000364,\"exit_code\":0,\"original_token_count\":2651,\"output\":\"total 340M\\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\\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\\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\"}\n{\"current_time\":\"2026-09-23 05:20:47 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:20:47.304Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361aae9c087d28529925784c5e7fd", "usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 0, "total_tokens": 15510 }, "turn_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 0, "total_tokens": 15510 }, "thread_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 0, "total_tokens": 15510 } } }, { "kind": "context", "timestamp": "2026-09-23T05:20:47.315Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8de8b08d-4d3e-4def-9b6e-22f66d604018", "process_id": "89756", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "cat /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\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": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\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": 3640 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\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" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-23T05:20:49.902Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:20:49.902Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:20:55.857Z", "callId": "call_tvLauujoqIBcGTQ9sRJOX4Sx", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"mkdir -p /app/solution && ls -la /app && sed -n '1,240p' /task/evaluate_public.py && python - <<'PY'\\nimport pyarrow.parquet as pq,numpy as np,os\\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\\nPY\",\"max_output_tokens\":7500}));\n" }, "result": { "timestamp": "2026-09-23T05:20:55.917Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"db5a06\",\"wall_time_seconds\":0.00000379,\"exit_code\":1,\"original_token_count\":1512,\"output\":\"total 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\\n#!/usr/bin/env python3\\n\\\"\\\"\\\"Score a validation-stage ranking against public discovery labels.\\n\\n``build_task.py`` publishes this module with the solver-visible task files. It\\nloads target users, validation eligibility, and validation-window events from\\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\\ncommand line. Stage-independent mechanics come from the adjacent public\\n``ranking_evaluation.py`` module. The builder installs the same checked-in\\nmodule in the private judge, so public and private scoring mechanics cannot\\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\\nan organic listen with at least 80 percent completion; a like takes precedence\\nwhen both events exist.\\n\\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\\nignored. Quality is graded NDCG@10, averaged only over users who have at least\\none eligible relevant item in the validation window.\\n\\nThe evaluator prints a JSON object containing the model path and scores. It\\ndoes not apply the task's pass threshold; its purpose is model development and\\nselection. The private judge applies the threshold to the final submission.\\n\\nUsage::\\n\\n python evaluate_public.py \\n\\\"\\\"\\\"\\nfrom __future__ import annotations\\n\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nimport pandas as pd\\n\\nfrom ranking_evaluation import (\\n CandidateSet,\\n RankingValidationError,\\n ranked_items,\\n read_ranking,\\n score_rankings,\\n validate_ranking,\\n)\\n\\n\\nHERE = Path(__file__).resolve().parent\\nDATA = HERE / \\\"data\\\"\\nLISTEN_STRONG = 80\\n\\n\\ndef validation_truth(\\n eligibility: CandidateSet, targets: set[int]\\n) -> dict[int, dict[int, int]]:\\n \\\"\\\"\\\"Build cutoff-correct graded relevance from public validation events.\\n\\n Only target-user events for eligible candidates are retained. Organic\\n listens completed to at least 80 percent receive grade 1, and organic likes\\n receive grade 3. Repeated user-item events are collapsed, and processing\\n likes second ensures that grade 3 wins over grade 1.\\n\\n Args:\\n eligibility: Validation-cutoff candidate membership service.\\n targets: Complete set of target user IDs.\\n\\n Returns:\\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\\n relevant event are omitted.\\n \\\"\\\"\\\"\\n listens = pd.read_parquet(DATA / \\\"val_listens.parquet\\\")\\n likes = pd.read_parquet(DATA / \\\"val_likes.parquet\\\")\\n strong = listens[\\n listens.uid.isin(targets)\\n & (listens.is_organic == 1)\\n & (listens.played_ratio_pct >= LISTEN_STRONG)\\n ][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n organic_likes = likes[\\n likes.uid.isin(targets) & (likes.is_organic == 1)\\n ][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n\\n graded: dict[int, dict[int, int]] = {}\\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\\n uid, item = int(raw_uid), int(raw_item)\\n if eligibility.is_eligible(uid, item):\\n graded.setdefault(uid, {})[item] = 1\\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\\n uid, item = int(raw_uid), int(raw_item)\\n if eligibility.is_eligible(uid, item):\\n graded.setdefault(uid, {})[item] = 3\\n return graded\\n\\n\\ndef load_submission(\\n path: Path, eligibility: CandidateSet, targets: set[int]\\n) -> dict[int, list[int]]:\\n \\\"\\\"\\\"Load and validate a public validation ranking.\\n\\n Files ending in ``.parquet`` are read as parquet; every other suffix is\\n read as CSV. The required columns must be integer-valued, cover exactly the\\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\\n duplicate user-item pairs, and contain only cutoff-eligible items.\\n\\n Args:\\n path: Ranking file to read.\\n eligibility: Validation-cutoff candidate membership service.\\n targets: Exact set of users the ranking must cover.\\n\\n Returns:\\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\\n\\n Raises:\\n ValueError: If the ranking violates the required schema or invariants.\\n \\\"\\\"\\\"\\n frame = validate_ranking(read_ranking(path), targets)\\n if not all(\\n eligibility.is_eligible(uid, item)\\n for uid, item in frame[[\\\"uid\\\", \\\"item_id\\\"]].itertuples(index=False, name=None)\\n ):\\n raise RankingValidationError(\\n \\\"submission contains an item rejected by eligibility service\\\"\\n )\\n return ranked_items(frame)\\n\\n\\ndef score(\\n ranked: dict[int, list[int]],\\n graded: dict[int, dict[int, int]],\\n) -> dict[str, float | int]:\\n \\\"\\\"\\\"Aggregate per-user NDCG@10 over users with public relevance labels.\\n\\n Args:\\n ranked: Ranked item IDs for every target user.\\n graded: Eligible validation relevance keyed by user and item.\\n\\n Returns:\\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\\n \\\"\\\"\\\"\\n return score_rankings(ranked, graded)\\n\\n\\ndef main() -> None:\\n \\\"\\\"\\\"Run the public evaluator CLI and print its JSON score report.\\\"\\\"\\\"\\n targets_frame = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n targets = set(targets_frame.uid.astype(int))\\n eligibility = CandidateSet(DATA / \\\"validation_eligible_candidates.npz\\\")\\n graded = validation_truth(eligibility, targets)\\n\\n if len(sys.argv) != 2:\\n raise SystemExit(\\n \\\"usage: evaluate_public.py \\\"\\n )\\n path = Path(sys.argv[1])\\n ranked = load_submission(path, eligibility, targets)\\n print(json.dumps({\\\"model\\\": str(path), \\\"scores\\\": score(ranked, graded)}, indent=2))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\nTraceback (most recent call last):\\n File \\\"\\\", line 1, in \\nModuleNotFoundError: No module named 'pyarrow'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:20:55.911Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361af8a1c87d299d4dd82c0094613", "usage": { "input_tokens": 18787, "cached_input_tokens": 15232, "cache_write_input_tokens": 0, "output_tokens": 228, "reasoning_output_tokens": 19, "total_tokens": 19015 }, "turn_token_usage": { "input_tokens": 34193, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 332, "reasoning_output_tokens": 19, "total_tokens": 34525 }, "thread_token_usage": { "input_tokens": 34193, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 332, "reasoning_output_tokens": 19, "total_tokens": 34525 } } }, { "kind": "context", "timestamp": "2026-09-23T05:20:55.912Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-95e9af6a-3b17-440b-bd82-39f409cf1dac", "process_id": "30470", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && ls -la /app && sed -n '1,240p' /task/evaluate_public.py && python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && ls -la /app && sed -n '1,240p' /task/evaluate_public.py && python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "stderr": "", "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3790 }, "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-23T05:20:57.791Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:20:57.791Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:20:58.884Z", "callId": "call_A1XXUS15HAFEWjTHXtkiVeMh", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"which python python3; ls -la /opt /app/solution; printenv | sort\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-23T05:20:58.950Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"71a1be\",\"wall_time_seconds\":0.00000469,\"exit_code\":0,\"original_token_count\":263,\"output\":\"/usr/local/bin/python\\n/usr/local/bin/python3\\n/app/solution:\\ntotal 8\\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 .\\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 ..\\n\\n/opt:\\ntotal 16\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\\ndrwxr-xr-x 4 root root 4096 Sep 23 05:18 codex-home\\ndr-xr-xr-x 1 root root 4096 Sep 23 05:18 task-venv\\nCODEX_CI=1\\nCODEX_HOME=/tmp/codex-home\\nCODEX_SESSION_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\\nCODEX_THREAD_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\\nCOLORTERM=\\nGH_PAGER=cat\\nGIT_PAGER=cat\\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\\nHOME=/home/agent\\nHOSTNAME=e8152c5dadea\\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": "2026-09-23T05:20:58.923Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e1117c8e-3806-43d4-9138-7fdea7e407eb", "process_id": "1786", "command": [ "/bin/sh", "-lc", "which python python3; ls -la /opt /app/solution; printenv | sort" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python python3; ls -la /opt /app/solution; 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 05:18 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:18 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:18 task-venv\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCODEX_THREAD_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e8152c5dadea\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 05:18 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:18 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:18 task-venv\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCODEX_THREAD_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e8152c5dadea\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": 4690 }, "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 05:18 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:18 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:18 task-venv\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCODEX_THREAD_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e8152c5dadea\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": "context", "timestamp": "2026-09-23T05:20:58.948Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361b8242c87d2bb9f16fb82e8a4fc", "usage": { "input_tokens": 20659, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 20713 }, "turn_token_usage": { "input_tokens": 54852, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 386, "reasoning_output_tokens": 26, "total_tokens": 55238 }, "thread_token_usage": { "input_tokens": 54852, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 386, "reasoning_output_tokens": 26, "total_tokens": 55238 } } }, { "kind": "tool", "timestamp": "2026-09-23T05:21:08.046Z", "callId": "call_tFCgSYchIFsdCBQe9L6BDKFk", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq,numpy as np,os\\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\\nPY\",\"max_output_tokens\":5000}));\n" }, "result": { "timestamp": "2026-09-23T05:21:09.110Z", "content": "Script completed\nWall time 1.1 seconds\nOutput:\n\n{\"chunk_id\":\"bad4b0\",\"wall_time_seconds\":0.896021933,\"exit_code\":0,\"original_token_count\":1435,\"output\":\"train_listens 43484159 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\\n0 100 39420 8326270 0 100 170\\n1 100 39420 1441281 0 100 105\\n2 100 39625 286361 0 100 185\\n3 100 40110 732449 0 100 240\\n4 100 40360 3397170 0 46 130\\ntrain_likes 814831 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic\\n0 100 44755 732449 1\\n1 100 1155860 6568592 0\\n2 100 1259125 5411243 1\\n3 100 1260005 7371186 0\\n4 100 1263935 4943655 0\\ntrain_dislikes 101075 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic\\n0 100 3087560 9170134 1\\n1 100 3936560 8661238 1\\n2 100 20432825 4927727 1\\n3 400 2980800 399811 1\\n4 400 3157520 1575108 1\\nval_listens 1501697 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\\n0 100 24943430 9321269 0 100 195\\n1 100 24943605 5452713 0 100 175\\n2 100 24943765 6448777 0 100 160\\n3 100 24951960 6008056 0 8 175\\n4 100 25246895 7968587 0 45 565\\ntarget_users 8456 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional double field_id=-1 recent_positive_listens;\\n optional int64 field_id=-1 history_positive_listens;\\n}\\n\\n uid recent_positive_listens history_positive_listens\\n0 100 844.0 2604\\n1 200 50.0 286\\n2 300 168.0 359\\n3 600 1985.0 2423\\n4 700 1776.0 6114\\ncandidate_catalog 30000 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int64 field_id=-1 positive_listens;\\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\\n optional int32 field_id=-1 popularity_rank;\\n}\\n\\n item_id positive_listens organic_positive_listens popularity_rank\\n0 5862961 26615 20620 1\\n1 6901374 25536 17347 2\\n2 5635052 24691 17194 3\\n3 3542184 24075 17699 4\\n4 9378983 24036 18195 5\\nvalidation_preference_state 469124 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional binary field_id=-1 state (String);\\n}\\n\\n uid item_id state\\n0 100 410868 liked\\n1 100 732449 liked\\n2 100 982903 liked\\n3 100 1730273 liked\\n4 100 2621343 liked\\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\\ncpus 8\\nMemTotal: 15988968 kB\\nMemFree: 5620740 kB\\nMemAvailable: 14824416 kB\\nBuffers: 176408 kB\\nCached: 8945184 kB\\nSwapCached: 0 kB\\nActive: 709972 kB\\nInactive: 8893384 kB\\nActive(anon): 508204 kB\\nInactive(anon): 0 kB\\nActive(file): 20\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:08.107Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361bb5dc087d2a8e71ed48e35d3a7", "usage": { "input_tokens": 21250, "cached_input_tokens": 20480, "cache_write_input_tokens": 0, "output_tokens": 184, "reasoning_output_tokens": 0, "total_tokens": 21434 }, "turn_token_usage": { "input_tokens": 76102, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 570, "reasoning_output_tokens": 26, "total_tokens": 76672 }, "thread_token_usage": { "input_tokens": 76102, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 570, "reasoning_output_tokens": 26, "total_tokens": 76672 } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:09.105Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6bbea632-8666-42c4-a3a4-db5b6af225a7", "process_id": "18627", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\ntrain_dislikes 101075 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 3087560 9170134 1\n1 100 3936560 8661238 1\n2 100 20432825 4927727 1\n3 400 2980800 399811 1\n4 400 3157520 1575108 1\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal: 15988968 kB\nMemFree: 5620740 kB\nMemAvailable: 14824416 kB\nBuffers: 176408 kB\nCached: 8945184 kB\nSwapCached: 0 kB\nActive: 709972 kB\nInactive: 8893384 kB\nActive(anon): 508204 kB\nInactive(anon): 0 kB\nActive(file): 20\n", "stderr": "", "aggregated_output": "train_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\ntrain_dislikes 101075 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 3087560 9170134 1\n1 100 3936560 8661238 1\n2 100 20432825 4927727 1\n3 400 2980800 399811 1\n4 400 3157520 1575108 1\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal: 15988968 kB\nMemFree: 5620740 kB\nMemAvailable: 14824416 kB\nBuffers: 176408 kB\nCached: 8945184 kB\nSwapCached: 0 kB\nActive: 709972 kB\nInactive: 8893384 kB\nActive(anon): 508204 kB\nInactive(anon): 0 kB\nActive(file): 20\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 896016804 }, "formatted_output": "train_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\ntrain_dislikes 101075 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 3087560 9170134 1\n1 100 3936560 8661238 1\n2 100 20432825 4927727 1\n3 400 2980800 399811 1\n4 400 3157520 1575108 1\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal: 15988968 kB\nMemFree: 5620740 kB\nMemAvailable: 14824416 kB\nBuffers: 176408 kB\nCached: 8945184 kB\nSwapCached: 0 kB\nActive: 709972 kB\nInactive: 8893384 kB\nActive(anon): 508204 kB\nInactive(anon): 0 kB\nActive(file): 20\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-23T05:21:13.002Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:21:13.002Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:21:32.811Z", "callId": "call_NO0GGdk6lhx23knZwnIkk2dV", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/inspect.py <<'PY'\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\\nz=np.load('/task/data/validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little');print('eligible',m.sum(1).min(),np.percentile(m.sum(1),[25,50,75,100]),flush=True)\\nui=pd.Index(u);ii=pd.Index(it)\\nseen=np.zeros(m.shape,np.uint8); pos=seen.copy();recent=seen.copy();last=np.zeros(m.shape,np.int32)\\nfor batch in pq.ParquetFile('/task/data/train_listens.parquet').iter_batches(batch_size=1000000):\\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];v=d.played_ratio_pct.values[ok]>=50;t=d.timestamp.values[ok];seen[r,c]=1;pos[r[v],c[v]]=1;v&=t>24786800-90*86400;recent[r[v],c[v]]=1;np.maximum.at(last,(r,c),t)\\nprint('seen eligible',np.sum(seen*m),'positive eligible',np.sum(pos*m),'recent eligible',np.sum(recent*m),flush=True)\\nfor name in ['validation_preference_state','test_preference_state']:\\n d=pd.read_parquet('/task/data/'+name+'.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);d=d[ok];print(name,d.groupby('state').size(), 'eligible',d[m[r[ok],c[ok]]>0].groupby('state').size(),flush=True)\\nfor name in ['val_listens','val_likes']:\\n d=pd.read_parquet('/task/data/'+name+'.parquet');d=d[d.is_organic==1];\\n if 'played_ratio_pct' in d:d=d[d.played_ratio_pct>=80]\\n d=d.drop_duplicates(['uid','item_id']);r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=m[r,c]>0;r=r[ok];c=c[ok];print(name,'relevant',len(r),'seen',seen[r,c].mean(),'positive',pos[r,c].mean(),'recent',recent[r,c].mean(),'age',np.percentile((24786800-last[r,c])/86400,[0,25,50,75,100]),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:21:33.421Z", "content": "Script completed\nWall time 0.6 seconds\nOutput:\n\n{\"chunk_id\":\"f87794\",\"wall_time_seconds\":0.441296534,\"exit_code\":1,\"original_token_count\":243,\"output\":\"eligible 24657 [28682.75 29242. 29644. 30000. ]\\nTraceback (most recent call last):\\n File \\\"/app/solution/inspect.py\\\", line 1, in \\n import numpy as np,pandas as pd,pyarrow.parquet as pq\\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 3, in \\n ui=pd.Index(u);ii=pd.Index(it)\\n ^^^^^^^^\\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:32.852Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361c54a4487d29a54e2ecc6a1ece0", "usage": { "input_tokens": 23309, "cached_input_tokens": 21120, "cache_write_input_tokens": 0, "output_tokens": 741, "reasoning_output_tokens": 72, "total_tokens": 24050 }, "turn_token_usage": { "input_tokens": 99411, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 1311, "reasoning_output_tokens": 98, "total_tokens": 100722 }, "thread_token_usage": { "input_tokens": 99411, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 1311, "reasoning_output_tokens": 98, "total_tokens": 100722 } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:33.417Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cab017c8-fbb5-4d7e-be2c-b32531711ee2", "process_id": "35239", "command": [ "/bin/sh", "-lc", "cat > /app/solution/inspect.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nz=np.load('/task/data/validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little');print('eligible',m.sum(1).min(),np.percentile(m.sum(1),[25,50,75,100]),flush=True)\nui=pd.Index(u);ii=pd.Index(it)\nseen=np.zeros(m.shape,np.uint8); pos=seen.copy();recent=seen.copy();last=np.zeros(m.shape,np.int32)\nfor batch in pq.ParquetFile('/task/data/train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];v=d.played_ratio_pct.values[ok]>=50;t=d.timestamp.values[ok];seen[r,c]=1;pos[r[v],c[v]]=1;v&=t>24786800-90*86400;recent[r[v],c[v]]=1;np.maximum.at(last,(r,c),t)\nprint('seen eligible',np.sum(seen*m),'positive eligible',np.sum(pos*m),'recent eligible',np.sum(recent*m),flush=True)\nfor name in ['validation_preference_state','test_preference_state']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);d=d[ok];print(name,d.groupby('state').size(), 'eligible',d[m[r[ok],c[ok]]>0].groupby('state').size(),flush=True)\nfor name in ['val_listens','val_likes']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');d=d[d.is_organic==1];\n if 'played_ratio_pct' in d:d=d[d.played_ratio_pct>=80]\n d=d.drop_duplicates(['uid','item_id']);r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=m[r,c]>0;r=r[ok];c=c[ok];print(name,'relevant',len(r),'seen',seen[r,c].mean(),'positive',pos[r,c].mean(),'recent',recent[r,c].mean(),'age',np.percentile((24786800-last[r,c])/86400,[0,25,50,75,100]),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,pyarrow.parquet as pq\nz=np.load('/task/data/validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little');print('eligible',m.sum(1).min(),np.percentile(m.sum(1),[25,50,75,100]),flush=True)\nui=pd.Index(u);ii=pd.Index(it)\nseen=np.zeros(m.shape,np.uint8); pos=seen.copy();recent=seen.copy();last=np.zeros(m.shape,np.int32)\nfor batch in pq.ParquetFile('/task/data/train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];v=d.played_ratio_pct.values[ok]>=50;t=d.timestamp.values[ok];seen[r,c]=1;pos[r[v],c[v]]=1;v&=t>24786800-90*86400;recent[r[v],c[v]]=1;np.maximum.at(last,(r,c),t)\nprint('seen eligible',np.sum(seen*m),'positive eligible',np.sum(pos*m),'recent eligible',np.sum(recent*m),flush=True)\nfor name in ['validation_preference_state','test_preference_state']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);d=d[ok];print(name,d.groupby('state').size(), 'eligible',d[m[r[ok],c[ok]]>0].groupby('state').size(),flush=True)\nfor name in ['val_listens','val_likes']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');d=d[d.is_organic==1];\n if 'played_ratio_pct' in d:d=d[d.played_ratio_pct>=80]\n d=d.drop_duplicates(['uid','item_id']);r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=m[r,c]>0;r=r[ok];c=c[ok];print(name,'relevant',len(r),'seen',seen[r,c].mean(),'positive',pos[r,c].mean(),'recent',recent[r,c].mean(),'age',np.percentile((24786800-last[r,c])/86400,[0,25,50,75,100]),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nTraceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd,pyarrow.parquet as pq\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 3, in \n ui=pd.Index(u);ii=pd.Index(it)\n ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n", "stderr": "", "aggregated_output": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nTraceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd,pyarrow.parquet as pq\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 3, in \n ui=pd.Index(u);ii=pd.Index(it)\n ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 441294574 }, "formatted_output": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nTraceback (most recent call last):\n File \"/app/solution/inspect.py\", line 1, in \n import numpy as np,pandas as pd,pyarrow.parquet as pq\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 3, in \n ui=pd.Index(u);ii=pd.Index(it)\n ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:21:38.577Z", "callId": "call_KDLzczIWsZv79SkUvAfoGsQB", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"mv /app/solution/inspect.py /app/solution/explore.py && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/explore.py\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:21:39.741Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a6e0a6\",\"wall_time_seconds\":1.000248367,\"session_id\":63643,\"original_token_count\":14,\"output\":\"eligible 24657 [28682.75 29242. 29644. 30000. ]\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:38.609Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361dda04887d2a175a2bf4195e208", "usage": { "input_tokens": 24408, "cached_input_tokens": 23168, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 0, "total_tokens": 24479 }, "turn_token_usage": { "input_tokens": 123819, "cached_input_tokens": 110720, "cache_write_input_tokens": 0, "output_tokens": 1382, "reasoning_output_tokens": 98, "total_tokens": 125201 }, "thread_token_usage": { "input_tokens": 123819, "cached_input_tokens": 110720, "cache_write_input_tokens": 0, "output_tokens": 1382, "reasoning_output_tokens": 98, "total_tokens": 125201 } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:45.018Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-89295ace-e9b2-4be1-82ba-ff1ac1d872af", "process_id": "63643", "command": [ "/bin/sh", "-lc", "mv /app/solution/inspect.py /app/solution/explore.py && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/explore.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mv /app/solution/inspect.py /app/solution/explore.py && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/explore.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nseen eligible 0 positive eligible 0 recent eligible 0\nvalidation_preference_state state\ndisliked 50689\nliked 418435\ndtype: int64 eligible Series([], dtype: int64)\ntest_preference_state state\ndisliked 52117\nliked 432659\ndtype: int64 eligible state\ndisliked 675\nliked 9010\ndtype: int64\nval_listens relevant 35597 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\nval_likes relevant 4417 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\n", "stderr": "", "aggregated_output": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nseen eligible 0 positive eligible 0 recent eligible 0\nvalidation_preference_state state\ndisliked 50689\nliked 418435\ndtype: int64 eligible Series([], dtype: int64)\ntest_preference_state state\ndisliked 52117\nliked 432659\ndtype: int64 eligible state\ndisliked 675\nliked 9010\ndtype: int64\nval_listens relevant 35597 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\nval_likes relevant 4417 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 278795430 }, "formatted_output": "eligible 24657 [28682.75 29242. 29644. 30000. ]\nseen eligible 0 positive eligible 0 recent eligible 0\nvalidation_preference_state state\ndisliked 50689\nliked 418435\ndtype: int64 eligible Series([], dtype: int64)\ntest_preference_state state\ndisliked 52117\nliked 432659\ndtype: int64 eligible state\ndisliked 675\nliked 9010\ndtype: int64\nval_listens relevant 35597 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\nval_likes relevant 4417 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-23T05:21:50.274Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:21:50.274Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:21:51.914Z", "text": "There are 8,456 target users and 30,000 catalog tracks. I’m checking how much eligible relevance comes from previously heard tracks; that will determine how much weight to give repeat listening versus collaborative discovery.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:21:53.956Z", "callId": "call_5eIAsAQwtQOsfPGaefXeJPbq", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:63643,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\ntext(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:21:54.017Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"4c8c65\",\"wall_time_seconds\":0.00000591,\"exit_code\":0,\"original_token_count\":144,\"output\":\"seen eligible 0 positive eligible 0 recent eligible 0\\nvalidation_preference_state state\\ndisliked 50689\\nliked 418435\\ndtype: int64 eligible Series([], dtype: int64)\\ntest_preference_state state\\ndisliked 52117\\nliked 432659\\ndtype: int64 eligible state\\ndisliked 675\\nliked 9010\\ndtype: int64\\nval_listens relevant 35597 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\\nval_likes relevant 4417 seen 0.0 positive 0.0 recent 0.0 age [286.88425926 286.88425926 286.88425926 286.88425926 286.88425926]\\n\"}\n{\"chunk_id\":\"cfaa4c\",\"wall_time_seconds\":0.000003491,\"exit_code\":0,\"original_token_count\":7,\"output\":\"8589934592\\n400000 100000\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:21:53.993Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50e665e6-3fc7-4c73-86b0-1373e062e15d", "process_id": "76725", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.max", "name": "memory.max", "path": "/sys/fs/cgroup/memory.max" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/cpu.max", "name": "cpu.max", "path": "/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": 3491 }, "formatted_output": "8589934592\n400000 100000\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:21:54.016Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361e3f0dc87d28a54a07392f81b48", "usage": { "input_tokens": 24565, "cached_input_tokens": 24192, "cache_write_input_tokens": 0, "output_tokens": 354, "reasoning_output_tokens": 227, "total_tokens": 24919 }, "turn_token_usage": { "input_tokens": 148384, "cached_input_tokens": 134912, "cache_write_input_tokens": 0, "output_tokens": 1736, "reasoning_output_tokens": 325, "total_tokens": 150120 }, "thread_token_usage": { "input_tokens": 148384, "cached_input_tokens": 134912, "cache_write_input_tokens": 0, "output_tokens": 1736, "reasoning_output_tokens": 325, "total_tokens": 150120 } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-23T05:22:13.578Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:22:13.578Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:22:42.519Z", "callId": "call_6qhK15kYhPDFusnhQ1uL55kA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare.py <<'PY'\\nimport os,time\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\\nfrom scipy import sparse\\nfrom pathlib import Path\\nP=Path('/app/solution');D=Path('/task/data'); t=time.time()\\nz=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; targets=z['uids']\\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());np.savez(P/'ids.npz',uids=uids,items=items,targets=targets);print('users',len(uids),flush=True)\\nui=pd.Index(uids);ii=pd.Index(items);shape=(len(uids),len(items))\\nfor stage in ['train','val']:\\n mats={k:sparse.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','short','strong','skip']}\\n cutoff=24786800 if stage=='train' else 24786800+1800+7*86400\\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];d=d[ok];age=(cutoff-d.timestamp.to_numpy(dtype=np.float64))/86400;pos=d.played_ratio_pct.values>=50;org=d.is_organic.values==1;strong=d.played_ratio_pct.values>=80\\n vals={'count':pos,'organic':pos*org,'recent':pos*np.exp(-age/60),'short':pos*np.exp(-age/14),'strong':strong*(0.3+0.7*org)*np.exp(-age/90),'skip':(~pos)*np.exp(-age/60)}\\n for k,v in vals.items():\\n v=v.astype(np.float32);nz=v>0;mats[k]+=sparse.csr_matrix((v[nz],(r[nz],c[nz])),shape=shape)\\n for k,x in mats.items():sparse.save_npz(P/f'{stage}_{k}.npz',x)\\n d=pd.read_parquet(D/f'{stage}_likes.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=shape);sparse.save_npz(P/f'{stage}_likes.npz',x)\\n print(stage,'done',time.time()-t,[(k,x.nnz) for k,x in mats.items()],flush=True)\\n# Always keep an eligible final baseline, using observed train + val organic popularity.\\npop=np.asarray((sparse.load_npz(P/'train_organic.npz')+4*sparse.load_npz(P/'val_organic.npz')).sum(axis=0)).ravel()\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz');rows=[];scores=pop if stage=='test' else np.asarray(sparse.load_npz(P/'train_organic.npz').sum(axis=0)).ravel()\\n for r,u in enumerate(z['uids']):\\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(eligible,scores,-np.inf); ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix],kind='stable')];rows.append(ix)\\n out=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[np.asarray(rows).ravel()],'rank':np.tile(np.arange(1,101),len(rows))});out.to_parquet(P/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\\nprint('baseline saved',time.time()-t,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:22:43.685Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"adf705\",\"wall_time_seconds\":1.001157979,\"session_id\":64561,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:22:42.557Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab361f24dd087d297d67adc76d806df", "usage": { "input_tokens": 25224, "cached_input_tokens": 24448, "cache_write_input_tokens": 0, "output_tokens": 1490, "reasoning_output_tokens": 516, "total_tokens": 26714 }, "turn_token_usage": { "input_tokens": 173608, "cached_input_tokens": 159360, "cache_write_input_tokens": 0, "output_tokens": 3226, "reasoning_output_tokens": 841, "total_tokens": 176834 }, "thread_token_usage": { "input_tokens": 173608, "cached_input_tokens": 159360, "cache_write_input_tokens": 0, "output_tokens": 3226, "reasoning_output_tokens": 841, "total_tokens": 176834 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-23T05:22:47.779Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:22:47.779Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:22:48.804Z", "callId": "call_9t08W2CD4rXHGgdgc9jjgWwY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/prepare.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:22:48.862Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"4c5954\",\"wall_time_seconds\":0.00000436,\"exit_code\":0,\"original_token_count\":3,\"output\":\"users 9238\\n\"}\n{\"current_time\":\"2026-09-23 05:22:48 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:22:48.840Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-267951fc-386d-4855-a22b-8968bff2f295", "process_id": "17522", "command": [ "/bin/sh", "-lc", "cat /app/solution/prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/prepare.log", "name": "prepare.log", "path": "/app/solution/prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 9238\n", "stderr": "", "aggregated_output": "users 9238\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "users 9238\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:22:48.860Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36223e37c87d2993f47806e32df84", "usage": { "input_tokens": 26771, "cached_input_tokens": 25088, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 76, "total_tokens": 26892 }, "turn_token_usage": { "input_tokens": 200379, "cached_input_tokens": 184448, "cache_write_input_tokens": 0, "output_tokens": 3347, "reasoning_output_tokens": 917, "total_tokens": 203726 }, "thread_token_usage": { "input_tokens": 200379, "cached_input_tokens": 184448, "cache_write_input_tokens": 0, "output_tokens": 3347, "reasoning_output_tokens": 917, "total_tokens": 203726 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-23T05:23:05.231Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:23:05.231Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:23:05.751Z", "callId": "call_OPxkmHIr7bSmzgqwdz5G8tEX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/prepare.log; df -h /app/solution\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:23:05.792Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"3d20c2\",\"wall_time_seconds\":0.00000352,\"exit_code\":0,\"original_token_count\":25,\"output\":\"users 9238\\nFilesystem Size Used Avail Use% Mounted on\\noverlay 96G 11G 86G 11% /\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:05.782Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36229228887d2ac5a9255de506cc0", "usage": { "input_tokens": 26971, "cached_input_tokens": 26624, "cache_write_input_tokens": 0, "output_tokens": 500, "reasoning_output_tokens": 454, "total_tokens": 27471 }, "turn_token_usage": { "input_tokens": 227350, "cached_input_tokens": 211072, "cache_write_input_tokens": 0, "output_tokens": 3847, "reasoning_output_tokens": 1371, "total_tokens": 231197 }, "thread_token_usage": { "input_tokens": 227350, "cached_input_tokens": 211072, "cache_write_input_tokens": 0, "output_tokens": 3847, "reasoning_output_tokens": 1371, "total_tokens": 231197 } } }, { "kind": "context", "timestamp": "2026-09-23T05:23:05.788Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ca99d0f5-9f01-4405-8516-1cd253a311c2", "process_id": "13189", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/prepare.log; df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/prepare.log; df -h /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 9238\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 11G 86G 11% /\n", "stderr": "", "aggregated_output": "users 9238\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 11G 86G 11% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3520 }, "formatted_output": "users 9238\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 11G 86G 11% /\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-23T05:23:07.824Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:23:07.824Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:23:09.348Z", "text": "The eligibility masks exclude every previously heard track. I’ll focus on collaborative models that transfer preferences to unheard tracks, and compare how full history, recent listening, and explicit likes contribute to validation quality.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:23:53.526Z", "callId": "call_SMp2TONvf2cPeJxkZtyqEZi3", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/model.py <<'PY'\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\\nimport numpy as np,pandas as pd,time,json,sys\\nfrom scipy import sparse,linalg\\nfrom pathlib import Path\\nP=Path('/app/solution');D=Path('/task/data');ids=np.load(P/'ids.npz');uids=ids['uids'];items=ids['items'];targets=ids['targets'];ui=pd.Index(uids);ii=pd.Index(items);tr=ui.get_indexer(targets);N=len(uids);M=len(items)\\ndef load(k,stage='validation'):\\n x=sparse.load_npz(P/f'train_{k}.npz')\\n if stage=='test':\\n if k in ['recent','short','strong','skip']:x*=np.exp(-(7+1800/86400)/{'recent':60,'short':14,'strong':90,'skip':60}[k])\\n x+=sparse.load_npz(P/f'val_{k}.npz')\\n return x\\nclass Eval:\\n def __init__(self,stage='validation'):\\n self.stage=stage;z=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(z['uids'],targets);self.packed=z['packed_eligible'];self.discount=1/np.log2(np.arange(2,12));self.gold=sparse.csr_matrix((len(targets),M),dtype=np.float32);self.idcg=np.ones(len(targets));self.active=np.zeros(len(targets),bool)\\n if stage=='validation':\\n gg=[]\\n for name in ['listens','likes']:\\n d=pd.read_parquet(D/f'val_{name}.parquet');d=d[d.is_organic==1]\\n if name=='listens':d=d[d.played_ratio_pct>=80]\\n r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=((self.packed[r,c//8]>>(c%8))&1)>0;r=r[ok];c=c[ok];gg.append(pd.DataFrame({'r':r,'c':c,'g':1 if name=='listens' else 7}))\\n g=pd.concat(gg).groupby(['r','c']).g.max().reset_index();self.gold=sparse.csr_matrix((g.g,(g.r,g.c)),shape=(len(targets),M),dtype=np.float32)\\n for r in range(len(targets)):\\n v=self.gold.data[self.gold.indptr[r]:self.gold.indptr[r+1]]\\n if len(v):self.active[r]=True;self.idcg[r]=(np.sort(v)[::-1][:10]*self.discount[:min(10,len(v))]).sum()\\n def mask(self,start,end):return np.unpackbits(self.packed[start:end],axis=1,count=M,bitorder='little').astype(bool)\\n def run(self,scores,name,save=True):\\n ranks=[];metric=[]\\n for st in range(0,len(targets),128):\\n en=min(st+128,len(targets));s=np.asarray(scores[st:en],dtype=np.float32).copy();s[~self.mask(st,en)]=-np.inf;ix=np.argpartition(s,-100,axis=1)[:,-100:];v=np.take_along_axis(s,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1);ranks.append(ix)\\n g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\\n result=float(np.asarray(metric)[self.active].mean()) if self.active.any() else 0.;print(name,'NDCG',result,flush=True)\\n if save:\\n ranks=np.concatenate(ranks);pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranks.ravel()],'rank':np.tile(np.arange(1,101),len(targets))}).to_parquet(P/f'{name}.parquet',index=False)\\n return result\\n\\ndef ease(stage,variant,reg):\\n t=time.time();x=load('count',stage);x.data[:]=1\\n if variant=='log':x=load('count',stage);x.data=np.log1p(x.data)\\n if variant=='recent':x=load('recent',stage);x.data=np.sqrt(x.data)\\n if variant=='strong':x=load('strong',stage);x.data=np.log1p(x.data)\\n if variant=='hybrid':\\n x.data[:]=0.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=load('likes',stage);l.data[:]=1;x+=l\\n if variant=='liked':\\n l=load('likes',stage);l.data[:]=1;x+=2*l\\n print('matrix',variant,x.shape,x.nnz,'time',time.time()-t,flush=True)\\n # A user-space Woodbury solve yields the exact EASE unseen-item scores.\\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;print('kernel',time.time()-t,flush=True)\\n chol=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False);print('chol',time.time()-t,flush=True)\\n name=f'{stage}_ease_{variant}_{reg}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,M,1000):\\n en=min(M,st+1000);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(chol,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\\nif __name__=='__main__':ease(sys.argv[1],sys.argv[2],float(sys.argv[3]))\\nPY\\ncat /app/solution/prepare.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:23:53.573Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"818ab6\",\"wall_time_seconds\":0.00000284,\"exit_code\":0,\"original_token_count\":78,\"output\":\"users 9238\\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:53.563Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3623a126887d2ab2007de8140e32d", "usage": { "input_tokens": 27565, "cached_input_tokens": 26752, "cache_write_input_tokens": 0, "output_tokens": 1544, "reasoning_output_tokens": 16, "total_tokens": 29109 }, "turn_token_usage": { "input_tokens": 254915, "cached_input_tokens": 237824, "cache_write_input_tokens": 0, "output_tokens": 5391, "reasoning_output_tokens": 1387, "total_tokens": 260306 }, "thread_token_usage": { "input_tokens": 254915, "cached_input_tokens": 237824, "cache_write_input_tokens": 0, "output_tokens": 5391, "reasoning_output_tokens": 1387, "total_tokens": 260306 } } }, { "kind": "context", "timestamp": "2026-09-23T05:23:53.568Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-275438f3-0af0-4720-bc63-07a111a7195f", "process_id": "2958", "command": [ "/bin/sh", "-lc", "cat > /app/solution/model.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,time,json,sys\nfrom scipy import sparse,linalg\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');ids=np.load(P/'ids.npz');uids=ids['uids'];items=ids['items'];targets=ids['targets'];ui=pd.Index(uids);ii=pd.Index(items);tr=ui.get_indexer(targets);N=len(uids);M=len(items)\ndef load(k,stage='validation'):\n x=sparse.load_npz(P/f'train_{k}.npz')\n if stage=='test':\n if k in ['recent','short','strong','skip']:x*=np.exp(-(7+1800/86400)/{'recent':60,'short':14,'strong':90,'skip':60}[k])\n x+=sparse.load_npz(P/f'val_{k}.npz')\n return x\nclass Eval:\n def __init__(self,stage='validation'):\n self.stage=stage;z=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(z['uids'],targets);self.packed=z['packed_eligible'];self.discount=1/np.log2(np.arange(2,12));self.gold=sparse.csr_matrix((len(targets),M),dtype=np.float32);self.idcg=np.ones(len(targets));self.active=np.zeros(len(targets),bool)\n if stage=='validation':\n gg=[]\n for name in ['listens','likes']:\n d=pd.read_parquet(D/f'val_{name}.parquet');d=d[d.is_organic==1]\n if name=='listens':d=d[d.played_ratio_pct>=80]\n r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=((self.packed[r,c//8]>>(c%8))&1)>0;r=r[ok];c=c[ok];gg.append(pd.DataFrame({'r':r,'c':c,'g':1 if name=='listens' else 7}))\n g=pd.concat(gg).groupby(['r','c']).g.max().reset_index();self.gold=sparse.csr_matrix((g.g,(g.r,g.c)),shape=(len(targets),M),dtype=np.float32)\n for r in range(len(targets)):\n v=self.gold.data[self.gold.indptr[r]:self.gold.indptr[r+1]]\n if len(v):self.active[r]=True;self.idcg[r]=(np.sort(v)[::-1][:10]*self.discount[:min(10,len(v))]).sum()\n def mask(self,start,end):return np.unpackbits(self.packed[start:end],axis=1,count=M,bitorder='little').astype(bool)\n def run(self,scores,name,save=True):\n ranks=[];metric=[]\n for st in range(0,len(targets),128):\n en=min(st+128,len(targets));s=np.asarray(scores[st:en],dtype=np.float32).copy();s[~self.mask(st,en)]=-np.inf;ix=np.argpartition(s,-100,axis=1)[:,-100:];v=np.take_along_axis(s,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1);ranks.append(ix)\n g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n result=float(np.asarray(metric)[self.active].mean()) if self.active.any() else 0.;print(name,'NDCG',result,flush=True)\n if save:\n ranks=np.concatenate(ranks);pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranks.ravel()],'rank':np.tile(np.arange(1,101),len(targets))}).to_parquet(P/f'{name}.parquet',index=False)\n return result\n\ndef ease(stage,variant,reg):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='log':x=load('count',stage);x.data=np.log1p(x.data)\n if variant=='recent':x=load('recent',stage);x.data=np.sqrt(x.data)\n if variant=='strong':x=load('strong',stage);x.data=np.log1p(x.data)\n if variant=='hybrid':\n x.data[:]=0.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=load('likes',stage);l.data[:]=1;x+=l\n if variant=='liked':\n l=load('likes',stage);l.data[:]=1;x+=2*l\n print('matrix',variant,x.shape,x.nnz,'time',time.time()-t,flush=True)\n # A user-space Woodbury solve yields the exact EASE unseen-item scores.\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;print('kernel',time.time()-t,flush=True)\n chol=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False);print('chol',time.time()-t,flush=True)\n name=f'{stage}_ease_{variant}_{reg}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(M,st+1000);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(chol,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':ease(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\ncat /app/solution/prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/model.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,time,json,sys\nfrom scipy import sparse,linalg\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');ids=np.load(P/'ids.npz');uids=ids['uids'];items=ids['items'];targets=ids['targets'];ui=pd.Index(uids);ii=pd.Index(items);tr=ui.get_indexer(targets);N=len(uids);M=len(items)\ndef load(k,stage='validation'):\n x=sparse.load_npz(P/f'train_{k}.npz')\n if stage=='test':\n if k in ['recent','short','strong','skip']:x*=np.exp(-(7+1800/86400)/{'recent':60,'short':14,'strong':90,'skip':60}[k])\n x+=sparse.load_npz(P/f'val_{k}.npz')\n return x\nclass Eval:\n def __init__(self,stage='validation'):\n self.stage=stage;z=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(z['uids'],targets);self.packed=z['packed_eligible'];self.discount=1/np.log2(np.arange(2,12));self.gold=sparse.csr_matrix((len(targets),M),dtype=np.float32);self.idcg=np.ones(len(targets));self.active=np.zeros(len(targets),bool)\n if stage=='validation':\n gg=[]\n for name in ['listens','likes']:\n d=pd.read_parquet(D/f'val_{name}.parquet');d=d[d.is_organic==1]\n if name=='listens':d=d[d.played_ratio_pct>=80]\n r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=((self.packed[r,c//8]>>(c%8))&1)>0;r=r[ok];c=c[ok];gg.append(pd.DataFrame({'r':r,'c':c,'g':1 if name=='listens' else 7}))\n g=pd.concat(gg).groupby(['r','c']).g.max().reset_index();self.gold=sparse.csr_matrix((g.g,(g.r,g.c)),shape=(len(targets),M),dtype=np.float32)\n for r in range(len(targets)):\n v=self.gold.data[self.gold.indptr[r]:self.gold.indptr[r+1]]\n if len(v):self.active[r]=True;self.idcg[r]=(np.sort(v)[::-1][:10]*self.discount[:min(10,len(v))]).sum()\n def mask(self,start,end):return np.unpackbits(self.packed[start:end],axis=1,count=M,bitorder='little').astype(bool)\n def run(self,scores,name,save=True):\n ranks=[];metric=[]\n for st in range(0,len(targets),128):\n en=min(st+128,len(targets));s=np.asarray(scores[st:en],dtype=np.float32).copy();s[~self.mask(st,en)]=-np.inf;ix=np.argpartition(s,-100,axis=1)[:,-100:];v=np.take_along_axis(s,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1);ranks.append(ix)\n g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n result=float(np.asarray(metric)[self.active].mean()) if self.active.any() else 0.;print(name,'NDCG',result,flush=True)\n if save:\n ranks=np.concatenate(ranks);pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranks.ravel()],'rank':np.tile(np.arange(1,101),len(targets))}).to_parquet(P/f'{name}.parquet',index=False)\n return result\n\ndef ease(stage,variant,reg):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='log':x=load('count',stage);x.data=np.log1p(x.data)\n if variant=='recent':x=load('recent',stage);x.data=np.sqrt(x.data)\n if variant=='strong':x=load('strong',stage);x.data=np.log1p(x.data)\n if variant=='hybrid':\n x.data[:]=0.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=load('likes',stage);l.data[:]=1;x+=l\n if variant=='liked':\n l=load('likes',stage);l.data[:]=1;x+=2*l\n print('matrix',variant,x.shape,x.nnz,'time',time.time()-t,flush=True)\n # A user-space Woodbury solve yields the exact EASE unseen-item scores.\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;print('kernel',time.time()-t,flush=True)\n chol=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False);print('chol',time.time()-t,flush=True)\n name=f'{stage}_ease_{variant}_{reg}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(M,st+1000);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(chol,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':ease(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\ncat /app/solution/prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n", "stderr": "", "aggregated_output": "users 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2840 }, "formatted_output": "users 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-23T05:23:55.944Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:23:55.944Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:23:59.075Z", "callId": "call_qkn4UzL8QLCmM4Z9iR4moS9I", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation binary 300 > /app/solution/ease_binary.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:24:01.406Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"2c4ca7\",\"wall_time_seconds\":1.000968567,\"session_id\":3071,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"766e11\",\"wall_time_seconds\":1.00140291,\"session_id\":72877,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:24:01 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:23:59.122Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36269d9c887d2811d9100a81cff78", "usage": { "input_tokens": 29283, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 126, "reasoning_output_tokens": 13, "total_tokens": 29409 }, "turn_token_usage": { "input_tokens": 284198, "cached_input_tokens": 265216, "cache_write_input_tokens": 0, "output_tokens": 5517, "reasoning_output_tokens": 1400, "total_tokens": 289715 }, "thread_token_usage": { "input_tokens": 284198, "cached_input_tokens": 265216, "cache_write_input_tokens": 0, "output_tokens": 5517, "reasoning_output_tokens": 1400, "total_tokens": 289715 } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:01.648Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ea5855b6-5c9f-4d99-9cac-40eec8f21b2e", "process_id": "72877", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\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 ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\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 ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n", "exit_code": 1, "duration": { "secs": 1, "nanos": 247554139 }, "formatted_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\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 ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n" } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-23T05:24:22.141Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:22.141Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:24:24.081Z", "callId": "call_yOgNlFLkn7YS9ViUQP1n7ev4", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/ease_binary.log; tail /app/solution/prepare.log\",\"max_output_tokens\":1500}));\ntext(await tools.write_stdin({session_id:72877,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:24:24.151Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"867126\",\"wall_time_seconds\":0.000005021,\"exit_code\":0,\"original_token_count\":105,\"output\":\"matrix binary (9238, 30000) 5564926 time 0.16855263710021973\\nkernel 7.266043663024902\\nchol 8.841795206069946\\nusers 9238\\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\\n\"}\n{\"chunk_id\":\"a6c077\",\"wall_time_seconds\":0.00000275,\"exit_code\":1,\"original_token_count\":353,\"output\":\"Traceback (most recent call last):\\n File \\\"/task/evaluate_public.py\\\", line 157, in \\n main()\\n File \\\"/task/evaluate_public.py\\\", line 152, in main\\n ranked = load_submission(path, eligibility, targets)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/task/evaluate_public.py\\\", line 113, in load_submission\\n frame = validate_ranking(read_ranking(path), targets)\\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 ^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\\\", line 669, in read_parquet\\n return impl.read(\\n ^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\\\", line 258, in read\\n path_or_handle, handles, filesystem = _get_path_or_handle(\\n ^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\\\", line 141, in _get_path_or_handle\\n handles = get_handle(\\n ^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\\\", line 882, in get_handle\\n handle = open(handle, ioargs.mode)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^\\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:24.138Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3da9252a-edc9-4d26-b128-cf51221c457b", "process_id": "37431", "command": [ "/bin/sh", "-lc", "tail /app/solution/ease_binary.log; tail /app/solution/prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/ease_binary.log", "name": "ease_binary.log", "path": "/app/solution/ease_binary.log" }, { "type": "read", "cmd": "tail /app/solution/prepare.log", "name": "prepare.log", "path": "/app/solution/prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nusers 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n", "stderr": "", "aggregated_output": "matrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nusers 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5021 }, "formatted_output": "matrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nusers 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:24.145Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362719d2487d29fb509a242dd1379", "usage": { "input_tokens": 29520, "cached_input_tokens": 29056, "cache_write_input_tokens": 0, "output_tokens": 592, "reasoning_output_tokens": 516, "total_tokens": 30112 }, "turn_token_usage": { "input_tokens": 313718, "cached_input_tokens": 294272, "cache_write_input_tokens": 0, "output_tokens": 6109, "reasoning_output_tokens": 1916, "total_tokens": 319827 }, "thread_token_usage": { "input_tokens": 313718, "cached_input_tokens": 294272, "cache_write_input_tokens": 0, "output_tokens": 6109, "reasoning_output_tokens": 1916, "total_tokens": 319827 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-23T05:24:26.613Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:26.613Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:24:29.124Z", "callId": "call_7KZaDZNszrK6OvINhwfSx623", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ps -o pid,pcpu,rss,etime,args -C python; tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet\",\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:64561,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:24:34.204Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\n{\"chunk_id\":\"073d90\",\"wall_time_seconds\":0.00000573,\"exit_code\":2,\"original_token_count\":52,\"output\":\"/bin/sh: 1: ps: not found\\nmatrix binary (9238, 30000) 5564926 time 0.16855263710021973\\nkernel 7.266043663024902\\nchol 8.841795206069946\\nls: cannot access '/app/solution/*parquet': No such file or directory\\n\"}\n{\"chunk_id\":\"08c987\",\"wall_time_seconds\":5.000980277,\"session_id\":64561,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:29.168Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60b6d0c6-c1e0-4d6d-8ab3-7d75f6d33aea", "process_id": "12724", "command": [ "/bin/sh", "-lc", "ps -o pid,pcpu,rss,etime,args -C python; tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -o pid,pcpu,rss,etime,args -C python; tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: ps: not found\nmatrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nls: cannot access '/app/solution/*parquet': No such file or directory\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\nmatrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nls: cannot access '/app/solution/*parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 5730 }, "formatted_output": "/bin/sh: 1: ps: not found\nmatrix binary (9238, 30000) 5564926 time 0.16855263710021973\nkernel 7.266043663024902\nchol 8.841795206069946\nls: cannot access '/app/solution/*parquet': No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:29.199Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362887e4487d294b85f6a923bc10f", "usage": { "input_tokens": 30747, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 23, "total_tokens": 30864 }, "turn_token_usage": { "input_tokens": 344465, "cached_input_tokens": 323584, "cache_write_input_tokens": 0, "output_tokens": 6226, "reasoning_output_tokens": 1939, "total_tokens": 350691 }, "thread_token_usage": { "input_tokens": 344465, "cached_input_tokens": 323584, "cache_write_input_tokens": 0, "output_tokens": 6226, "reasoning_output_tokens": 1939, "total_tokens": 350691 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-23T05:24:41.618Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:41.618Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:24:44.108Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b58b061e-3885-4a96-b22a-8cb2ea9a45da", "process_id": "3071", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation binary 300 > /app/solution/ease_binary.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation binary 300 > /app/solution/ease_binary.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 44, "nanos": 868065010 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:24:50.630Z", "callId": "call_Bql46fQqlEo45Ur5fWGKYo2T", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,signal\\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\\n try:\\n args=p.read_bytes().split(b'\\\\0')\\n if len(args)>1 and args[1]==b'/app/solution/prepare.py':os.kill(int(p.parent.name),signal.SIGTERM)\\n except (OSError,ValueError):pass\\np=Path('/app/solution/prepare.py');s=p.read_text().replace(\\\"rows=[];scores=pop\\\", \\\"packed=z['packed_eligible'];rows=[];scores=pop\\\").replace(\\\"z['packed_eligible'][r]\\\",\\\"packed[r]\\\");p.write_text(s)\\ns=s[s.index('# Always keep'):];Path('/app/solution/baseline.py').write_text(\\\"import numpy as np,pandas as pd\\\\nfrom scipy import sparse\\\\nfrom pathlib import Path\\\\nP=Path('/app/solution');D=Path('/task/data');items=np.load(P/'ids.npz')['items']\\\\nimport time\\\\nt=time.time()\\\\n\\\"+s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/baseline.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:24:51.796Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"79b266\",\"wall_time_seconds\":1.001051084,\"session_id\":31865,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:50.709Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36292657c87d2946a3bcac45b475c", "usage": { "input_tokens": 31028, "cached_input_tokens": 30592, "cache_write_input_tokens": 0, "output_tokens": 498, "reasoning_output_tokens": 188, "total_tokens": 31526 }, "turn_token_usage": { "input_tokens": 375493, "cached_input_tokens": 354176, "cache_write_input_tokens": 0, "output_tokens": 6724, "reasoning_output_tokens": 2127, "total_tokens": 382217 }, "thread_token_usage": { "input_tokens": 375493, "cached_input_tokens": 354176, "cache_write_input_tokens": 0, "output_tokens": 6724, "reasoning_output_tokens": 2127, "total_tokens": 382217 } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:50.778Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b89b50ea-1083-48f7-8faf-50f8d6c64e60", "process_id": "64561", "command": [ "/bin/sh", "-lc", "cat > /app/solution/prepare.py <<'PY'\nimport os,time\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data'); t=time.time()\nz=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; targets=z['uids']\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());np.savez(P/'ids.npz',uids=uids,items=items,targets=targets);print('users',len(uids),flush=True)\nui=pd.Index(uids);ii=pd.Index(items);shape=(len(uids),len(items))\nfor stage in ['train','val']:\n mats={k:sparse.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','short','strong','skip']}\n cutoff=24786800 if stage=='train' else 24786800+1800+7*86400\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];d=d[ok];age=(cutoff-d.timestamp.to_numpy(dtype=np.float64))/86400;pos=d.played_ratio_pct.values>=50;org=d.is_organic.values==1;strong=d.played_ratio_pct.values>=80\n vals={'count':pos,'organic':pos*org,'recent':pos*np.exp(-age/60),'short':pos*np.exp(-age/14),'strong':strong*(0.3+0.7*org)*np.exp(-age/90),'skip':(~pos)*np.exp(-age/60)}\n for k,v in vals.items():\n v=v.astype(np.float32);nz=v>0;mats[k]+=sparse.csr_matrix((v[nz],(r[nz],c[nz])),shape=shape)\n for k,x in mats.items():sparse.save_npz(P/f'{stage}_{k}.npz',x)\n d=pd.read_parquet(D/f'{stage}_likes.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=shape);sparse.save_npz(P/f'{stage}_likes.npz',x)\n print(stage,'done',time.time()-t,[(k,x.nnz) for k,x in mats.items()],flush=True)\n# Always keep an eligible final baseline, using observed train + val organic popularity.\npop=np.asarray((sparse.load_npz(P/'train_organic.npz')+4*sparse.load_npz(P/'val_organic.npz')).sum(axis=0)).ravel()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz');rows=[];scores=pop if stage=='test' else np.asarray(sparse.load_npz(P/'train_organic.npz').sum(axis=0)).ravel()\n for r,u in enumerate(z['uids']):\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(eligible,scores,-np.inf); ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix],kind='stable')];rows.append(ix)\n out=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[np.asarray(rows).ravel()],'rank':np.tile(np.arange(1,101),len(rows))});out.to_parquet(P/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('baseline saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/prepare.py <<'PY'\nimport os,time\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data'); t=time.time()\nz=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; targets=z['uids']\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());np.savez(P/'ids.npz',uids=uids,items=items,targets=targets);print('users',len(uids),flush=True)\nui=pd.Index(uids);ii=pd.Index(items);shape=(len(uids),len(items))\nfor stage in ['train','val']:\n mats={k:sparse.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','short','strong','skip']}\n cutoff=24786800 if stage=='train' else 24786800+1800+7*86400\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];d=d[ok];age=(cutoff-d.timestamp.to_numpy(dtype=np.float64))/86400;pos=d.played_ratio_pct.values>=50;org=d.is_organic.values==1;strong=d.played_ratio_pct.values>=80\n vals={'count':pos,'organic':pos*org,'recent':pos*np.exp(-age/60),'short':pos*np.exp(-age/14),'strong':strong*(0.3+0.7*org)*np.exp(-age/90),'skip':(~pos)*np.exp(-age/60)}\n for k,v in vals.items():\n v=v.astype(np.float32);nz=v>0;mats[k]+=sparse.csr_matrix((v[nz],(r[nz],c[nz])),shape=shape)\n for k,x in mats.items():sparse.save_npz(P/f'{stage}_{k}.npz',x)\n d=pd.read_parquet(D/f'{stage}_likes.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=shape);sparse.save_npz(P/f'{stage}_likes.npz',x)\n print(stage,'done',time.time()-t,[(k,x.nnz) for k,x in mats.items()],flush=True)\n# Always keep an eligible final baseline, using observed train + val organic popularity.\npop=np.asarray((sparse.load_npz(P/'train_organic.npz')+4*sparse.load_npz(P/'val_organic.npz')).sum(axis=0)).ravel()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz');rows=[];scores=pop if stage=='test' else np.asarray(sparse.load_npz(P/'train_organic.npz').sum(axis=0)).ravel()\n for r,u in enumerate(z['uids']):\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(eligible,scores,-np.inf); ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix],kind='stable')];rows.append(ix)\n out=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[np.asarray(rows).ravel()],'rank':np.tile(np.arange(1,101),len(rows))});out.to_parquet(P/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('baseline saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 128, "nanos": 95446403 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:53.884Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-45bebad5-864b-4b14-8ca4-778b54492e3c", "process_id": "31865", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n args=p.read_bytes().split(b'\\0')\n if len(args)>1 and args[1]==b'/app/solution/prepare.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\np=Path('/app/solution/prepare.py');s=p.read_text().replace(\"rows=[];scores=pop\", \"packed=z['packed_eligible'];rows=[];scores=pop\").replace(\"z['packed_eligible'][r]\",\"packed[r]\");p.write_text(s)\ns=s[s.index('# Always keep'):];Path('/app/solution/baseline.py').write_text(\"import numpy as np,pandas as pd\\nfrom scipy import sparse\\nfrom pathlib import Path\\nP=Path('/app/solution');D=Path('/task/data');items=np.load(P/'ids.npz')['items']\\nimport time\\nt=time.time()\\n\"+s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/baseline.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n args=p.read_bytes().split(b'\\0')\n if len(args)>1 and args[1]==b'/app/solution/prepare.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\np=Path('/app/solution/prepare.py');s=p.read_text().replace(\"rows=[];scores=pop\", \"packed=z['packed_eligible'];rows=[];scores=pop\").replace(\"z['packed_eligible'][r]\",\"packed[r]\");p.write_text(s)\ns=s[s.index('# Always keep'):];Path('/app/solution/baseline.py').write_text(\"import numpy as np,pandas as pd\\nfrom scipy import sparse\\nfrom pathlib import Path\\nP=Path('/app/solution');D=Path('/task/data');items=np.load(P/'ids.npz')['items']\\nimport time\\nt=time.time()\\n\"+s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/baseline.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "baseline saved 2.288760185241699\n", "stderr": "", "aggregated_output": "baseline saved 2.288760185241699\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 89667213 }, "formatted_output": "baseline saved 2.288760185241699\n" } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-23T05:24:53.892Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:24:53.892Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:24:55.252Z", "text": "The collaborative model is fitting now. I’ve also prepared recency and organic-listening features so I can test whether emphasizing recent, self-directed listening improves discovery ranking.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:24:56.677Z", "callId": "call_gY70FlcA71c8oNrAf4O0cb8d", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:24:56.753Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"d08c67\",\"wall_time_seconds\":0.00000341,\"exit_code\":0,\"original_token_count\":207,\"output\":\" File \\\"/app/solution/model.py\\\", line 54, in ease\\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/model.py\\\", line 32, in run\\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\\\", line 52, in _sum\\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\\n\"}\n{\"current_time\":\"2026-09-23 05:24:56 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:24:56.715Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-39242e57-29e7-45fc-8e9c-189a33c20f22", "process_id": "38241", "command": [ "/bin/sh", "-lc", "tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail /app/solution/ease_binary.log; ls -lh /app/solution/*parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " File \"/app/solution/model.py\", line 54, in ease\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 32, in run\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": " File \"/app/solution/model.py\", line 54, in ease\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 32, in run\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3410 }, "formatted_output": " File \"/app/solution/model.py\", line 54, in ease\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 32, in run\n metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:24:56.752Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362a4012087d28aa67b5b2e6f9c54", "usage": { "input_tokens": 31583, "cached_input_tokens": 30848, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 16, "total_tokens": 31696 }, "turn_token_usage": { "input_tokens": 407076, "cached_input_tokens": 385024, "cache_write_input_tokens": 0, "output_tokens": 6837, "reasoning_output_tokens": 2143, "total_tokens": 413913 }, "thread_token_usage": { "input_tokens": 407076, "cached_input_tokens": 385024, "cache_write_input_tokens": 0, "output_tokens": 6837, "reasoning_output_tokens": 2143, "total_tokens": 413913 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-23T05:25:00.722Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:00.722Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:03.666Z", "callId": "call_coWi1ytBdA0SC8JsM0rUWYN6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\\nfrom model import *\\ne=Eval();g=e.gold[np.arange(128)[:,None],np.ones((128,10),dtype=int)];print(type(g),g.shape,np.asarray(g).shape)\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:25:04.810Z", "content": "Script completed\nWall time 1.1 seconds\nOutput:\n\n{\"chunk_id\":\"7107a1\",\"wall_time_seconds\":0.977041533,\"exit_code\":0,\"original_token_count\":13,\"output\":\" (128, 10) ()\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:03.753Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362a8f6e487d299d341e25118d667", "usage": { "input_tokens": 32007, "cached_input_tokens": 31360, "cache_write_input_tokens": 0, "output_tokens": 174, "reasoning_output_tokens": 67, "total_tokens": 32181 }, "turn_token_usage": { "input_tokens": 439083, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 7011, "reasoning_output_tokens": 2210, "total_tokens": 446094 }, "thread_token_usage": { "input_tokens": 439083, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 7011, "reasoning_output_tokens": 2210, "total_tokens": 446094 } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:04.806Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aa08f31d-c50f-4abe-b8ca-28939f1a37a1", "process_id": "28297", "command": [ "/bin/sh", "-lc", "cd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\ne=Eval();g=e.gold[np.arange(128)[:,None],np.ones((128,10),dtype=int)];print(type(g),g.shape,np.asarray(g).shape)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\ne=Eval();g=e.gold[np.arange(128)[:,None],np.ones((128,10),dtype=int)];print(type(g),g.shape,np.asarray(g).shape)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " (128, 10) ()\n", "stderr": "", "aggregated_output": " (128, 10) ()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 977033203 }, "formatted_output": " (128, 10) ()\n" } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-23T05:25:10.000Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:10.000Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:17.711Z", "callId": "call_VSp7lC3bHeaeqwI9nWtvsoXo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/model.py');s=p.read_text();s=s.replace(\\\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\\\",\\\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray()\\\");p.write_text(s)\\nPY\\ncd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\\nfrom model import *\\ne=Eval();e.run(np.load(P/'validation_ease_binary_300.0.npy',mmap_mode='r'),'validation_ease_binary_300.0')\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation hybrid 300 > /app/solution/ease_hybrid.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:25:20.038Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"efd491\",\"wall_time_seconds\":1.000778167,\"session_id\":11347,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"b04989\",\"wall_time_seconds\":1.001495321,\"session_id\":17224,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:17.760Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362b0f7c487d28cb4523abe4e9571", "usage": { "input_tokens": 32259, "cached_input_tokens": 31872, "cache_write_input_tokens": 0, "output_tokens": 275, "reasoning_output_tokens": 9, "total_tokens": 32534 }, "turn_token_usage": { "input_tokens": 471342, "cached_input_tokens": 448256, "cache_write_input_tokens": 0, "output_tokens": 7286, "reasoning_output_tokens": 2219, "total_tokens": 478628 }, "thread_token_usage": { "input_tokens": 471342, "cached_input_tokens": 448256, "cache_write_input_tokens": 0, "output_tokens": 7286, "reasoning_output_tokens": 2219, "total_tokens": 478628 } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:19.991Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50a9aebb-d191-41dd-9867-816e2d0859a6", "process_id": "11347", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/model.py');s=p.read_text();s=s.replace(\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\",\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray()\");p.write_text(s)\nPY\ncd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\ne=Eval();e.run(np.load(P/'validation_ease_binary_300.0.npy',mmap_mode='r'),'validation_ease_binary_300.0')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/model.py');s=p.read_text();s=s.replace(\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\",\"g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray()\");p.write_text(s)\nPY\ncd /app/solution && OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\ne=Eval();e.run(np.load(P/'validation_ease_binary_300.0.npy',mmap_mode='r'),'validation_ease_binary_300.0')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_ease_binary_300.0 NDCG 0.019635663927020566\n", "stderr": "", "aggregated_output": "validation_ease_binary_300.0 NDCG 0.019635663927020566\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 115595496 }, "formatted_output": "validation_ease_binary_300.0 NDCG 0.019635663927020566\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-23T05:25:24.194Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:24.194Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:25:26.867Z", "callId": "call_qelxfWY82tIj9XNfleQlMRq2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"head -5 /app/solution/ease_binary.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:11347,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:25:28.040Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"4815c6\",\"wall_time_seconds\":1.001236093,\"session_id\":73283,\"original_token_count\":43,\"output\":\"matrix binary (9238, 30000) 5564926 time 0.16855263710021973\\nkernel 7.266043663024902\\nchol 8.841795206069946\\nsolved 43.80845284461975\\nTraceback (most recent call last):\\n\"}\n{\"chunk_id\":\"f89608\",\"wall_time_seconds\":0.00000253,\"exit_code\":0,\"original_token_count\":14,\"output\":\"validation_ease_binary_300.0 NDCG 0.019635663927020566\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:25:26.944Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362c036e087d2bc1220911e2082eb", "usage": { "input_tokens": 32624, "cached_input_tokens": 32128, "cache_write_input_tokens": 0, "output_tokens": 109, "reasoning_output_tokens": 10, "total_tokens": 32733 }, "turn_token_usage": { "input_tokens": 503966, "cached_input_tokens": 480384, "cache_write_input_tokens": 0, "output_tokens": 7395, "reasoning_output_tokens": 2229, "total_tokens": 511361 }, "thread_token_usage": { "input_tokens": 503966, "cached_input_tokens": 480384, "cache_write_input_tokens": 0, "output_tokens": 7395, "reasoning_output_tokens": 2229, "total_tokens": 511361 } } }, { "kind": "context", "timestamp": "2026-09-23T05:25:30.593Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cb7a9a94-f5b8-4e49-bb1e-d0821f42d713", "process_id": "73283", "command": [ "/bin/sh", "-lc", "head -5 /app/solution/ease_binary.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "head -5 /app/solution/ease_binary.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 559087014 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-23T05:25:40.878Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:25:40.878Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:25:53.067Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3e8f3388-98de-4383-9c87-b721e071a1d3", "process_id": "17224", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation hybrid 300 > /app/solution/ease_hybrid.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation hybrid 300 > /app/solution/ease_hybrid.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 34, "nanos": 31713080 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:26:24.620Z", "callId": "call_oOljqOSXCuV4ikY7REwsBmIp", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/knn.py <<'PY'\\nfrom model import *\\ndef knn(stage,variant='binary',power=0.65,top=200):\\n t=time.time();x=load('count',stage);x.data[:]=1\\n if variant=='organic':\\n x.data[:]=.25;o=load('organic',stage);o.data[:]=1;x+=o\\n if variant=='likes':\\n l=load('likes',stage);l.data[:]=1;x+=3*l\\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)/500))@x\\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power\\n xt=x.T.tocsr();rows=[];cols=[];vals=[]\\n for st in range(0,M,300):\\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0\\n ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\\n if st%6000==0:print('knn block',st,time.time()-t,flush=True)\\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_knn_{variant}_{power}.npz',sim)\\n for profile in ['count','recent','short','strong']:\\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);l=load('likes',stage)[tr];l.data[:]=1;h+=2*l\\n out=np.lib.format.open_memmap(P/f'{stage}_knn_{variant}_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,len(targets),256):out[st:st+256]=(h[st:st+256]@sim).toarray()\\n out.flush();Eval(stage).run(out,f'{stage}_knn_{variant}_{power}_{profile}')\\n print('knn done',time.time()-t,flush=True)\\nif __name__=='__main__':knn(sys.argv[1],sys.argv[2],float(sys.argv[3]))\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation binary 0.65 > /app/solution/knn.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:73283,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/ease_hybrid.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:26:25.823Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"13ee1c\",\"wall_time_seconds\":1.001299254,\"session_id\":42950,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"4c9d81\",\"wall_time_seconds\":0.00000258,\"exit_code\":0,\"original_token_count\":32,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.010577,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}\n{\"chunk_id\":\"7eac62\",\"wall_time_seconds\":0.000004071,\"exit_code\":0,\"original_token_count\":48,\"output\":\"matrix hybrid (9238, 30000) 5618006 time 0.40281200408935547\\nkernel 7.248539686203003\\nchol 8.948179244995117\\nsolved 32.044275522232056\\nvalidation_ease_hybrid_300.0 NDCG 0.02671803366608401\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:26:24.687Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab362e9795087d281ae01a2ff7c839f", "usage": { "input_tokens": 33270, "cached_input_tokens": 32384, "cache_write_input_tokens": 0, "output_tokens": 718, "reasoning_output_tokens": 0, "total_tokens": 33988 }, "turn_token_usage": { "input_tokens": 537236, "cached_input_tokens": 512768, "cache_write_input_tokens": 0, "output_tokens": 8113, "reasoning_output_tokens": 2229, "total_tokens": 545349 }, "thread_token_usage": { "input_tokens": 537236, "cached_input_tokens": 512768, "cache_write_input_tokens": 0, "output_tokens": 8113, "reasoning_output_tokens": 2229, "total_tokens": 545349 } } }, { "kind": "context", "timestamp": "2026-09-23T05:26:25.818Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-babf395d-2888-4cc1-bf4b-1d49b490460a", "process_id": "55518", "command": [ "/bin/sh", "-lc", "tail /app/solution/ease_hybrid.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/ease_hybrid.log", "name": "ease_hybrid.log", "path": "/app/solution/ease_hybrid.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix hybrid (9238, 30000) 5618006 time 0.40281200408935547\nkernel 7.248539686203003\nchol 8.948179244995117\nsolved 32.044275522232056\nvalidation_ease_hybrid_300.0 NDCG 0.02671803366608401\n", "stderr": "", "aggregated_output": "matrix hybrid (9238, 30000) 5618006 time 0.40281200408935547\nkernel 7.248539686203003\nchol 8.948179244995117\nsolved 32.044275522232056\nvalidation_ease_hybrid_300.0 NDCG 0.02671803366608401\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4071 }, "formatted_output": "matrix hybrid (9238, 30000) 5618006 time 0.40281200408935547\nkernel 7.248539686203003\nchol 8.948179244995117\nsolved 32.044275522232056\nvalidation_ease_hybrid_300.0 NDCG 0.02671803366608401\n" } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-23T05:26:29.272Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:26:29.272Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:26:33.400Z", "callId": "call_8ATsCAfjLzWsqH87dN9omO6M", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py test hybrid 300 > /app/solution/final_hybrid.log 2>&1 && cp /app/solution/test_ease_hybrid_300.0.parquet /app/solution/submission.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation strong 300 > /app/solution/ease_strong.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:26:35.729Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"ac15db\",\"wall_time_seconds\":1.001307167,\"session_id\":75742,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"56af26\",\"wall_time_seconds\":1.001583572,\"session_id\":2692,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:26:33.477Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36301fa9887d29fa3cc0cc865d568", "usage": { "input_tokens": 34243, "cached_input_tokens": 33152, "cache_write_input_tokens": 0, "output_tokens": 199, "reasoning_output_tokens": 52, "total_tokens": 34442 }, "turn_token_usage": { "input_tokens": 571479, "cached_input_tokens": 545920, "cache_write_input_tokens": 0, "output_tokens": 8312, "reasoning_output_tokens": 2281, "total_tokens": 579791 }, "thread_token_usage": { "input_tokens": 571479, "cached_input_tokens": 545920, "cache_write_input_tokens": 0, "output_tokens": 8312, "reasoning_output_tokens": 2281, "total_tokens": 579791 } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-23T05:26:53.131Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:26:53.131Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:26:55.404Z", "text": "The recency-and-likes model reached **0.0267 NDCG@10**, compared with **0.0106** for popularity and **0.0196** for the basic collaborative model. I’m refitting it for the hidden-test cutoff now, while testing item-neighbor and organic-focused variants.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:05.294Z", "callId": "call_IBMd1LsOEgoDYzROTGQ3Gwae", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/state.py <<'PY'\\nfrom model import *\\nfor stage in ['validation','test']:\\n for action,undo in [('likes','unlikes'),('dislikes','undislikes')]:\\n a=[]\\n for period in (['train'] if stage=='validation' else ['train','val']):\\n for name,value in [(action,1),(undo,0)]:\\n d=pd.read_parquet(D/f'{period}_{name}.parquet');d['v']=value;a.append(d)\\n d=pd.concat(a).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');d=d[d.v==1];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=(N,M));sparse.save_npz(P/f'{stage}_active_{action}.npz',x)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/state.py\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:27:06.738Z", "content": "Script completed\nWall time 1.4 seconds\nOutput:\n\n{\"chunk_id\":\"74c650\",\"wall_time_seconds\":1.002747429,\"session_id\":46355,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"eb43ee\",\"wall_time_seconds\":0.00000566,\"exit_code\":0,\"original_token_count\":97,\"output\":\"knn block 0 1.4161667823791504\\nknn block 6000 11.359322547912598\\nknn block 12000 17.574801445007324\\nknn block 18000 29.642476558685303\\nknn block 24000 40.25537300109863\\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\\nkernel 7.82088565826416\\nchol 10.436923027038574\\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\\nkernel 8.577764749526978\\nchol 13.495468378067017\\n\"}\n{\"current_time\":\"2026-09-23 05:27:06 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:05.406Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3630bed0087d285a4e35dc5683025", "usage": { "input_tokens": 34533, "cached_input_tokens": 34048, "cache_write_input_tokens": 0, "output_tokens": 924, "reasoning_output_tokens": 516, "total_tokens": 35457 }, "turn_token_usage": { "input_tokens": 606012, "cached_input_tokens": 579968, "cache_write_input_tokens": 0, "output_tokens": 9236, "reasoning_output_tokens": 2797, "total_tokens": 615248 }, "thread_token_usage": { "input_tokens": 606012, "cached_input_tokens": 579968, "cache_write_input_tokens": 0, "output_tokens": 9236, "reasoning_output_tokens": 2797, "total_tokens": 615248 } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:06.634Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1c9267ae-79c3-4526-97fe-0c59219158ac", "process_id": "60168", "command": [ "/bin/sh", "-lc", "tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/knn.log", "name": "knn.log", "path": "/app/solution/knn.log" }, { "type": "read", "cmd": "tail /app/solution/ease_strong.log", "name": "ease_strong.log", "path": "/app/solution/ease_strong.log" }, { "type": "read", "cmd": "tail /app/solution/final_hybrid.log", "name": "final_hybrid.log", "path": "/app/solution/final_hybrid.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n", "stderr": "", "aggregated_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5660 }, "formatted_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:11.798Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fe624e55-1004-405f-9dcb-fd1945aa126d", "process_id": "46355", "command": [ "/bin/sh", "-lc", "cat > /app/solution/state.py <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n for action,undo in [('likes','unlikes'),('dislikes','undislikes')]:\n a=[]\n for period in (['train'] if stage=='validation' else ['train','val']):\n for name,value in [(action,1),(undo,0)]:\n d=pd.read_parquet(D/f'{period}_{name}.parquet');d['v']=value;a.append(d)\n d=pd.concat(a).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');d=d[d.v==1];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=(N,M));sparse.save_npz(P/f'{stage}_active_{action}.npz',x)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/state.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/state.py <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n for action,undo in [('likes','unlikes'),('dislikes','undislikes')]:\n a=[]\n for period in (['train'] if stage=='validation' else ['train','val']):\n for name,value in [(action,1),(undo,0)]:\n d=pd.read_parquet(D/f'{period}_{name}.parquet');d['v']=value;a.append(d)\n d=pd.concat(a).sort_values('timestamp').drop_duplicates(['uid','item_id'],keep='last');d=d[d.v==1];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=(N,M));sparse.save_npz(P/f'{stage}_active_{action}.npz',x)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/state.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 6, "nanos": 300984272 }, "formatted_output": "" } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-23T05:27:25.832Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:25.832Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:26.494Z", "callId": "call_mtmMkm52JVzGbImqOlz0WKKw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:26.609Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"d0517e\",\"wall_time_seconds\":0.0000041,\"exit_code\":0,\"original_token_count\":97,\"output\":\"knn block 0 1.4161667823791504\\nknn block 6000 11.359322547912598\\nknn block 12000 17.574801445007324\\nknn block 18000 29.642476558685303\\nknn block 24000 40.25537300109863\\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\\nkernel 7.82088565826416\\nchol 10.436923027038574\\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\\nkernel 8.577764749526978\\nchol 13.495468378067017\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:26.521Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-adbe3e29-53b4-4544-a426-4cc5bad96c51", "process_id": "92267", "command": [ "/bin/sh", "-lc", "tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/knn.log", "name": "knn.log", "path": "/app/solution/knn.log" }, { "type": "read", "cmd": "tail /app/solution/ease_strong.log", "name": "ease_strong.log", "path": "/app/solution/ease_strong.log" }, { "type": "read", "cmd": "tail /app/solution/final_hybrid.log", "name": "final_hybrid.log", "path": "/app/solution/final_hybrid.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n", "stderr": "", "aggregated_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4100 }, "formatted_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:26.598Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3632af90087d2bdd16ed7c6ed1011", "usage": { "input_tokens": 35729, "cached_input_tokens": 34304, "cache_write_input_tokens": 0, "output_tokens": 517, "reasoning_output_tokens": 458, "total_tokens": 36246 }, "turn_token_usage": { "input_tokens": 641741, "cached_input_tokens": 614272, "cache_write_input_tokens": 0, "output_tokens": 9753, "reasoning_output_tokens": 3255, "total_tokens": 651494 }, "thread_token_usage": { "input_tokens": 641741, "cached_input_tokens": 614272, "cache_write_input_tokens": 0, "output_tokens": 9753, "reasoning_output_tokens": 3255, "total_tokens": 651494 } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-23T05:27:29.717Z", "blocks": 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agent agent 968M Sep 23 05:27 /app/solution/test_ease_hybrid_300.0.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:24 /app/solution/validation_ease_binary_300.0.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:25 /app/solution/validation_ease_hybrid_300.0.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_ease_strong_300.0.npy\\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_knn_binary_0.65_count.npy\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:32.042Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3633ee08c87d2a96d75a7f4dd0171", "usage": { "input_tokens": 36467, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 85, 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/app/solution/*npy" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "6341005312\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 580766957\nuser_usec 566404945\nsystem_usec 14362011\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 3183\nnr_throttled 914\nthrottled_usec 204094702\nnr_bursts 0\nburst_usec 0\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/test_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:24 /app/solution/validation_ease_binary_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:25 /app/solution/validation_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_ease_strong_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_knn_binary_0.65_count.npy\n", "stderr": "", "aggregated_output": "6341005312\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 580766957\nuser_usec 566404945\nsystem_usec 14362011\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 3183\nnr_throttled 914\nthrottled_usec 204094702\nnr_bursts 0\nburst_usec 0\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/test_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:24 /app/solution/validation_ease_binary_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:25 /app/solution/validation_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_ease_strong_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_knn_binary_0.65_count.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2689 }, "formatted_output": "6341005312\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 580766957\nuser_usec 566404945\nsystem_usec 14362011\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 3183\nnr_throttled 914\nthrottled_usec 204094702\nnr_bursts 0\nburst_usec 0\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/test_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:24 /app/solution/validation_ease_binary_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:25 /app/solution/validation_ease_hybrid_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_ease_strong_300.0.npy\n-rw-r--r-- 1 agent agent 968M Sep 23 05:27 /app/solution/validation_knn_binary_0.65_count.npy\n" } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-23T05:27:37.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:37.621Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:27:40.944Z", "callId": "call_aFkfOu9V2k1ZZfR0FFFPdSGj", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/sweep.py <<'PY'\\nfrom model import *\\nfor variant,reg in [('hybrid',100),('hybrid',1000),('recent',300),('liked',300)]:ease('validation',variant,reg)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/sweep.py > /app/solution/sweep.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:27:42.205Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"9ac652\",\"wall_time_seconds\":1.001417624,\"session_id\":98616,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:27:41.102Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36344848087d2a63044be64a91079", "usage": { "input_tokens": 36898, "cached_input_tokens": 32768, "cache_write_input_tokens": 0, "output_tokens": 218, "reasoning_output_tokens": 96, "total_tokens": 37116 }, "turn_token_usage": { "input_tokens": 715106, "cached_input_tokens": 682624, "cache_write_input_tokens": 0, "output_tokens": 10056, "reasoning_output_tokens": 3368, "total_tokens": 725162 }, "thread_token_usage": { "input_tokens": 715106, "cached_input_tokens": 682624, "cache_write_input_tokens": 0, "output_tokens": 10056, "reasoning_output_tokens": 3368, "total_tokens": 725162 } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-23T05:27:44.713Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:27:44.713Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:27:51.886Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b2e3ee3b-5b33-4ffd-9b51-8fba8c75c4fd", "process_id": "2692", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation strong 300 > /app/solution/ease_strong.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py validation strong 300 > /app/solution/ease_strong.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 77, "nanos": 160688829 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:27:53.973Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50bb72f9-c99b-4a91-8428-c35483523cda", "process_id": "75742", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py test hybrid 300 > /app/solution/final_hybrid.log 2>&1 && cp /app/solution/test_ease_hybrid_300.0.parquet /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/model.py test hybrid 300 > /app/solution/final_hybrid.log 2>&1 && cp /app/solution/test_ease_hybrid_300.0.parquet /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 80, "nanos": 408034540 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:28:05.042Z", "callId": "call_AYBnZkY7CmmDeqItYt0ACKBz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/asymmetric.py <<'PY'\\nfrom model import *\\ndef fit(stage,reg=300,like=2,mode='organic'):\\n t=time.time();x=load('count',stage);x.data[:]=.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=l\\n y=load('organic',stage);y.data=np.log1p(y.data);y+=like*l\\n if mode=='binary':y.data=np.minimum(y.data,1)\\n if mode=='recent':\\n o=load('organic',stage);o.data[:]=1;y=load('recent',stage).multiply(o);y.data=np.log1p(y.data);y+=like*l\\n if mode=='multi':\\n # Independent explicit-preference and organic-history feature channels.\\n a=load('organic',stage);a.data=np.minimum(a.data,1);x=sparse.hstack([x,2*l,a],format='csr')\\n if mode=='norm':\\n w=1/np.maximum(np.asarray(x.power(2).sum(1)).ravel()/500,0.1)**.25;x=sparse.diags(w)@x;y=sparse.diags(w)@y\\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\\n name=f'{stage}_asym_{mode}_{reg}_{like}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,M,1000):\\n en=min(st+1000,M);xx=x[:,st:en];yy=y[:,st:en];a=linalg.cho_solve(cf,xx.toarray(order='F'),overwrite_b=True,check_finite=False);b=linalg.cho_solve(cf,yy.toarray(order='F'),overwrite_b=True,check_finite=False)\\n den=1-np.asarray(xx.multiply(a).sum(0)).ravel();q=np.asarray(xx.multiply(b).sum(0)).ravel();out[:,st:en]=-reg*(b[tr]+a[tr]*(q/np.maximum(den,1e-5)))\\n out.flush();print(name,time.time()-t,flush=True);Eval(stage).run(out,name)\\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),sys.argv[4])\\nPY\\nls -lh /app/solution/submission.parquet; tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log; tail /app/solution/sweep.log\",\"max_output_tokens\":2500}));\n" }, "result": { "timestamp": "2026-09-23T05:28:05.135Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"36e499\",\"wall_time_seconds\":0.00000588,\"exit_code\":0,\"original_token_count\":192,\"output\":\"-rw-r--r-- 1 agent agent 1.8M Sep 23 05:27 /app/solution/submission.parquet\\nknn block 0 1.4161667823791504\\nknn block 6000 11.359322547912598\\nknn block 12000 17.574801445007324\\nknn block 18000 29.642476558685303\\nknn block 24000 40.25537300109863\\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\\nkernel 7.82088565826416\\nchol 10.436923027038574\\nsolved 74.98889970779419\\nvalidation_ease_strong_300.0 NDCG 0.03109444468385386\\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\\nkernel 8.577764749526978\\nchol 13.495468378067017\\nsolved 78.38493657112122\\ntest_ease_hybrid_300.0 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\\nkernel 12.370550394058228\\nchol 13.734721422195435\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:05.123Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-813bcb43-ff28-463a-b695-3fb04ab0e9c1", "process_id": "62741", "command": [ "/bin/sh", "-lc", "cat > /app/solution/asymmetric.py <<'PY'\nfrom model import *\ndef fit(stage,reg=300,like=2,mode='organic'):\n t=time.time();x=load('count',stage);x.data[:]=.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=l\n y=load('organic',stage);y.data=np.log1p(y.data);y+=like*l\n if mode=='binary':y.data=np.minimum(y.data,1)\n if mode=='recent':\n o=load('organic',stage);o.data[:]=1;y=load('recent',stage).multiply(o);y.data=np.log1p(y.data);y+=like*l\n if mode=='multi':\n # Independent explicit-preference and organic-history feature channels.\n a=load('organic',stage);a.data=np.minimum(a.data,1);x=sparse.hstack([x,2*l,a],format='csr')\n if mode=='norm':\n w=1/np.maximum(np.asarray(x.power(2).sum(1)).ravel()/500,0.1)**.25;x=sparse.diags(w)@x;y=sparse.diags(w)@y\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_asym_{mode}_{reg}_{like}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);xx=x[:,st:en];yy=y[:,st:en];a=linalg.cho_solve(cf,xx.toarray(order='F'),overwrite_b=True,check_finite=False);b=linalg.cho_solve(cf,yy.toarray(order='F'),overwrite_b=True,check_finite=False)\n den=1-np.asarray(xx.multiply(a).sum(0)).ravel();q=np.asarray(xx.multiply(b).sum(0)).ravel();out[:,st:en]=-reg*(b[tr]+a[tr]*(q/np.maximum(den,1e-5)))\n out.flush();print(name,time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),sys.argv[4])\nPY\nls -lh /app/solution/submission.parquet; tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log; tail /app/solution/sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/asymmetric.py <<'PY'\nfrom model import *\ndef fit(stage,reg=300,like=2,mode='organic'):\n t=time.time();x=load('count',stage);x.data[:]=.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=l\n y=load('organic',stage);y.data=np.log1p(y.data);y+=like*l\n if mode=='binary':y.data=np.minimum(y.data,1)\n if mode=='recent':\n o=load('organic',stage);o.data[:]=1;y=load('recent',stage).multiply(o);y.data=np.log1p(y.data);y+=like*l\n if mode=='multi':\n # Independent explicit-preference and organic-history feature channels.\n a=load('organic',stage);a.data=np.minimum(a.data,1);x=sparse.hstack([x,2*l,a],format='csr')\n if mode=='norm':\n w=1/np.maximum(np.asarray(x.power(2).sum(1)).ravel()/500,0.1)**.25;x=sparse.diags(w)@x;y=sparse.diags(w)@y\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_asym_{mode}_{reg}_{like}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);xx=x[:,st:en];yy=y[:,st:en];a=linalg.cho_solve(cf,xx.toarray(order='F'),overwrite_b=True,check_finite=False);b=linalg.cho_solve(cf,yy.toarray(order='F'),overwrite_b=True,check_finite=False)\n den=1-np.asarray(xx.multiply(a).sum(0)).ravel();q=np.asarray(xx.multiply(b).sum(0)).ravel();out[:,st:en]=-reg*(b[tr]+a[tr]*(q/np.maximum(den,1e-5)))\n out.flush();print(name,time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),sys.argv[4])\nPY\nls -lh /app/solution/submission.parquet; tail /app/solution/knn.log; tail /app/solution/ease_strong.log; tail /app/solution/final_hybrid.log; tail /app/solution/sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 1.8M Sep 23 05:27 /app/solution/submission.parquet\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nsolved 74.98889970779419\nvalidation_ease_strong_300.0 NDCG 0.03109444468385386\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\nsolved 78.38493657112122\ntest_ease_hybrid_300.0 NDCG 0.0\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 1.8M Sep 23 05:27 /app/solution/submission.parquet\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nsolved 74.98889970779419\nvalidation_ease_strong_300.0 NDCG 0.03109444468385386\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\nsolved 78.38493657112122\ntest_ease_hybrid_300.0 NDCG 0.0\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5880 }, "formatted_output": "-rw-r--r-- 1 agent agent 1.8M Sep 23 05:27 /app/solution/submission.parquet\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nmatrix strong (9238, 30000) 5337972 time 0.4207315444946289\nkernel 7.82088565826416\nchol 10.436923027038574\nsolved 74.98889970779419\nvalidation_ease_strong_300.0 NDCG 0.03109444468385386\nmatrix hybrid (9238, 30000) 5778549 time 0.48677706718444824\nkernel 8.577764749526978\nchol 13.495468378067017\nsolved 78.38493657112122\ntest_ease_hybrid_300.0 NDCG 0.0\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:05.131Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3634e7c0887d2be24268e0c202a71", "usage": { "input_tokens": 37173, "cached_input_tokens": 36736, "cache_write_input_tokens": 0, "output_tokens": 693, "reasoning_output_tokens": 24, "total_tokens": 37866 }, "turn_token_usage": { "input_tokens": 752279, "cached_input_tokens": 719360, "cache_write_input_tokens": 0, "output_tokens": 10749, "reasoning_output_tokens": 3392, "total_tokens": 763028 }, "thread_token_usage": { "input_tokens": 752279, "cached_input_tokens": 719360, "cache_write_input_tokens": 0, "output_tokens": 10749, "reasoning_output_tokens": 3392, "total_tokens": 763028 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-23T05:28:24.020Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:24.020Z", "text": "" } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-23T05:28:28.986Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:28.986Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:28:30.673Z", "callId": "call_wuhJ20Obm85U29QyPkCOtjmw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/asymmetric.py validation 300 2 organic > /app/solution/asym.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/knn.log; tail /app/solution/sweep.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:28:32.484Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\n{\"chunk_id\":\"a7eb99\",\"wall_time_seconds\":1.001180545,\"session_id\":72251,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"c0f10b\",\"wall_time_seconds\":0.00000379,\"exit_code\":0,\"original_token_count\":114,\"output\":\"knn block 0 1.4161667823791504\\nknn block 6000 11.359322547912598\\nknn block 12000 17.574801445007324\\nknn block 18000 29.642476558685303\\nknn block 24000 40.25537300109863\\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\\nkernel 12.370550394058228\\nchol 13.734721422195435\\n\"}\n{\"current_time\":\"2026-09-23 05:28:32 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:30.941Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36365673c87d2968fbcfe521c9131", "usage": { "input_tokens": 38246, "cached_input_tokens": 36992, "cache_write_input_tokens": 0, "output_tokens": 789, "reasoning_output_tokens": 678, "total_tokens": 39035 }, "turn_token_usage": { "input_tokens": 790525, "cached_input_tokens": 756352, "cache_write_input_tokens": 0, "output_tokens": 11538, "reasoning_output_tokens": 4070, "total_tokens": 802063 }, "thread_token_usage": { "input_tokens": 790525, "cached_input_tokens": 756352, "cache_write_input_tokens": 0, "output_tokens": 11538, "reasoning_output_tokens": 4070, "total_tokens": 802063 } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:32.303Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3758c6c6-910b-4393-a482-b67c22d59fe0", "process_id": "48883", "command": [ "/bin/sh", "-lc", "tail /app/solution/knn.log; tail /app/solution/sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/knn.log", "name": "knn.log", "path": "/app/solution/knn.log" }, { "type": "read", "cmd": "tail /app/solution/sweep.log", "name": "sweep.log", "path": "/app/solution/sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n", "stderr": "", "aggregated_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3790 }, "formatted_output": "knn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nmatrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-23T05:28:37.404Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:37.404Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:28:37.943Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-85644b0f-29bf-49c5-8d05-c93c092adcdb", "process_id": "42950", "command": [ "/bin/sh", "-lc", "cat > /app/solution/knn.py <<'PY'\nfrom model import *\ndef knn(stage,variant='binary',power=0.65,top=200):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='organic':\n x.data[:]=.25;o=load('organic',stage);o.data[:]=1;x+=o\n if variant=='likes':\n l=load('likes',stage);l.data[:]=1;x+=3*l\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)/500))@x\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power\n xt=x.T.tocsr();rows=[];cols=[];vals=[]\n for st in range(0,M,300):\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0\n ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\n if st%6000==0:print('knn block',st,time.time()-t,flush=True)\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_knn_{variant}_{power}.npz',sim)\n for profile in ['count','recent','short','strong']:\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);l=load('likes',stage)[tr];l.data[:]=1;h+=2*l\n out=np.lib.format.open_memmap(P/f'{stage}_knn_{variant}_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,len(targets),256):out[st:st+256]=(h[st:st+256]@sim).toarray()\n out.flush();Eval(stage).run(out,f'{stage}_knn_{variant}_{power}_{profile}')\n print('knn done',time.time()-t,flush=True)\nif __name__=='__main__':knn(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation binary 0.65 > /app/solution/knn.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/knn.py <<'PY'\nfrom model import *\ndef knn(stage,variant='binary',power=0.65,top=200):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='organic':\n x.data[:]=.25;o=load('organic',stage);o.data[:]=1;x+=o\n if variant=='likes':\n l=load('likes',stage);l.data[:]=1;x+=3*l\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)/500))@x\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power\n xt=x.T.tocsr();rows=[];cols=[];vals=[]\n for st in range(0,M,300):\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0\n ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\n if st%6000==0:print('knn block',st,time.time()-t,flush=True)\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_knn_{variant}_{power}.npz',sim)\n for profile in ['count','recent','short','strong']:\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);l=load('likes',stage)[tr];l.data[:]=1;h+=2*l\n out=np.lib.format.open_memmap(P/f'{stage}_knn_{variant}_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,len(targets),256):out[st:st+256]=(h[st:st+256]@sim).toarray()\n out.flush();Eval(stage).run(out,f'{stage}_knn_{variant}_{power}_{profile}')\n print('knn done',time.time()-t,flush=True)\nif __name__=='__main__':knn(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation binary 0.65 > /app/solution/knn.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 133, "nanos": 160059308 }, "formatted_output": "" } } }, { "kind": "message", "timestamp": "2026-09-23T05:28:39.095Z", "text": "The organic-focused model improved validation to **0.0311 NDCG@10**. The item-neighbor models tested so far are weaker. I’m checking regularization, model blends, and item popularity trends next.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:28:54.432Z", "callId": "call_UAOXedMRvsL9QfYZpqHTRIw7", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ease_strong_300.0.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/strong_sweep.py <<'PY'\\nfrom model import *\\ndef fit(stage,reg=300,likes=1,power=0,organic=1):\\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data)\\n l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=likes*l\\n if power:\\n deg=np.asarray(x.power(2).sum(1)).ravel();x=sparse.diags(np.maximum(deg/300,0.05)**(-power))@x\\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\\n name=f'{stage}_strong_{reg}_{likes}_{power}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,M,1000):\\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\\nif __name__=='__main__':\\n if len(sys.argv)>1:fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),float(sys.argv[4]))\\n else:\\n for reg,likes,power in [(100,0,0),(1000,0,0),(300,1,0),(300,2,.25),(100,1,.25)]:fit('validation',reg,likes,power)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:28:55.664Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"6c64d6\",\"wall_time_seconds\":1.001339335,\"session_id\":36607,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"6c2734\",\"wall_time_seconds\":0.00000422,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:28:54.526Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36380cb5087d2b34b6f8ed0df38bc", "usage": { "input_tokens": 39331, "cached_input_tokens": 38016, "cache_write_input_tokens": 0, "output_tokens": 610, "reasoning_output_tokens": 38, "total_tokens": 39941 }, "turn_token_usage": { "input_tokens": 829856, "cached_input_tokens": 794368, "cache_write_input_tokens": 0, "output_tokens": 12148, "reasoning_output_tokens": 4108, "total_tokens": 842004 }, "thread_token_usage": { "input_tokens": 829856, "cached_input_tokens": 794368, "cache_write_input_tokens": 0, "output_tokens": 12148, "reasoning_output_tokens": 4108, "total_tokens": 842004 } } }, { "kind": "context", "timestamp": "2026-09-23T05:28:55.647Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cde9d003-05c8-4b9a-847c-56720e7aff6e", "process_id": "9700", "command": [ "/bin/sh", "-lc", "cat > /app/solution/strong_sweep.py <<'PY'\nfrom model import *\ndef fit(stage,reg=300,likes=1,power=0,organic=1):\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data)\n l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=likes*l\n if power:\n deg=np.asarray(x.power(2).sum(1)).ravel();x=sparse.diags(np.maximum(deg/300,0.05)**(-power))@x\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_strong_{reg}_{likes}_{power}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':\n if len(sys.argv)>1:fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),float(sys.argv[4]))\n else:\n for reg,likes,power in [(100,0,0),(1000,0,0),(300,1,0),(300,2,.25),(100,1,.25)]:fit('validation',reg,likes,power)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/strong_sweep.py <<'PY'\nfrom model import *\ndef fit(stage,reg=300,likes=1,power=0,organic=1):\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data)\n l=sparse.load_npz(P/f'{stage}_active_likes.npz');x+=likes*l\n if power:\n deg=np.asarray(x.power(2).sum(1)).ravel();x=sparse.diags(np.maximum(deg/300,0.05)**(-power))@x\n k=(x@x.T).toarray(order='F');k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_strong_{reg}_{likes}_{power}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':\n if len(sys.argv)>1:fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]),float(sys.argv[4]))\n else:\n for reg,likes,power in [(100,0,0),(1000,0,0),(300,1,0),(300,2,.25),(100,1,.25)]:fit('validation',reg,likes,power)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4220 }, "formatted_output": "" } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-23T05:28:58.639Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:28:58.639Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:29:00.037Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7677c0d4-e1f3-44fc-a7a2-ca625531fa7a", "process_id": "36607", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ease_strong_300.0.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ease_strong_300.0.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_ease_strong_300.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031094,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_ease_strong_300.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031094,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 431131780 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_ease_strong_300.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031094,\n \"quality_users\": 4263\n }\n}\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:29:01.211Z", "callId": "call_YgHpk5aTpsLje53qSwW8a1go", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/sweep.log; tail /app/solution/asym.log; tail /app/solution/knn.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1200}));\ntext(await tools.write_stdin({session_id:36607,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:29:01.303Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"01e258\",\"wall_time_seconds\":0.00000494,\"exit_code\":0,\"original_token_count\":185,\"output\":\"matrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\\nkernel 12.370550394058228\\nchol 13.734721422195435\\nsolved 53.80422306060791\\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\\nkernel 7.902369737625122\\nchol 9.242098569869995\\nknn block 0 1.4161667823791504\\nknn block 6000 11.359322547912598\\nknn block 12000 17.574801445007324\\nknn block 18000 29.642476558685303\\nknn block 24000 40.25537300109863\\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\\nvalidation_knn_binary_0.65_strong NDCG 0.010131016277298626\\nknn done 132.32966923713684\\n8293363712\\n\"}\n{\"chunk_id\":\"5922b4\",\"wall_time_seconds\":0.00000204,\"exit_code\":0,\"original_token_count\":37,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation_ease_strong_300.0.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.031094,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:01.261Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-61bdced6-b76a-4542-bd77-cdb1993be6f3", "process_id": "98758", "command": [ "/bin/sh", "-lc", "tail /app/solution/sweep.log; tail /app/solution/asym.log; tail /app/solution/knn.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/sweep.log", "name": "sweep.log", "path": "/app/solution/sweep.log" }, { "type": "read", "cmd": "tail /app/solution/asym.log", "name": "asym.log", "path": "/app/solution/asym.log" }, { "type": "read", "cmd": "tail /app/solution/knn.log", "name": "knn.log", "path": "/app/solution/knn.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\nsolved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nvalidation_knn_binary_0.65_strong NDCG 0.010131016277298626\nknn done 132.32966923713684\n8293363712\n", "stderr": "", "aggregated_output": "matrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\nsolved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nvalidation_knn_binary_0.65_strong NDCG 0.010131016277298626\nknn done 132.32966923713684\n8293363712\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4940 }, "formatted_output": "matrix hybrid (9238, 30000) 5618006 time 1.1980400085449219\nkernel 12.370550394058228\nchol 13.734721422195435\nsolved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nknn block 0 1.4161667823791504\nknn block 6000 11.359322547912598\nknn block 12000 17.574801445007324\nknn block 18000 29.642476558685303\nknn block 24000 40.25537300109863\nvalidation_knn_binary_0.65_count NDCG 0.00824536436034393\nvalidation_knn_binary_0.65_recent NDCG 0.00918631073321531\nvalidation_knn_binary_0.65_short NDCG 0.011918547503591526\nvalidation_knn_binary_0.65_strong NDCG 0.010131016277298626\nknn done 132.32966923713684\n8293363712\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:01.301Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36397dcf087d28e81423a270ac097", "usage": { "input_tokens": 40034, "cached_input_tokens": 36352, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 18, "total_tokens": 40147 }, "turn_token_usage": { "input_tokens": 869890, "cached_input_tokens": 830720, "cache_write_input_tokens": 0, "output_tokens": 12261, "reasoning_output_tokens": 4126, "total_tokens": 882151 }, "thread_token_usage": { "input_tokens": 869890, "cached_input_tokens": 830720, "cache_write_input_tokens": 0, "output_tokens": 12261, "reasoning_output_tokens": 4126, "total_tokens": 882151 } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-23T05:29:19.074Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:29:19.074Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:29:39.282Z", "callId": "call_Ri4VZQICWtKtyevP8ade94ad", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/blend.py <<'PY'\\nfrom model import *\\ndef tune(names):\\n e=Eval();a=[np.load(P/(n+'.npy'),mmap_mode='r') for n in names]; rr=[];ff=[];gg=[]\\n for st in range(0,len(targets),64):\\n en=min(st+64,len(targets));mask=e.mask(st,en);cand=[];full=[]\\n for s in a:\\n b=np.asarray(s[st:en]).copy();b[~mask]=-np.inf;ix=np.argpartition(b,-100,axis=1)[:,-100:];cand.append(ix);full.append(b)\\n pool=np.concatenate(cand,axis=1)\\n # duplicate candidates get -infinity in all features after their first occurrence\\n for row in range(len(pool)):\\n _,ix=np.unique(pool[row],return_index=True);du=np.ones(pool.shape[1],bool);du[ix]=False;pool[row,du]=-1\\n f=[]\\n for b in full:\\n scale=np.partition(b,-10,axis=1)[:,-10:].mean(1);v=np.take_along_axis(b,np.maximum(pool,0),axis=1)/np.maximum(scale[:,None],1e-6);v[pool<0]=-1e6;f.append(v)\\n rr.append(pool);ff.append(np.stack(f,axis=2));gg.append(e.gold[np.arange(st,en)[:,None],np.maximum(pool,0)].toarray())\\n pool=np.concatenate(rr);feat=np.concatenate(ff);gold=np.concatenate(gg);gold[pool<0]=0\\n def score(w):\\n s=feat@w;ix=np.argsort(-s,axis=1)[:,:10];dcg=(np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg;return dcg[e.active].mean()\\n best=(0,None)\\n rng=np.random.default_rng(42)\\n weights=[np.eye(len(a))[i] for i in range(len(a))]\\n weights += [rng.dirichlet(np.ones(len(a))*.35) for _ in range(150)]\\n for w in weights:\\n scoreval=score(w)\\n if scoreval>best[0]:best=(scoreval,w);print('best',scoreval,dict(zip(names,w)),flush=True)\\n np.savez(P/'blend_tuning.npz',pool=pool,feat=feat,gold=gold,names=np.array(names),weights=best[1]);return best\\nif __name__=='__main__':tune(sys.argv[1:])\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py > /app/solution/strong_sweep.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/asym.log; tail /app/solution/sweep.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:29:40.507Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"2c0227\",\"wall_time_seconds\":1.00149818,\"session_id\":27589,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"bb18ea\",\"wall_time_seconds\":0.00000381,\"exit_code\":0,\"original_token_count\":94,\"output\":\"solved 53.80422306060791\\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\\nkernel 7.902369737625122\\nchol 9.242098569869995\\nsolved 44.20782208442688\\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\\nkernel 7.929508447647095\\nchol 9.285109043121338\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:39.400Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3639d812087d2ac4d02d442023f70", "usage": { "input_tokens": 40597, "cached_input_tokens": 39168, "cache_write_input_tokens": 0, "output_tokens": 1200, "reasoning_output_tokens": 516, "total_tokens": 41797 }, "turn_token_usage": { "input_tokens": 910487, "cached_input_tokens": 869888, "cache_write_input_tokens": 0, "output_tokens": 13461, "reasoning_output_tokens": 4642, "total_tokens": 923948 }, "thread_token_usage": { "input_tokens": 910487, "cached_input_tokens": 869888, "cache_write_input_tokens": 0, "output_tokens": 13461, "reasoning_output_tokens": 4642, "total_tokens": 923948 } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:40.490Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be1290ab-e244-44d3-89c9-6ba4ad82599b", "process_id": "24283", "command": [ "/bin/sh", "-lc", "tail /app/solution/asym.log; tail /app/solution/sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/asym.log", "name": "asym.log", "path": "/app/solution/asym.log" }, { "type": "read", "cmd": "tail /app/solution/sweep.log", "name": "sweep.log", "path": "/app/solution/sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "solved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\n", "stderr": "", "aggregated_output": "solved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3810 }, "formatted_output": "solved 53.80422306060791\nvalidation_ease_hybrid_100 NDCG 0.02496293123813829\nmatrix hybrid (9238, 30000) 5618006 time 0.3484961986541748\nkernel 7.902369737625122\nchol 9.242098569869995\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\n" } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-23T05:29:42.649Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:29:42.649Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:29:44.345Z", "callId": "call_ZeuST7ZjETbsZtp63lu1Z7h5", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/asym.log; tail /app/solution/strong_sweep.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:29:44.580Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"current_time\":\"2026-09-23 05:29:44 UTC\"}\n{\"chunk_id\":\"d095bd\",\"wall_time_seconds\":0.0000048,\"exit_code\":0,\"original_token_count\":19,\"output\":\"low 0\\nhigh 0\\nmax 1847\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:29:44.430Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab363c4acfc87d2beea3922ea50b58e", "usage": { "input_tokens": 42040, "cached_input_tokens": 40448, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 13, "total_tokens": 42119 }, "turn_token_usage": { "input_tokens": 952527, "cached_input_tokens": 910336, "cache_write_input_tokens": 0, "output_tokens": 13540, "reasoning_output_tokens": 4655, "total_tokens": 966067 }, "thread_token_usage": { "input_tokens": 952527, "cached_input_tokens": 910336, "cache_write_input_tokens": 0, "output_tokens": 13540, "reasoning_output_tokens": 4655, "total_tokens": 966067 } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:44.562Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-89142b10-46ed-41f0-a99c-1c523964bb99", "process_id": "67153", "command": [ "/bin/sh", "-lc", "tail /app/solution/asym.log; tail /app/solution/strong_sweep.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/asym.log", "name": "asym.log", "path": "/app/solution/asym.log" }, { "type": "read", "cmd": "tail /app/solution/strong_sweep.log", "name": "strong_sweep.log", "path": "/app/solution/strong_sweep.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "low 0\nhigh 0\nmax 1847\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "low 0\nhigh 0\nmax 1847\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4800 }, "formatted_output": "low 0\nhigh 0\nmax 1847\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:29:46.273Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-687bbe95-98ad-456b-9fee-c4ca6767b210", "process_id": "72251", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/asymmetric.py validation 300 2 organic > /app/solution/asym.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/asymmetric.py validation 300 2 organic > /app/solution/asym.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 75, "nanos": 11581600 }, "formatted_output": "" } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-23T05:30:05.345Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:30:05.345Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:30:06.363Z", "text": "The public evaluator confirmed the **0.0311** score, and the refitted submission is saved. I’m now testing blends and a learned reranker; the reranker will be checked on held-out users to assess whether it adds value.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:30:46.841Z", "callId": "call_VORCNjz6ZVAYgmuG6AyfL96h", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/rerank.py <<'PY'\\nfrom model import *\\nfrom sklearn.ensemble import HistGradientBoostingRegressor\\nimport joblib\\n\\ndef features(stage,names,K=500):\\n e=Eval(stage);models=[np.load(P/(stage+'_'+n+'.npy'),mmap_mode='r') for n in names]\\n # Retrieval union: 200 from the strongest organic model, 100 from other models.\\n lists=[];stats=[];us=[]\\n for k in ['count','organic','recent','short','strong','likes','skip']:\\n x=load(k,stage);stats.append(np.asarray(x.sum(0)).ravel());us.append(np.asarray(x[tr].sum(1)).ravel())\\n ip=np.stack(stats,axis=1);up=np.stack(us,axis=1)\\n itemfeat=np.concatenate([np.log1p(ip),np.log((ip[:,[1,2,3,4,5,6]]+1)/(ip[:,[0,0,2,0,0,0]]+1))],axis=1)\\n userfeat=np.concatenate([np.log1p(up),np.log((up[:,[1,2,3,4,5,6]]+1)/(up[:,[0,0,2,0,0,0]]+1))],axis=1)\\n popscore=ip[:,3]+.05*ip[:,1];po=np.argsort(-popscore)\\n F=len(names)*3+itemfeat.shape[1]+userfeat.shape[1]\\n X=np.lib.format.open_memmap(P/f'{stage}_features.npy',mode='w+',dtype=np.float32,shape=(len(targets),K,F));pool=np.empty((len(targets),K),np.int32);Y=np.empty((len(targets),K),np.float32)\\n for st in range(0,len(targets),64):\\n en=min(st+64,len(targets));mask=e.mask(st,en);ss=[];cand=[]\\n for j,mo in enumerate(models):\\n s=np.array(mo[st:en]);s[~mask]=-np.inf;ix=np.argpartition(s,-K,axis=1)[:,-K:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1);cand.append(ix);ss.append(s)\\n for row in range(en-st):\\n v=np.concatenate([cand[0][row,:200]]+[a[row,:100] for a in cand[1:]]+[po[mask[row,po]][:100],cand[0][row]])\\n _,ix=np.unique(v,return_index=True);pool[st+row]=v[np.sort(ix)[:K]]\\n q=pool[st:en];f=[]\\n for s in ss:\\n v=np.take_along_axis(s,q,axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\\n X[st:en]=np.concatenate([np.stack(f,axis=2),itemfeat[q],np.broadcast_to(userfeat[st:en,None,:],(en-st,K,userfeat.shape[1]))],axis=2)\\n Y[st:en]=e.gold[np.arange(st,en)[:,None],q].toarray()\\n X.flush();np.savez(P/f'{stage}_pool.npz',pool=pool,Y=Y,names=names);print('features',stage,X.shape,'retrieved positives',np.sum(Y>0),flush=True);return X,pool,Y,e\\n\\ndef metric(pred,Y,e,rows):\\n ix=np.argsort(-pred,axis=1)[:,:10];scores=(np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg;return scores[rows&e.active].mean()\\n\\ndef train(names):\\n X,pool,Y,e=features('validation',names);rng=np.random.default_rng(2026);fold=rng.random(len(targets))<.5;K=X.shape[1];sample=fold&e.active;hold=~fold\\n # Report the unchanged retrieval model on exactly the held-out users.\\n print('baseline holdout',metric(X[:,:,1],Y,e,hold),flush=True)\\n w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\\n best=(-1,None)\\n for loss in ['poisson','squared_error']:\\n model=HistGradientBoostingRegressor(loss=loss,learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\\n t=time.time();model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape)\\n for blend in [0,.25,.5,.75,1]:\\n # Scale baseline into the same positive prediction range per user.\\n base=np.maximum(X[:,:,1],0);base/=np.maximum(base.max(1,keepdims=True),1e-6);pp=np.maximum(pred,0);pp/=np.maximum(pp.max(1,keepdims=True),1e-6);score=metric(blend*pp+(1-blend)*base,Y,e,hold)\\n print('rerank',loss,'blend',blend,'holdout',score,'seconds',time.time()-t,flush=True)\\n if score>best[0]:best=(score,dict(loss=loss,blend=blend))\\n joblib.dump(model,P/f'rerank_{loss}_holdout.joblib')\\n print('best',best,flush=True);json.dump({'names':names,'config':best[1],'holdout_ndcg':best[0]},open(P/'rerank_config.json','w'))\\nif __name__=='__main__':train(sys.argv[1:])\\nPY\\ntail /app/solution/asym.log; tail /app/solution/sweep.log; tail /app/solution/strong_sweep.log\",\"max_output_tokens\":2200}));\n" }, "result": { "timestamp": "2026-09-23T05:30:46.938Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"b14862\",\"wall_time_seconds\":0.00000723,\"exit_code\":0,\"original_token_count\":146,\"output\":\"validation_asym_organic_300.0_2.0 72.85397028923035\\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\\nsolved 44.20782208442688\\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\\nkernel 7.929508447647095\\nchol 9.285109043121338\\nsolved 44.72099852561951\\nvalidation_ease_recent_300 NDCG 0.02825181915291134\\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\\nkernel 8.21726393699646\\nchol 9.574827194213867\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:30:46.901Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-218bcbab-aeaf-4176-9f46-935f65eb67a2", "process_id": "70254", "command": [ "/bin/sh", "-lc", "cat > /app/solution/rerank.py <<'PY'\nfrom model import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\n\ndef features(stage,names,K=500):\n e=Eval(stage);models=[np.load(P/(stage+'_'+n+'.npy'),mmap_mode='r') for n in names]\n # Retrieval union: 200 from the strongest organic model, 100 from other models.\n lists=[];stats=[];us=[]\n for k in ['count','organic','recent','short','strong','likes','skip']:\n x=load(k,stage);stats.append(np.asarray(x.sum(0)).ravel());us.append(np.asarray(x[tr].sum(1)).ravel())\n ip=np.stack(stats,axis=1);up=np.stack(us,axis=1)\n itemfeat=np.concatenate([np.log1p(ip),np.log((ip[:,[1,2,3,4,5,6]]+1)/(ip[:,[0,0,2,0,0,0]]+1))],axis=1)\n userfeat=np.concatenate([np.log1p(up),np.log((up[:,[1,2,3,4,5,6]]+1)/(up[:,[0,0,2,0,0,0]]+1))],axis=1)\n popscore=ip[:,3]+.05*ip[:,1];po=np.argsort(-popscore)\n F=len(names)*3+itemfeat.shape[1]+userfeat.shape[1]\n X=np.lib.format.open_memmap(P/f'{stage}_features.npy',mode='w+',dtype=np.float32,shape=(len(targets),K,F));pool=np.empty((len(targets),K),np.int32);Y=np.empty((len(targets),K),np.float32)\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));mask=e.mask(st,en);ss=[];cand=[]\n for j,mo in enumerate(models):\n s=np.array(mo[st:en]);s[~mask]=-np.inf;ix=np.argpartition(s,-K,axis=1)[:,-K:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1);cand.append(ix);ss.append(s)\n for row in range(en-st):\n v=np.concatenate([cand[0][row,:200]]+[a[row,:100] for a in cand[1:]]+[po[mask[row,po]][:100],cand[0][row]])\n _,ix=np.unique(v,return_index=True);pool[st+row]=v[np.sort(ix)[:K]]\n q=pool[st:en];f=[]\n for s in ss:\n v=np.take_along_axis(s,q,axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n X[st:en]=np.concatenate([np.stack(f,axis=2),itemfeat[q],np.broadcast_to(userfeat[st:en,None,:],(en-st,K,userfeat.shape[1]))],axis=2)\n Y[st:en]=e.gold[np.arange(st,en)[:,None],q].toarray()\n X.flush();np.savez(P/f'{stage}_pool.npz',pool=pool,Y=Y,names=names);print('features',stage,X.shape,'retrieved positives',np.sum(Y>0),flush=True);return X,pool,Y,e\n\ndef metric(pred,Y,e,rows):\n ix=np.argsort(-pred,axis=1)[:,:10];scores=(np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg;return scores[rows&e.active].mean()\n\ndef train(names):\n X,pool,Y,e=features('validation',names);rng=np.random.default_rng(2026);fold=rng.random(len(targets))<.5;K=X.shape[1];sample=fold&e.active;hold=~fold\n # Report the unchanged retrieval model on exactly the held-out users.\n print('baseline holdout',metric(X[:,:,1],Y,e,hold),flush=True)\n w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\n best=(-1,None)\n for loss in ['poisson','squared_error']:\n model=HistGradientBoostingRegressor(loss=loss,learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n t=time.time();model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape)\n for blend in [0,.25,.5,.75,1]:\n # Scale baseline into the same positive prediction range per user.\n base=np.maximum(X[:,:,1],0);base/=np.maximum(base.max(1,keepdims=True),1e-6);pp=np.maximum(pred,0);pp/=np.maximum(pp.max(1,keepdims=True),1e-6);score=metric(blend*pp+(1-blend)*base,Y,e,hold)\n print('rerank',loss,'blend',blend,'holdout',score,'seconds',time.time()-t,flush=True)\n if score>best[0]:best=(score,dict(loss=loss,blend=blend))\n joblib.dump(model,P/f'rerank_{loss}_holdout.joblib')\n print('best',best,flush=True);json.dump({'names':names,'config':best[1],'holdout_ndcg':best[0]},open(P/'rerank_config.json','w'))\nif __name__=='__main__':train(sys.argv[1:])\nPY\ntail /app/solution/asym.log; tail /app/solution/sweep.log; tail /app/solution/strong_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/rerank.py <<'PY'\nfrom model import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\n\ndef features(stage,names,K=500):\n e=Eval(stage);models=[np.load(P/(stage+'_'+n+'.npy'),mmap_mode='r') for n in names]\n # Retrieval union: 200 from the strongest organic model, 100 from other models.\n lists=[];stats=[];us=[]\n for k in ['count','organic','recent','short','strong','likes','skip']:\n x=load(k,stage);stats.append(np.asarray(x.sum(0)).ravel());us.append(np.asarray(x[tr].sum(1)).ravel())\n ip=np.stack(stats,axis=1);up=np.stack(us,axis=1)\n itemfeat=np.concatenate([np.log1p(ip),np.log((ip[:,[1,2,3,4,5,6]]+1)/(ip[:,[0,0,2,0,0,0]]+1))],axis=1)\n userfeat=np.concatenate([np.log1p(up),np.log((up[:,[1,2,3,4,5,6]]+1)/(up[:,[0,0,2,0,0,0]]+1))],axis=1)\n popscore=ip[:,3]+.05*ip[:,1];po=np.argsort(-popscore)\n F=len(names)*3+itemfeat.shape[1]+userfeat.shape[1]\n X=np.lib.format.open_memmap(P/f'{stage}_features.npy',mode='w+',dtype=np.float32,shape=(len(targets),K,F));pool=np.empty((len(targets),K),np.int32);Y=np.empty((len(targets),K),np.float32)\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));mask=e.mask(st,en);ss=[];cand=[]\n for j,mo in enumerate(models):\n s=np.array(mo[st:en]);s[~mask]=-np.inf;ix=np.argpartition(s,-K,axis=1)[:,-K:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1);cand.append(ix);ss.append(s)\n for row in range(en-st):\n v=np.concatenate([cand[0][row,:200]]+[a[row,:100] for a in cand[1:]]+[po[mask[row,po]][:100],cand[0][row]])\n _,ix=np.unique(v,return_index=True);pool[st+row]=v[np.sort(ix)[:K]]\n q=pool[st:en];f=[]\n for s in ss:\n v=np.take_along_axis(s,q,axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n X[st:en]=np.concatenate([np.stack(f,axis=2),itemfeat[q],np.broadcast_to(userfeat[st:en,None,:],(en-st,K,userfeat.shape[1]))],axis=2)\n Y[st:en]=e.gold[np.arange(st,en)[:,None],q].toarray()\n X.flush();np.savez(P/f'{stage}_pool.npz',pool=pool,Y=Y,names=names);print('features',stage,X.shape,'retrieved positives',np.sum(Y>0),flush=True);return X,pool,Y,e\n\ndef metric(pred,Y,e,rows):\n ix=np.argsort(-pred,axis=1)[:,:10];scores=(np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg;return scores[rows&e.active].mean()\n\ndef train(names):\n X,pool,Y,e=features('validation',names);rng=np.random.default_rng(2026);fold=rng.random(len(targets))<.5;K=X.shape[1];sample=fold&e.active;hold=~fold\n # Report the unchanged retrieval model on exactly the held-out users.\n print('baseline holdout',metric(X[:,:,1],Y,e,hold),flush=True)\n w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\n best=(-1,None)\n for loss in ['poisson','squared_error']:\n model=HistGradientBoostingRegressor(loss=loss,learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n t=time.time();model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape)\n for blend in [0,.25,.5,.75,1]:\n # Scale baseline into the same positive prediction range per user.\n base=np.maximum(X[:,:,1],0);base/=np.maximum(base.max(1,keepdims=True),1e-6);pp=np.maximum(pred,0);pp/=np.maximum(pp.max(1,keepdims=True),1e-6);score=metric(blend*pp+(1-blend)*base,Y,e,hold)\n print('rerank',loss,'blend',blend,'holdout',score,'seconds',time.time()-t,flush=True)\n if score>best[0]:best=(score,dict(loss=loss,blend=blend))\n joblib.dump(model,P/f'rerank_{loss}_holdout.joblib')\n print('best',best,flush=True);json.dump({'names':names,'config':best[1],'holdout_ndcg':best[0]},open(P/'rerank_config.json','w'))\nif __name__=='__main__':train(sys.argv[1:])\nPY\ntail /app/solution/asym.log; tail /app/solution/sweep.log; tail /app/solution/strong_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_asym_organic_300.0_2.0 72.85397028923035\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\n", "stderr": "", "aggregated_output": "validation_asym_organic_300.0_2.0 72.85397028923035\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7230 }, "formatted_output": "validation_asym_organic_300.0_2.0 72.85397028923035\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:30:46.934Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", 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"exec-a7b17fad-e980-4af0-af4e-2a108b60db96", "process_id": "98616", "command": [ "/bin/sh", "-lc", "cat > /app/solution/sweep.py <<'PY'\nfrom model import *\nfor variant,reg in [('hybrid',100),('hybrid',1000),('recent',300),('liked',300)]:ease('validation',variant,reg)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/sweep.py > /app/solution/sweep.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/sweep.py <<'PY'\nfrom model import *\nfor variant,reg in [('hybrid',100),('hybrid',1000),('recent',300),('liked',300)]:ease('validation',variant,reg)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/sweep.py > /app/solution/sweep.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 193, "nanos": 342360783 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:30:59.055Z", "callId": "call_whs0cPMRzK9idHkU2BjCQXFa", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/rerank.py ease_strong_300.0 ease_recent_300 ease_hybrid_1000 asym_organic_300.0_2.0 > /app/solution/rerank.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend.py validation_ease_strong_300.0 validation_ease_recent_300 validation_ease_hybrid_1000 validation_asym_organic_300.0_2.0 > /app/solution/blend.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:31:01.408Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"f9c151\",\"wall_time_seconds\":1.001020472,\"session_id\":5194,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"30a23c\",\"wall_time_seconds\":1.001551684,\"session_id\":97587,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:31:01 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:30:59.151Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab364072ae487d29b2c8c9b47493fd3", "usage": { "input_tokens": 44491, "cached_input_tokens": 42112, "cache_write_input_tokens": 0, "output_tokens": 304, "reasoning_output_tokens": 111, "total_tokens": 44795 }, "turn_token_usage": { "input_tokens": 1039250, "cached_input_tokens": 994304, 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/app/solution/blend.py validation_ease_strong_300.0 validation_ease_recent_300 validation_ease_hybrid_1000 validation_asym_organic_300.0_2.0 > /app/solution/blend.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend.py validation_ease_strong_300.0 validation_ease_recent_300 validation_ease_hybrid_1000 validation_asym_organic_300.0_2.0 > /app/solution/blend.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 43, "nanos": 14470022 }, "formatted_output": "" } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-23T05:31:48.006Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:31:48.006Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:31:48.803Z", "callId": "call_xtbkkuVQDPuzM9xiflG98S5p", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/rerank.log; tail /app/solution/blend.log; tail /app/solution/strong_sweep.log; tail /app/solution/sweep.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:31:48.880Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"1dade4\",\"wall_time_seconds\":0.000005591,\"exit_code\":0,\"original_token_count\":583,\"output\":\"features validation (8456, 500, 38) retrieved positives 11540\\nbaseline holdout 0.03196263974640137\\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\nvalidation_strong_1000_0_0 time 43.76785898208618\\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\\nkernel 7.929508447647095\\nchol 9.285109043121338\\nsolved 44.72099852561951\\nvalidation_ease_recent_300 NDCG 0.02825181915291134\\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\\nkernel 8.21726393699646\\nchol 9.574827194213867\\nsolved 42.9216411113739\\nvalidation_ease_liked_300 NDCG 0.02145577970503426\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:31:48.861Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-56f62aba-2e6a-44aa-805f-9326dccf6fe2", "process_id": "63600", "command": [ "/bin/sh", "-lc", "tail /app/solution/rerank.log; tail /app/solution/blend.log; tail /app/solution/strong_sweep.log; tail /app/solution/sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/rerank.log", "name": "rerank.log", "path": "/app/solution/rerank.log" }, { "type": "read", "cmd": "tail /app/solution/blend.log", "name": "blend.log", "path": "/app/solution/blend.log" }, { "type": "read", "cmd": "tail /app/solution/strong_sweep.log", "name": "strong_sweep.log", "path": "/app/solution/strong_sweep.log" }, { "type": "read", "cmd": "tail /app/solution/sweep.log", "name": "sweep.log", "path": "/app/solution/sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n", "stderr": "", "aggregated_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5591 }, "formatted_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:31:48.875Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3641595e087d28a8f9f1ebf28b8cb", "usage": { "input_tokens": 44906, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 1403, "reasoning_output_tokens": 1337, "total_tokens": 46309 }, "turn_token_usage": { "input_tokens": 1084156, "cached_input_tokens": 1038592, "cache_write_input_tokens": 0, "output_tokens": 17210, "reasoning_output_tokens": 6600, "total_tokens": 1101366 }, "thread_token_usage": { "input_tokens": 1084156, "cached_input_tokens": 1038592, "cache_write_input_tokens": 0, "output_tokens": 17210, "reasoning_output_tokens": 6600, "total_tokens": 1101366 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-23T05:31:52.382Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:31:52.382Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:32:07.235Z", "callId": "call_5UNOF4UgwxqiHoNBTKSXNhsX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/extra_prepare.py <<'PY'\\nfrom model import *\\nimport pyarrow.parquet as pq\\nfor stage in ['train','val']:\\n t=time.time();cut=24786800 if stage=='train' else 25393400;parts=[];outside=sparse.csr_matrix((N,10000001),dtype=np.float32)\\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\\n d=batch.to_pandas();d=d[d.played_ratio_pct>=80];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ts=d.timestamp.to_numpy(dtype=np.float64);age=(cut-ts)/86400;v=((.3+.7*d.is_organic.values)*np.exp(-age/90)).astype(np.float32)\\n ok=(r>=0)&(c<0);outside+=sparse.csr_matrix((v[ok],(r[ok],d.item_id.values[ok])),shape=outside.shape)\\n ok=(r>=0)&(c>=0);key=r[ok].astype(np.int64)*10000+(ts[ok]//7200).astype(np.int64);_,sess=np.unique(key,return_inverse=True);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(sess,c[ok])),shape=(sess.max()+1,M));x.data[:]=1;parts.append(x)\\n outside.data=np.log1p(outside.data);sparse.save_npz(P/f'{stage}_outside.npz',outside);sparse.save_npz(P/f'{stage}_sessions.npz',sparse.vstack(parts,format='csr'));print(stage,'done',time.time()-t,'outside',outside.nnz,'sessions',sum(x.shape[0] for x in parts),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_prepare.py > /app/solution/extra_prepare.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/rerank.log; tail /app/solution/strong_sweep.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:32:08.454Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"af3075\",\"wall_time_seconds\":1.001305471,\"session_id\":33168,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"549ea4\",\"wall_time_seconds\":0.00000581,\"exit_code\":0,\"original_token_count\":101,\"output\":\"features validation (8456, 500, 38) retrieved positives 11540\\nbaseline holdout 0.03196263974640137\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\nvalidation_strong_1000_0_0 time 43.76785898208618\\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\\nvalidation_strong_300_1_0 time 44.70142436027527\\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\\n\"}\n{\"current_time\":\"2026-09-23 05:32:08 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:07.305Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3644522f487d2b4f3d848615c01b9", "usage": { "input_tokens": 47259, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 559, "reasoning_output_tokens": 47, "total_tokens": 47818 }, "turn_token_usage": { "input_tokens": 1131415, "cached_input_tokens": 1083264, "cache_write_input_tokens": 0, "output_tokens": 17769, "reasoning_output_tokens": 6647, "total_tokens": 1149184 }, "thread_token_usage": { "input_tokens": 1131415, "cached_input_tokens": 1083264, "cache_write_input_tokens": 0, "output_tokens": 17769, "reasoning_output_tokens": 6647, "total_tokens": 1149184 } } }, { "kind": "context", "timestamp": "2026-09-23T05:32:08.443Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d1b8ea5b-a35b-4113-8cac-924638fbac85", "process_id": "26834", "command": [ "/bin/sh", "-lc", "tail /app/solution/rerank.log; tail /app/solution/strong_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/rerank.log", "name": "rerank.log", "path": "/app/solution/rerank.log" }, { "type": "read", "cmd": "tail /app/solution/strong_sweep.log", "name": "strong_sweep.log", "path": "/app/solution/strong_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n", "stderr": "", "aggregated_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5810 }, "formatted_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n" } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-23T05:32:13.035Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:13.035Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:32:15.896Z", "callId": "call_RTfWJt0WIuTbjGCHJ5PK3qGk", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py test 1000 0 0 > /app/solution/final_strong.log 2>&1 && cp /app/solution/test_strong_1000.0_0.0_0.0.parquet /app/solution/submission.parquet\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:32:17.099Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"c73384\",\"wall_time_seconds\":1.000558875,\"session_id\":37905,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:16.000Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36458a71c87d29bf54ab6bdd8b156", "usage": { "input_tokens": 48081, "cached_input_tokens": 47104, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 89, "total_tokens": 48276 }, "turn_token_usage": { "input_tokens": 1179496, "cached_input_tokens": 1130368, "cache_write_input_tokens": 0, "output_tokens": 17964, "reasoning_output_tokens": 6736, "total_tokens": 1197460 }, "thread_token_usage": { "input_tokens": 1179496, "cached_input_tokens": 1130368, "cache_write_input_tokens": 0, "output_tokens": 17964, "reasoning_output_tokens": 6736, "total_tokens": 1197460 } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-23T05:32:20.513Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:32:20.513Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:32:22.598Z", "text": "Stronger regularization raised the best single model to **0.0318 NDCG@10**. I’m also testing two additional history signals: listening outside the retrieval catalog and tracks heard in the same session. Both can inform taste while recommendations stay within the supplied eligibility masks.\n" }, { "kind": "context", "timestamp": "2026-09-23T05:32:29.521Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-954a2cf9-4342-4a9e-aad7-2f5b4e429a69", "process_id": "33168", "command": [ "/bin/sh", "-lc", "cat > /app/solution/extra_prepare.py <<'PY'\nfrom model import *\nimport pyarrow.parquet as pq\nfor stage in ['train','val']:\n t=time.time();cut=24786800 if stage=='train' else 25393400;parts=[];outside=sparse.csr_matrix((N,10000001),dtype=np.float32)\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();d=d[d.played_ratio_pct>=80];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ts=d.timestamp.to_numpy(dtype=np.float64);age=(cut-ts)/86400;v=((.3+.7*d.is_organic.values)*np.exp(-age/90)).astype(np.float32)\n ok=(r>=0)&(c<0);outside+=sparse.csr_matrix((v[ok],(r[ok],d.item_id.values[ok])),shape=outside.shape)\n ok=(r>=0)&(c>=0);key=r[ok].astype(np.int64)*10000+(ts[ok]//7200).astype(np.int64);_,sess=np.unique(key,return_inverse=True);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(sess,c[ok])),shape=(sess.max()+1,M));x.data[:]=1;parts.append(x)\n outside.data=np.log1p(outside.data);sparse.save_npz(P/f'{stage}_outside.npz',outside);sparse.save_npz(P/f'{stage}_sessions.npz',sparse.vstack(parts,format='csr'));print(stage,'done',time.time()-t,'outside',outside.nnz,'sessions',sum(x.shape[0] for x in parts),flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_prepare.py > /app/solution/extra_prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/extra_prepare.py <<'PY'\nfrom model import *\nimport pyarrow.parquet as pq\nfor stage in ['train','val']:\n t=time.time();cut=24786800 if stage=='train' else 25393400;parts=[];outside=sparse.csr_matrix((N,10000001),dtype=np.float32)\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();d=d[d.played_ratio_pct>=80];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ts=d.timestamp.to_numpy(dtype=np.float64);age=(cut-ts)/86400;v=((.3+.7*d.is_organic.values)*np.exp(-age/90)).astype(np.float32)\n ok=(r>=0)&(c<0);outside+=sparse.csr_matrix((v[ok],(r[ok],d.item_id.values[ok])),shape=outside.shape)\n ok=(r>=0)&(c>=0);key=r[ok].astype(np.int64)*10000+(ts[ok]//7200).astype(np.int64);_,sess=np.unique(key,return_inverse=True);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(sess,c[ok])),shape=(sess.max()+1,M));x.data[:]=1;parts.append(x)\n outside.data=np.log1p(outside.data);sparse.save_npz(P/f'{stage}_outside.npz',outside);sparse.save_npz(P/f'{stage}_sessions.npz',sparse.vstack(parts,format='csr'));print(stage,'done',time.time()-t,'outside',outside.nnz,'sessions',sum(x.shape[0] for x in parts),flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_prepare.py > /app/solution/extra_prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 22, "nanos": 115785476 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:32:51.594Z", "callId": "call_aSXpilzIdQQxr6Saos7eu1Gt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/outside.py <<'PY'\\nfrom model import *\\ndef fit(stage,reg=1000,weight=.5):\\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data);o=sparse.load_npz(P/'train_outside.npz')\\n if stage=='test':\\n # Outside-cache values are logarithmic; decay and merge in count space.\\n o.data=np.expm1(o.data)*np.exp(-(7+1800/86400)/90);v=sparse.load_npz(P/'val_outside.npz');v.data=np.expm1(v.data);o+=v;o.data=np.log1p(o.data)\\n k=(x@x.T+weight*(o@o.T)).toarray(order='F');del o;k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\\n name=f'{stage}_outside_{reg}_{weight}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,M,1000):\\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]))\\nPY\\ncat > /app/solution/session_knn.py <<'PY'\\nfrom model import *\\ndef fit(stage,power=.6,top=200):\\n t=time.time();x=sparse.load_npz(P/'train_sessions.npz')\\n if stage=='test':x=sparse.vstack([x,sparse.load_npz(P/'val_sessions.npz')],format='csr')\\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)))@x\\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power;xt=x.T.tocsr();rows=[];cols=[];vals=[]\\n for st in range(0,M,300):\\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0;ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_session_sim_{power}.npz',sim);print('sim built',time.time()-t,flush=True)\\n for profile in ['short','strong']:\\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);out=np.lib.format.open_memmap(P/f'{stage}_session_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\\n for st in range(0,len(targets),128):out[st:st+128]=(h[st:st+128]@sim).toarray()\\n out.flush();Eval(stage).run(out,f'{stage}_session_{power}_{profile}')\\n print('done',time.time()-t,flush=True)\\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]))\\nPY\\ntail /app/solution/rerank.log; tail /app/solution/extra_prepare.log; tail /app/solution/strong_sweep.log; tail /app/solution/final_strong.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:32:51.804Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"0de423\",\"wall_time_seconds\":0.00000375,\"exit_code\":0,\"original_token_count\":230,\"output\":\"features validation (8456, 500, 38) retrieved positives 11540\\nbaseline holdout 0.03196263974640137\\nrerank poisson blend 0 holdout 0.03196263974640137 seconds 40.55131530761719\\nrerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\\ntrain done 19.82935070991516 outside 2764264 sessions 2334996\\nval done 0.8569111824035645 outside 223136 sessions 76366\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\nvalidation_strong_1000_0_0 time 43.76785898208618\\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\\nvalidation_strong_300_1_0 time 44.70142436027527\\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:32:51.659Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-95005555-6b58-48e5-9091-303c3d788b0b", "process_id": "89603", "command": [ "/bin/sh", "-lc", "cat > /app/solution/outside.py <<'PY'\nfrom model import *\ndef fit(stage,reg=1000,weight=.5):\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data);o=sparse.load_npz(P/'train_outside.npz')\n if stage=='test':\n # Outside-cache values are logarithmic; decay and merge in count space.\n o.data=np.expm1(o.data)*np.exp(-(7+1800/86400)/90);v=sparse.load_npz(P/'val_outside.npz');v.data=np.expm1(v.data);o+=v;o.data=np.log1p(o.data)\n k=(x@x.T+weight*(o@o.T)).toarray(order='F');del o;k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_outside_{reg}_{weight}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]))\nPY\ncat > /app/solution/session_knn.py <<'PY'\nfrom model import *\ndef fit(stage,power=.6,top=200):\n t=time.time();x=sparse.load_npz(P/'train_sessions.npz')\n if stage=='test':x=sparse.vstack([x,sparse.load_npz(P/'val_sessions.npz')],format='csr')\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)))@x\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power;xt=x.T.tocsr();rows=[];cols=[];vals=[]\n for st in range(0,M,300):\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0;ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_session_sim_{power}.npz',sim);print('sim built',time.time()-t,flush=True)\n for profile in ['short','strong']:\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);out=np.lib.format.open_memmap(P/f'{stage}_session_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,len(targets),128):out[st:st+128]=(h[st:st+128]@sim).toarray()\n out.flush();Eval(stage).run(out,f'{stage}_session_{power}_{profile}')\n print('done',time.time()-t,flush=True)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]))\nPY\ntail /app/solution/rerank.log; tail /app/solution/extra_prepare.log; tail /app/solution/strong_sweep.log; tail /app/solution/final_strong.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/outside.py <<'PY'\nfrom model import *\ndef fit(stage,reg=1000,weight=.5):\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data);o=sparse.load_npz(P/'train_outside.npz')\n if stage=='test':\n # Outside-cache values are logarithmic; decay and merge in count space.\n o.data=np.expm1(o.data)*np.exp(-(7+1800/86400)/90);v=sparse.load_npz(P/'val_outside.npz');v.data=np.expm1(v.data);o+=v;o.data=np.log1p(o.data)\n k=(x@x.T+weight*(o@o.T)).toarray(order='F');del o;k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_outside_{reg}_{weight}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]))\nPY\ncat > /app/solution/session_knn.py <<'PY'\nfrom model import *\ndef fit(stage,power=.6,top=200):\n t=time.time();x=sparse.load_npz(P/'train_sessions.npz')\n if stage=='test':x=sparse.vstack([x,sparse.load_npz(P/'val_sessions.npz')],format='csr')\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)))@x\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power;xt=x.T.tocsr();rows=[];cols=[];vals=[]\n for st in range(0,M,300):\n en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0;ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_session_sim_{power}.npz',sim);print('sim built',time.time()-t,flush=True)\n for profile in ['short','strong']:\n h=load(profile,stage)[tr];h.data=np.log1p(h.data);out=np.lib.format.open_memmap(P/f'{stage}_session_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,len(targets),128):out[st:st+128]=(h[st:st+128]@sim).toarray()\n out.flush();Eval(stage).run(out,f'{stage}_session_{power}_{profile}')\n print('done',time.time()-t,flush=True)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]))\nPY\ntail /app/solution/rerank.log; tail /app/solution/extra_prepare.log; tail /app/solution/strong_sweep.log; tail /app/solution/final_strong.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nrerank poisson blend 0 holdout 0.03196263974640137 seconds 40.55131530761719\nrerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\ntrain done 19.82935070991516 outside 2764264 sessions 2334996\nval done 0.8569111824035645 outside 223136 sessions 76366\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n", "stderr": "", "aggregated_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nrerank poisson blend 0 holdout 0.03196263974640137 seconds 40.55131530761719\nrerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\ntrain done 19.82935070991516 outside 2764264 sessions 2334996\nval done 0.8569111824035645 outside 223136 sessions 76366\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3750 }, "formatted_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nrerank poisson blend 0 holdout 0.03196263974640137 seconds 40.55131530761719\nrerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\ntrain done 19.82935070991516 outside 2764264 sessions 2334996\nval done 0.8569111824035645 outside 223136 sessions 76366\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:32:51.750Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36461420087d2b44b1120d43018ac", "usage": { "input_tokens": 48333, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 1088, "reasoning_output_tokens": 48, "total_tokens": 49421 }, "turn_token_usage": { "input_tokens": 1227829, "cached_input_tokens": 1178240, "cache_write_input_tokens": 0, "output_tokens": 19052, "reasoning_output_tokens": 6784, "total_tokens": 1246881 }, "thread_token_usage": { "input_tokens": 1227829, "cached_input_tokens": 1178240, "cache_write_input_tokens": 0, "output_tokens": 19052, "reasoning_output_tokens": 6784, "total_tokens": 1246881 } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:03.837Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-28069d87-f1d3-44ef-8c74-6eec82d1c6e1", "process_id": "5194", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/rerank.py ease_strong_300.0 ease_recent_300 ease_hybrid_1000 asym_organic_300.0_2.0 > /app/solution/rerank.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/rerank.py ease_strong_300.0 ease_recent_300 ease_hybrid_1000 asym_organic_300.0_2.0 > /app/solution/rerank.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 124, "nanos": 606792019 }, "formatted_output": "" } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-23T05:33:13.800Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:13.800Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:33:16.281Z", "callId": "call_wq5g2zABF7gufMzCrUOAMek2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py validation 1000 1 > /app/solution/outside.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py validation 0.6 > /app/solution/session_knn.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/rerank.log; tail /app/solution/final_strong.log; tail /app/solution/strong_sweep.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:33:19.025Z", "content": "Script completed\nWall time 2.7 seconds\nOutput:\n\n{\"chunk_id\":\"9c8931\",\"wall_time_seconds\":1.00126664,\"session_id\":65452,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"f01f34\",\"wall_time_seconds\":1.001563487,\"session_id\":85160,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"ed38b9\",\"wall_time_seconds\":0.00000453,\"exit_code\":0,\"original_token_count\":307,\"output\":\"rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\nvalidation_strong_1000_0_0 time 43.76785898208618\\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\\nvalidation_strong_300_1_0 time 44.70142436027527\\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\\nvalidation_strong_300_2_0.25 time 63.89295029640198\\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\\n\"}\n{\"current_time\":\"2026-09-23 05:33:19 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:33:16.336Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36484104087d28b720c8df9902f82", "usage": { "input_tokens": 49814, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 697, "reasoning_output_tokens": 516, "total_tokens": 50511 }, "turn_token_usage": { "input_tokens": 1277643, "cached_input_tokens": 1226368, "cache_write_input_tokens": 0, "output_tokens": 19749, "reasoning_output_tokens": 7300, "total_tokens": 1297392 }, "thread_token_usage": { "input_tokens": 1277643, "cached_input_tokens": 1226368, "cache_write_input_tokens": 0, "output_tokens": 19749, "reasoning_output_tokens": 7300, "total_tokens": 1297392 } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:18.873Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-63793b0d-e82d-429e-a9de-0bbd3af5a4ac", "process_id": "3154", "command": [ "/bin/sh", "-lc", "tail /app/solution/rerank.log; tail /app/solution/final_strong.log; tail /app/solution/strong_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/rerank.log", "name": "rerank.log", "path": "/app/solution/rerank.log" }, { "type": "read", "cmd": "tail /app/solution/final_strong.log", "name": "final_strong.log", "path": "/app/solution/final_strong.log" }, { "type": "read", "cmd": "tail /app/solution/strong_sweep.log", "name": "strong_sweep.log", "path": "/app/solution/strong_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\n", "stderr": "", "aggregated_output": "rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4530 }, "formatted_output": "rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:33:22.272Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-02280443-fcdc-4f7b-ad21-6d62cbb24ba6", "process_id": "37905", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py test 1000 0 0 > /app/solution/final_strong.log 2>&1 && cp /app/solution/test_strong_1000.0_0.0_0.0.parquet /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py test 1000 0 0 > /app/solution/final_strong.log 2>&1 && cp /app/solution/test_strong_1000.0_0.0_0.0.parquet /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 66, "nanos": 174579767 }, "formatted_output": "" } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-23T05:33:37.851Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:37.851Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:33:55.998Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e5616cd0-92a9-408c-832c-f31a15496846", "process_id": "27589", "command": [ "/bin/sh", "-lc", "cat > /app/solution/blend.py <<'PY'\nfrom model import *\ndef tune(names):\n e=Eval();a=[np.load(P/(n+'.npy'),mmap_mode='r') for n in names]; rr=[];ff=[];gg=[]\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));mask=e.mask(st,en);cand=[];full=[]\n for s in a:\n b=np.asarray(s[st:en]).copy();b[~mask]=-np.inf;ix=np.argpartition(b,-100,axis=1)[:,-100:];cand.append(ix);full.append(b)\n pool=np.concatenate(cand,axis=1)\n # duplicate candidates get -infinity in all features after their first occurrence\n for row in range(len(pool)):\n _,ix=np.unique(pool[row],return_index=True);du=np.ones(pool.shape[1],bool);du[ix]=False;pool[row,du]=-1\n f=[]\n for b in full:\n scale=np.partition(b,-10,axis=1)[:,-10:].mean(1);v=np.take_along_axis(b,np.maximum(pool,0),axis=1)/np.maximum(scale[:,None],1e-6);v[pool<0]=-1e6;f.append(v)\n rr.append(pool);ff.append(np.stack(f,axis=2));gg.append(e.gold[np.arange(st,en)[:,None],np.maximum(pool,0)].toarray())\n pool=np.concatenate(rr);feat=np.concatenate(ff);gold=np.concatenate(gg);gold[pool<0]=0\n def score(w):\n s=feat@w;ix=np.argsort(-s,axis=1)[:,:10];dcg=(np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg;return dcg[e.active].mean()\n best=(0,None)\n rng=np.random.default_rng(42)\n weights=[np.eye(len(a))[i] for i in range(len(a))]\n weights += [rng.dirichlet(np.ones(len(a))*.35) for _ in range(150)]\n for w in weights:\n scoreval=score(w)\n if scoreval>best[0]:best=(scoreval,w);print('best',scoreval,dict(zip(names,w)),flush=True)\n np.savez(P/'blend_tuning.npz',pool=pool,feat=feat,gold=gold,names=np.array(names),weights=best[1]);return best\nif __name__=='__main__':tune(sys.argv[1:])\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py > /app/solution/strong_sweep.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/blend.py <<'PY'\nfrom model import *\ndef tune(names):\n e=Eval();a=[np.load(P/(n+'.npy'),mmap_mode='r') for n in names]; rr=[];ff=[];gg=[]\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));mask=e.mask(st,en);cand=[];full=[]\n for s in a:\n b=np.asarray(s[st:en]).copy();b[~mask]=-np.inf;ix=np.argpartition(b,-100,axis=1)[:,-100:];cand.append(ix);full.append(b)\n pool=np.concatenate(cand,axis=1)\n # duplicate candidates get -infinity in all features after their first occurrence\n for row in range(len(pool)):\n _,ix=np.unique(pool[row],return_index=True);du=np.ones(pool.shape[1],bool);du[ix]=False;pool[row,du]=-1\n f=[]\n for b in full:\n scale=np.partition(b,-10,axis=1)[:,-10:].mean(1);v=np.take_along_axis(b,np.maximum(pool,0),axis=1)/np.maximum(scale[:,None],1e-6);v[pool<0]=-1e6;f.append(v)\n rr.append(pool);ff.append(np.stack(f,axis=2));gg.append(e.gold[np.arange(st,en)[:,None],np.maximum(pool,0)].toarray())\n pool=np.concatenate(rr);feat=np.concatenate(ff);gold=np.concatenate(gg);gold[pool<0]=0\n def score(w):\n s=feat@w;ix=np.argsort(-s,axis=1)[:,:10];dcg=(np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg;return dcg[e.active].mean()\n best=(0,None)\n rng=np.random.default_rng(42)\n weights=[np.eye(len(a))[i] for i in range(len(a))]\n weights += [rng.dirichlet(np.ones(len(a))*.35) for _ in range(150)]\n for w in weights:\n scoreval=score(w)\n if scoreval>best[0]:best=(scoreval,w);print('best',scoreval,dict(zip(names,w)),flush=True)\n np.savez(P/'blend_tuning.npz',pool=pool,feat=feat,gold=gold,names=np.array(names),weights=best[1]);return best\nif __name__=='__main__':tune(sys.argv[1:])\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py > /app/solution/strong_sweep.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 256, "nanos": 543850077 }, "formatted_output": "" } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-23T05:33:56.948Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:33:56.948Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:33:58.026Z", "text": "The learned reranker is the strongest result so far: **0.0556 NDCG@10 on held-out users**, versus **0.0320** for the base model on those same users. I’m checking its settings and adding recent explicit-action statistics before the final refit.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:34:07.449Z", "callId": "call_Z87RFdUs3WpHvqf9Gh0n9wJD", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune_rerank.py <<'PY'\\nfrom rerank import *\\ne=Eval();X=np.load(P/'validation_features.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;hold=~fold;K=X.shape[1];w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\\nfor leaves,iters,l2 in [(15,300,10),(31,250,10),(31,250,50),(7,300,10)]:\\n t=time.time();model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42);model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);score=metric(pred,Y,e,hold);print('config',leaves,iters,l2,'score',score,'time',time.time()-t,flush=True);joblib.dump(model,P/f'rerank_tuned_{leaves}_{iters}_{l2}.joblib')\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank.py > /app/solution/tune_rerank.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/final_strong.log; tail /app/solution/strong_sweep.log; tail /app/solution/outside.log; tail /app/solution/session_knn.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:34:08.855Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"fdb950\",\"wall_time_seconds\":1.000871894,\"session_id\":93512,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"bf3578\",\"wall_time_seconds\":0.00000279,\"exit_code\":0,\"original_token_count\":177,\"output\":\"test_strong_1000.0_0.0_0.0 time 62.70835757255554\\ntest_strong_1000.0_0.0_0.0 NDCG 0.0\\nvalidation_strong_100_0_0 time 42.17256259918213\\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\\nvalidation_strong_1000_0_0 time 43.76785898208618\\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\\nvalidation_strong_300_1_0 time 44.70142436027527\\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\\nvalidation_strong_300_2_0.25 time 63.89295029640198\\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\\nvalidation_strong_100_1_0.25 time 53.56865644454956\\nvalidation_strong_100_1_0.25 NDCG 0.028268317226975957\\nsim built 44.459381103515625\\nlow 0\\nhigh 0\\nmax 5668\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:07.549Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3649f563887d2ae8f01a7ed234b07", "usage": { "input_tokens": 51115, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 1553, "reasoning_output_tokens": 1034, "total_tokens": 52668 }, "turn_token_usage": { "input_tokens": 1328758, "cached_input_tokens": 1276032, "cache_write_input_tokens": 0, "output_tokens": 21302, "reasoning_output_tokens": 8334, "total_tokens": 1350060 }, "thread_token_usage": { "input_tokens": 1328758, "cached_input_tokens": 1276032, "cache_write_input_tokens": 0, "output_tokens": 21302, "reasoning_output_tokens": 8334, "total_tokens": 1350060 } } }, { "kind": "context", "timestamp": "2026-09-23T05:34:08.788Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-923f6b89-873c-42d7-8269-0ac0c4ac16dd", "process_id": "11274", "command": [ "/bin/sh", "-lc", "tail /app/solution/final_strong.log; tail /app/solution/strong_sweep.log; tail /app/solution/outside.log; tail /app/solution/session_knn.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/final_strong.log", "name": "final_strong.log", "path": "/app/solution/final_strong.log" }, { "type": "read", "cmd": "tail /app/solution/strong_sweep.log", "name": "strong_sweep.log", "path": "/app/solution/strong_sweep.log" }, { "type": "read", "cmd": "tail /app/solution/outside.log", "name": "outside.log", "path": "/app/solution/outside.log" }, { "type": "read", "cmd": "tail /app/solution/session_knn.log", "name": "session_knn.log", "path": "/app/solution/session_knn.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test_strong_1000.0_0.0_0.0 time 62.70835757255554\ntest_strong_1000.0_0.0_0.0 NDCG 0.0\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\nvalidation_strong_100_1_0.25 time 53.56865644454956\nvalidation_strong_100_1_0.25 NDCG 0.028268317226975957\nsim built 44.459381103515625\nlow 0\nhigh 0\nmax 5668\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "test_strong_1000.0_0.0_0.0 time 62.70835757255554\ntest_strong_1000.0_0.0_0.0 NDCG 0.0\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\nvalidation_strong_100_1_0.25 time 53.56865644454956\nvalidation_strong_100_1_0.25 NDCG 0.028268317226975957\nsim built 44.459381103515625\nlow 0\nhigh 0\nmax 5668\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2790 }, "formatted_output": "test_strong_1000.0_0.0_0.0 time 62.70835757255554\ntest_strong_1000.0_0.0_0.0 NDCG 0.0\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\nvalidation_strong_100_1_0.25 time 53.56865644454956\nvalidation_strong_100_1_0.25 NDCG 0.028268317226975957\nsim built 44.459381103515625\nlow 0\nhigh 0\nmax 5668\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-23T05:34:16.207Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:16.207Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:34:19.138Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-317d9792-d1cc-49c7-86b1-07225ba0d66f", "process_id": "65452", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py validation 1000 1 > /app/solution/outside.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py validation 1000 1 > /app/solution/outside.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 62, "nanos": 558098309 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:34:20.041Z", "callId": "call_QYUBkNpl6hlGPwFbdZY9qLD0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/refit_bases.py <<'PY'\\nfrom model import ease\\nfrom asymmetric import fit\\nfor variant,reg in [('strong',300.0),('recent',300),('hybrid',1000)]:ease('test',variant,reg)\\nfit('test',300.0,2.0,'organic')\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_bases.py > /app/solution/refit_bases.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:34:21.224Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"c3242b\",\"wall_time_seconds\":1.000571231,\"session_id\":8287,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:34:21 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:20.120Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab364d129a887d28d9c5abc9fd2b498", "usage": { "input_tokens": 53074, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 214, "reasoning_output_tokens": 61, "total_tokens": 53288 }, "turn_token_usage": { "input_tokens": 1381832, "cached_input_tokens": 1326976, "cache_write_input_tokens": 0, "output_tokens": 21516, "reasoning_output_tokens": 8395, "total_tokens": 1403348 }, "thread_token_usage": { "input_tokens": 1381832, "cached_input_tokens": 1326976, "cache_write_input_tokens": 0, "output_tokens": 21516, "reasoning_output_tokens": 8395, "total_tokens": 1403348 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-23T05:34:26.024Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:34:26.024Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:34:35.594Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6a09ef3b-4294-49bd-81a9-9027030d57cf", "process_id": "85160", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py validation 0.6 > /app/solution/session_knn.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py validation 0.6 > /app/solution/session_knn.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 77, "nanos": 758197544 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:34:47.004Z", "callId": "call_7xCk7JnpBJcnY6PmqvwIQyI9", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/extra_features.py <<'PY'\\nfrom model import *\\ndef extra(stage):\\n uf=[];itf=[];cut=24786800 if stage=='validation' else 25393400\\n for family in ['likes','dislikes','unlikes','undislikes']:\\n d=pd.concat([pd.read_parquet(D/f'{p}_{family}.parquet') for p in (['train'] if stage=='validation' else ['train','val'])]);r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);age=(cut-d.timestamp.to_numpy(dtype=np.float64))/86400\\n for decay in [7,30,90,10000]:\\n for organic in ([0,1] if family=='likes' else [None]):\\n v=np.exp(-age/decay)*(1 if organic is None else (d.is_organic.values==organic));ok=c>=0;itf.append(np.bincount(c[ok],weights=v[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],weights=v[ok],minlength=len(targets)))\\n for state in ['liked','disliked']:\\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state==state];r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=c>=0;itf.append(np.bincount(c[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],minlength=len(targets)))\\n return np.log1p(np.stack(itf,axis=1)).astype(np.float32),np.log1p(np.stack(uf,axis=1)).astype(np.float32)\\ndef extend(stage):\\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];itf,uf=extra(stage);x=np.lib.format.open_memmap(P/f'{stage}_features_extended.npy',mode='w+',dtype=np.float32,shape=(len(targets),pool.shape[1],old.shape[2]+itf.shape[1]+uf.shape[1]))\\n for st in range(0,len(targets),64):\\n en=min(st+64,len(targets));x[st:en]=np.concatenate([old[st:en],itf[pool[st:en]],np.broadcast_to(uf[st:en,None,:],(en-st,pool.shape[1],uf.shape[1]))],axis=2)\\n x.flush();return x\\nif __name__=='__main__':extend(sys.argv[1])\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_features.py validation > /app/solution/extra_features.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/outside.log; tail /app/solution/session_knn.log; tail /app/solution/tune_rerank.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:34:48.249Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a0589e\",\"wall_time_seconds\":1.000955189,\"session_id\":97929,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"87dbc2\",\"wall_time_seconds\":0.00000536,\"exit_code\":0,\"original_token_count\":68,\"output\":\"validation_outside_1000.0_1.0 time 60.25457143783569\\nvalidation_outside_1000.0_1.0 NDCG 0.032097069109203194\\nsim built 44.459381103515625\\nvalidation_session_0.6_short NDCG 0.021765337430107848\\nvalidation_session_0.6_strong NDCG 0.019545724441191823\\ndone 76.80051922798157\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:34:47.101Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab364de273487d29a2c08dc5521a8a8", "usage": { "input_tokens": 53364, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 778, "reasoning_output_tokens": 70, "total_tokens": 54142 }, "turn_token_usage": { "input_tokens": 1435196, "cached_input_tokens": 1379840, "cache_write_input_tokens": 0, "output_tokens": 22294, "reasoning_output_tokens": 8465, "total_tokens": 1457490 }, "thread_token_usage": { "input_tokens": 1435196, "cached_input_tokens": 1379840, "cache_write_input_tokens": 0, "output_tokens": 22294, "reasoning_output_tokens": 8465, "total_tokens": 1457490 } } }, { "kind": "context", "timestamp": "2026-09-23T05:34:48.237Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86bdf9b9-ae54-4100-8ffc-bb7ee368720b", "process_id": "3317", "command": [ "/bin/sh", "-lc", "tail /app/solution/outside.log; tail /app/solution/session_knn.log; tail /app/solution/tune_rerank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/outside.log", "name": "outside.log", "path": "/app/solution/outside.log" }, { "type": "read", "cmd": "tail /app/solution/session_knn.log", "name": "session_knn.log", "path": "/app/solution/session_knn.log" }, { "type": "read", "cmd": "tail /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_outside_1000.0_1.0 time 60.25457143783569\nvalidation_outside_1000.0_1.0 NDCG 0.032097069109203194\nsim built 44.459381103515625\nvalidation_session_0.6_short NDCG 0.021765337430107848\nvalidation_session_0.6_strong NDCG 0.019545724441191823\ndone 76.80051922798157\n", "stderr": "", "aggregated_output": "validation_outside_1000.0_1.0 time 60.25457143783569\nvalidation_outside_1000.0_1.0 NDCG 0.032097069109203194\nsim built 44.459381103515625\nvalidation_session_0.6_short NDCG 0.021765337430107848\nvalidation_session_0.6_strong NDCG 0.019545724441191823\ndone 76.80051922798157\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5360 }, "formatted_output": "validation_outside_1000.0_1.0 time 60.25457143783569\nvalidation_outside_1000.0_1.0 NDCG 0.032097069109203194\nsim built 44.459381103515625\nvalidation_session_0.6_short NDCG 0.021765337430107848\nvalidation_session_0.6_strong NDCG 0.019545724441191823\ndone 76.80051922798157\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:35:04.312Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dda586a7-8993-4ae2-b094-e0c7a95c720d", "process_id": "97929", "command": [ "/bin/sh", "-lc", "cat > /app/solution/extra_features.py <<'PY'\nfrom model import *\ndef extra(stage):\n uf=[];itf=[];cut=24786800 if stage=='validation' else 25393400\n for family in ['likes','dislikes','unlikes','undislikes']:\n d=pd.concat([pd.read_parquet(D/f'{p}_{family}.parquet') for p in (['train'] if stage=='validation' else ['train','val'])]);r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);age=(cut-d.timestamp.to_numpy(dtype=np.float64))/86400\n for decay in [7,30,90,10000]:\n for organic in ([0,1] if family=='likes' else [None]):\n v=np.exp(-age/decay)*(1 if organic is None else (d.is_organic.values==organic));ok=c>=0;itf.append(np.bincount(c[ok],weights=v[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],weights=v[ok],minlength=len(targets)))\n for state in ['liked','disliked']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state==state];r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=c>=0;itf.append(np.bincount(c[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],minlength=len(targets)))\n return np.log1p(np.stack(itf,axis=1)).astype(np.float32),np.log1p(np.stack(uf,axis=1)).astype(np.float32)\ndef extend(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];itf,uf=extra(stage);x=np.lib.format.open_memmap(P/f'{stage}_features_extended.npy',mode='w+',dtype=np.float32,shape=(len(targets),pool.shape[1],old.shape[2]+itf.shape[1]+uf.shape[1]))\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));x[st:en]=np.concatenate([old[st:en],itf[pool[st:en]],np.broadcast_to(uf[st:en,None,:],(en-st,pool.shape[1],uf.shape[1]))],axis=2)\n x.flush();return x\nif __name__=='__main__':extend(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_features.py validation > /app/solution/extra_features.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/extra_features.py <<'PY'\nfrom model import *\ndef extra(stage):\n uf=[];itf=[];cut=24786800 if stage=='validation' else 25393400\n for family in ['likes','dislikes','unlikes','undislikes']:\n d=pd.concat([pd.read_parquet(D/f'{p}_{family}.parquet') for p in (['train'] if stage=='validation' else ['train','val'])]);r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);age=(cut-d.timestamp.to_numpy(dtype=np.float64))/86400\n for decay in [7,30,90,10000]:\n for organic in ([0,1] if family=='likes' else [None]):\n v=np.exp(-age/decay)*(1 if organic is None else (d.is_organic.values==organic));ok=c>=0;itf.append(np.bincount(c[ok],weights=v[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],weights=v[ok],minlength=len(targets)))\n for state in ['liked','disliked']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state==state];r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=c>=0;itf.append(np.bincount(c[ok],minlength=M));ok=r>=0;uf.append(np.bincount(r[ok],minlength=len(targets)))\n return np.log1p(np.stack(itf,axis=1)).astype(np.float32),np.log1p(np.stack(uf,axis=1)).astype(np.float32)\ndef extend(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];itf,uf=extra(stage);x=np.lib.format.open_memmap(P/f'{stage}_features_extended.npy',mode='w+',dtype=np.float32,shape=(len(targets),pool.shape[1],old.shape[2]+itf.shape[1]+uf.shape[1]))\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));x[st:en]=np.concatenate([old[st:en],itf[pool[st:en]],np.broadcast_to(uf[st:en,None,:],(en-st,pool.shape[1],uf.shape[1]))],axis=2)\n x.flush();return x\nif __name__=='__main__':extend(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_features.py validation > /app/solution/extra_features.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 133869526 }, "formatted_output": "" } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-23T05:35:10.918Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:10.918Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:35:11.717Z", "callId": "call_lzxwuaoifzN0C1FKLntkImME", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/tune_rerank.log; tail /app/solution/refit_bases.log; cat /app/solution/extra_features.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:35:11.785Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"140604\",\"wall_time_seconds\":0.0000049,\"exit_code\":0,\"original_token_count\":57,\"output\":\"matrix strong (9238, 30000) 5491168 time 0.48132848739624023\\nkernel 8.564818143844604\\nchol 9.943581819534302\\nsolved 46.84225296974182\\ntest_ease_strong_300.0 NDCG 0.0\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\n\"}\n{\"current_time\":\"2026-09-23 05:35:11 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:35:11.764Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5390cef-237a-4313-88dc-1e73c677511d", "process_id": "57679", "command": [ "/bin/sh", "-lc", "cat /app/solution/tune_rerank.log; tail /app/solution/refit_bases.log; cat /app/solution/extra_features.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" }, { "type": "read", "cmd": "tail /app/solution/refit_bases.log", "name": "refit_bases.log", "path": "/app/solution/refit_bases.log" }, { "type": "read", "cmd": "cat /app/solution/extra_features.log", "name": "extra_features.log", "path": "/app/solution/extra_features.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\n", "stderr": "", "aggregated_output": "matrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4900 }, "formatted_output": "matrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:35:11.783Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": 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"input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\ns=Path('/app/solution/tune_rerank.py').read_text().replace(\\\"'validation_features.npy'\\\",\\\"'validation_features_extended.npy'\\\").replace(\\\"[(15,300,10),(31,250,10),(31,250,50),(7,300,10)]\\\",\\\"[(15,150,10),(15,300,10)]\\\").replace(\\\"f'rerank_tuned_\\\", \\\"f'rerank_extended_\\\");Path('/app/solution/tune_extended.py').write_text(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_extended.py > /app/solution/tune_extended.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:35:24.597Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d5eeac\",\"wall_time_seconds\":1.001364127,\"session_id\":77284,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:35:23.543Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36510049887d2bb9f6676c5f16d32", "usage": { "input_tokens": 55094, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 286, "reasoning_output_tokens": 89, "total_tokens": 55380 }, "turn_token_usage": { "input_tokens": 1544632, "cached_input_tokens": 1487232, "cache_write_input_tokens": 0, "output_tokens": 23161, "reasoning_output_tokens": 9066, "total_tokens": 1567793 }, "thread_token_usage": { "input_tokens": 1544632, "cached_input_tokens": 1487232, "cache_write_input_tokens": 0, "output_tokens": 23161, "reasoning_output_tokens": 9066, "total_tokens": 1567793 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-23T05:35:27.599Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:27.599Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:35:29.098Z", "text": "Session-based neighbors and outside-catalog history are weaker than the learned reranker on their own, though they may add useful features. The base models are now being refitted on train plus validation history for the final cutoff.\n" } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-23T05:35:36.310Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:36.310Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:35:38.104Z", "callId": "call_mO2GgcAlMNcoXmGfw9w1VlzE", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1800}));\n" }, "result": { "timestamp": "2026-09-23T05:35:38.216Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"440294\",\"wall_time_seconds\":0.00000319,\"exit_code\":0,\"original_token_count\":103,\"output\":\"config 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nmatrix strong (9238, 30000) 5491168 time 0.48132848739624023\\nkernel 8.564818143844604\\nchol 9.943581819534302\\nsolved 46.84225296974182\\ntest_ease_strong_300.0 NDCG 0.0\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nlow 0\\nhigh 0\\nmax 7799\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:35:38.153Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b04d1fe1-0cc1-4a3d-8e14-661c24c0e684", "process_id": "68870", "command": [ "/bin/sh", "-lc", "tail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" }, { "type": "read", "cmd": "tail /app/solution/tune_extended.log", "name": "tune_extended.log", "path": "/app/solution/tune_extended.log" }, { "type": "read", "cmd": "tail /app/solution/refit_bases.log", "name": "refit_bases.log", "path": "/app/solution/refit_bases.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nmatrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nlow 0\nhigh 0\nmax 7799\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nmatrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nlow 0\nhigh 0\nmax 7799\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3190 }, "formatted_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nmatrix strong (9238, 30000) 5491168 time 0.48132848739624023\nkernel 8.564818143844604\nchol 9.943581819534302\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nlow 0\nhigh 0\nmax 7799\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:35:38.212Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3651cc49c87d28e5e5f305a313417", "usage": { "input_tokens": 55438, "cached_input_tokens": 54912, "cache_write_input_tokens": 0, "output_tokens": 379, "reasoning_output_tokens": 258, "total_tokens": 55817 }, "turn_token_usage": { "input_tokens": 1600070, "cached_input_tokens": 1542144, "cache_write_input_tokens": 0, "output_tokens": 23540, "reasoning_output_tokens": 9324, "total_tokens": 1623610 }, "thread_token_usage": { "input_tokens": 1600070, "cached_input_tokens": 1542144, "cache_write_input_tokens": 0, "output_tokens": 23540, "reasoning_output_tokens": 9324, "total_tokens": 1623610 } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-23T05:35:59.909Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:35:59.909Z", "text": "" } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-23T05:36:03.346Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:03.346Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:25.071Z", "callId": "call_9CP1Tm7DDKMoz5xLl6aR5X0I", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/finalize_reranker.py <<'PY'\\nfrom rerank import *\\nfrom extra_features import extend\\n\\ndef export(pred,pool,path):\\n ix=np.argsort(-pred,axis=1)[:,:100];ranked=np.take_along_axis(pool,ix,axis=1);frame=pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranked.ravel()],'rank':np.tile(np.arange(1,101),len(targets))});frame.to_parquet(path,index=False)\\n\\ndef fit_full(kind='base',leaves=15,iters=150,l2=10):\\n e=Eval();X=np.load(P/('validation_features_extended.npy' if kind=='extended' else 'validation_features.npy'),mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1]\\n config=dict(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42)\\n pred=np.zeros(Y.shape,np.float32)\\n for trainfold in [fold,~fold]:\\n sample=trainfold&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[~trainfold]=model.predict(np.asarray(X[~trainfold]).reshape(-1,X.shape[-1])).reshape(-1,K)\\n print(kind,'OOF fold score',metric(pred,Y,e,~trainfold),flush=True)\\n print(kind,'OOF score',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);export(pred,pool,P/f'validation_rerank_{kind}.parquet');np.save(P/f'oof_{kind}.npy',pred)\\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/f'final_reranker_{kind}.joblib');print('full model fitted',kind,flush=True)\\n json.dump(dict(kind=kind,names=z['names'].tolist(),config=config),open(P/f'final_config_{kind}.json','w'))\\n\\ndef predict(kind='base'):\\n config=json.load(open(P/f'final_config_{kind}.json'));X,pool,Y,e=features('test',config['names']);\\n if kind=='extended':X=extend('test')\\n model=joblib.load(P/f'final_reranker_{kind}.joblib');pred=np.empty(pool.shape,np.float32)\\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\\n export(pred,pool,P/f'submission_rerank_{kind}.parquet');np.save(P/f'test_predictions_{kind}.npy',pred);print('prediction saved',kind,flush=True)\\nif __name__=='__main__':\\n if sys.argv[1]=='fit':fit_full(sys.argv[2],int(sys.argv[3]),int(sys.argv[4]),float(sys.argv[5]))\\n else:predict(sys.argv[2])\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize_reranker.py fit base 15 150 10 > /app/solution/final_rerank_base.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:36:26.424Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"59cf72\",\"wall_time_seconds\":1.001593349,\"session_id\":53039,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"c8522c\",\"wall_time_seconds\":0.00000614,\"exit_code\":0,\"original_token_count\":99,\"output\":\"config 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nsolved 46.84225296974182\\ntest_ease_strong_300.0 NDCG 0.0\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\n\"}\n{\"current_time\":\"2026-09-23 05:36:26 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:25.201Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3652a690087d2835deaef148db498", "usage": { "input_tokens": 56057, "cached_input_tokens": 55296, "cache_write_input_tokens": 0, "output_tokens": 1501, "reasoning_output_tokens": 566, "total_tokens": 57558 }, "turn_token_usage": { "input_tokens": 1656127, "cached_input_tokens": 1597440, "cache_write_input_tokens": 0, "output_tokens": 25041, "reasoning_output_tokens": 9890, "total_tokens": 1681168 }, "thread_token_usage": { "input_tokens": 1656127, "cached_input_tokens": 1597440, "cache_write_input_tokens": 0, "output_tokens": 25041, "reasoning_output_tokens": 9890, "total_tokens": 1681168 } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:26.346Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6492d7a0-1ddb-414f-b7b4-11c8f781541b", "process_id": "41045", "command": [ "/bin/sh", "-lc", "tail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" }, { "type": "read", "cmd": "tail /app/solution/tune_extended.log", "name": "tune_extended.log", "path": "/app/solution/tune_extended.log" }, { "type": "read", "cmd": "tail /app/solution/refit_bases.log", "name": "refit_bases.log", "path": "/app/solution/refit_bases.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n", "stderr": "", "aggregated_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6140 }, "formatted_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-23T05:36:35.533Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:35.533Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:36:37.527Z", "callId": "call_Dqo9CH0Ri1qSTdE3HwjBBJk5", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -lh /app/solution/validation_features*; cat /sys/fs/cgroup/memory.current; tail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-23T05:36:37.707Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"324807\",\"wall_time_seconds\":0.00000488,\"exit_code\":0,\"original_token_count\":46,\"output\":\"-rw-r--r-- 1 agent agent 613M Sep 23 05:31 /app/solution/validation_features.npy\\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:34 /app/solution/validation_features_extended.npy\\n8555732992\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:36:37.627Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3655aabe087d285d57f37d742ba82", "usage": { "input_tokens": 57838, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 70, "total_tokens": 57980 }, "turn_token_usage": { "input_tokens": 1713965, "cached_input_tokens": 1653376, "cache_write_input_tokens": 0, "output_tokens": 25183, "reasoning_output_tokens": 9960, "total_tokens": 1739148 }, "thread_token_usage": { "input_tokens": 1713965, "cached_input_tokens": 1653376, "cache_write_input_tokens": 0, "output_tokens": 25183, "reasoning_output_tokens": 9960, "total_tokens": 1739148 } } }, { "kind": "context", "timestamp": "2026-09-23T05:36:37.696Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d2903847-a5d1-4aaf-9515-3ea6bb1a913e", "process_id": "29529", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/validation_features*; cat /sys/fs/cgroup/memory.current; tail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/validation_features*; cat /sys/fs/cgroup/memory.current; tail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 613M Sep 23 05:31 /app/solution/validation_features.npy\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:34 /app/solution/validation_features_extended.npy\n8555732992\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 613M Sep 23 05:31 /app/solution/validation_features.npy\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:34 /app/solution/validation_features_extended.npy\n8555732992\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4880 }, "formatted_output": "-rw-r--r-- 1 agent agent 613M Sep 23 05:31 /app/solution/validation_features.npy\n-rw-r--r-- 1 agent agent 1.3G Sep 23 05:34 /app/solution/validation_features_extended.npy\n8555732992\n" } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-23T05:36:59.414Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:36:59.414Z", "text": "" } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-23T05:37:14.738Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:14.738Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:37:22.094Z", "callId": "call_1ci8RviqyKaFAkwq5mIhuCLD", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/rerank.py');s=p.read_text().replace(\\\"e=Eval(stage);models=\\\", \\\"suffix='' if K==500 else str(K)\\\\n e=Eval(stage);models=\\\").replace(\\\"f'{stage}_features.npy'\\\", \\\"f'{stage}_features{suffix}.npy'\\\").replace(\\\"f'{stage}_pool.npz'\\\", \\\"f'{stage}_pool{suffix}.npz'\\\");p.write_text(s)\\nPY\\ncat > /app/solution/pool_test.py <<'PY'\\nfrom rerank import *\\nnames=np.load(P/'validation_pool.npz')['names'].tolist();X,pool,Y,e=features('validation',names,1000);hold=~(np.random.default_rng(2026).random(len(targets))<.5);model=joblib.load(P/'rerank_poisson_holdout.joblib');pred=np.zeros(Y.shape,np.float32)\\nfor st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,Y.shape[1])\\nprint('1000-pool holdout',metric(pred,Y,e,hold),flush=True);np.save(P/'validation_predictions1000.npy',pred)\\nPY\\ntail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log; tail /app/solution/final_rerank_base.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:37:22.743Z", "content": "Script completed\nWall time 0.6 seconds\nOutput:\n\n{\"chunk_id\":\"a4e99b\",\"wall_time_seconds\":0.335263709,\"exit_code\":0,\"original_token_count\":116,\"output\":\"config 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\\nsolved 46.84225296974182\\ntest_ease_strong_300.0 NDCG 0.0\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\n\"}\n{\"current_time\":\"2026-09-23 05:37:22 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:37:22.222Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36565f60887d2948911af1bb3586f", "usage": { "input_tokens": 58100, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 1358, "reasoning_output_tokens": 971, "total_tokens": 59458 }, "turn_token_usage": { "input_tokens": 1772065, "cached_input_tokens": 1710976, "cache_write_input_tokens": 0, "output_tokens": 26541, "reasoning_output_tokens": 10931, "total_tokens": 1798606 }, "thread_token_usage": { "input_tokens": 1772065, "cached_input_tokens": 1710976, "cache_write_input_tokens": 0, "output_tokens": 26541, "reasoning_output_tokens": 10931, "total_tokens": 1798606 } } }, { "kind": "context", "timestamp": "2026-09-23T05:37:22.641Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3c4d86e4-09cf-4404-a420-84a266ab2887", "process_id": "41936", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text().replace(\"e=Eval(stage);models=\", \"suffix='' if K==500 else str(K)\\n e=Eval(stage);models=\").replace(\"f'{stage}_features.npy'\", \"f'{stage}_features{suffix}.npy'\").replace(\"f'{stage}_pool.npz'\", \"f'{stage}_pool{suffix}.npz'\");p.write_text(s)\nPY\ncat > /app/solution/pool_test.py <<'PY'\nfrom rerank import *\nnames=np.load(P/'validation_pool.npz')['names'].tolist();X,pool,Y,e=features('validation',names,1000);hold=~(np.random.default_rng(2026).random(len(targets))<.5);model=joblib.load(P/'rerank_poisson_holdout.joblib');pred=np.zeros(Y.shape,np.float32)\nfor st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,Y.shape[1])\nprint('1000-pool holdout',metric(pred,Y,e,hold),flush=True);np.save(P/'validation_predictions1000.npy',pred)\nPY\ntail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log; tail /app/solution/final_rerank_base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rerank.py');s=p.read_text().replace(\"e=Eval(stage);models=\", \"suffix='' if K==500 else str(K)\\n e=Eval(stage);models=\").replace(\"f'{stage}_features.npy'\", \"f'{stage}_features{suffix}.npy'\").replace(\"f'{stage}_pool.npz'\", \"f'{stage}_pool{suffix}.npz'\");p.write_text(s)\nPY\ncat > /app/solution/pool_test.py <<'PY'\nfrom rerank import *\nnames=np.load(P/'validation_pool.npz')['names'].tolist();X,pool,Y,e=features('validation',names,1000);hold=~(np.random.default_rng(2026).random(len(targets))<.5);model=joblib.load(P/'rerank_poisson_holdout.joblib');pred=np.zeros(Y.shape,np.float32)\nfor st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,Y.shape[1])\nprint('1000-pool holdout',metric(pred,Y,e,hold),flush=True);np.save(P/'validation_predictions1000.npy',pred)\nPY\ntail /app/solution/tune_rerank.log; tail /app/solution/tune_extended.log; tail /app/solution/refit_bases.log; tail /app/solution/final_rerank_base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n", "stderr": "", "aggregated_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 335574155 }, "formatted_output": "config 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nsolved 46.84225296974182\ntest_ease_strong_300.0 NDCG 0.0\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\n" } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-23T05:37:32.008Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:32.008Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:37:32.817Z", "text": "More complex trees have not improved the held-out score so far, so the smaller reranker remains the leading choice. I’m also running a second user fold to check its performance across all target users.\n" } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-23T05:37:38.638Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:37:38.638Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:37:43.744Z", "callId": "call_q8wrL0MQP6ujhBMez7VWvRgF", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\\nimport joblib,numpy as np\\nm=joblib.load('/app/solution/rerank_poisson_holdout.joblib');gain=np.zeros(38)\\nfor tree in m._predictors:\\n n=tree[0].nodes\\n for f,g in zip(n['feature_idx'],n['gain']):\\n if g>0:gain[f]+=g\\nprint(sorted(enumerate(gain),key=lambda a:-a[1])[:18])\\nPY\\ntail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:37:48.640Z", "content": "Script completed\nWall time 4.8 seconds\nOutput:\n\n{\"chunk_id\":\"f8961b\",\"wall_time_seconds\":4.626620121,\"exit_code\":0,\"original_token_count\":261,\"output\":\"[(0, np.float64(134697.2671385402)), (6, np.float64(33197.630190711694)), (34, np.float64(30282.270894925256)), (20, np.float64(27103.005100455182)), (35, np.float64(26023.843583523394)), (21, np.float64(18420.257599302684)), (36, np.float64(15957.249507392115)), (37, np.float64(13999.619595031092)), (22, np.float64(13502.379931977548)), (31, np.float64(12583.023705082853)), (25, np.float64(11426.043434884363)), (1, np.float64(11393.164714454528)), (28, np.float64(10270.85774887428)), (32, np.float64(10112.618080137365)), (30, np.float64(9672.734152392142)), (26, np.float64(9620.17563707346)), (3, np.float64(8659.34582604769)), (33, np.float64(8499.429759261717))]\\nbase OOF fold score 0.055597262296575266\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\nsolved 87.70576524734497\\ntest_ease_hybrid_1000 NDCG 0.0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:37:43.829Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3659300ec87d2ac0c96ee4f73aeb6", "usage": { "input_tokens": 59732, "cached_input_tokens": 57984, "cache_write_input_tokens": 0, "output_tokens": 599, "reasoning_output_tokens": 367, "total_tokens": 60331 }, "turn_token_usage": { "input_tokens": 1831797, "cached_input_tokens": 1768960, "cache_write_input_tokens": 0, "output_tokens": 27140, "reasoning_output_tokens": 11298, "total_tokens": 1858937 }, "thread_token_usage": { "input_tokens": 1831797, "cached_input_tokens": 1768960, "cache_write_input_tokens": 0, "output_tokens": 27140, "reasoning_output_tokens": 11298, "total_tokens": 1858937 } } }, { "kind": "context", "timestamp": "2026-09-23T05:37:48.627Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ffc6af2-cd6c-498f-a18e-71e4592fb432", "process_id": "59697", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport joblib,numpy as np\nm=joblib.load('/app/solution/rerank_poisson_holdout.joblib');gain=np.zeros(38)\nfor tree in m._predictors:\n n=tree[0].nodes\n for f,g in zip(n['feature_idx'],n['gain']):\n if g>0:gain[f]+=g\nprint(sorted(enumerate(gain),key=lambda a:-a[1])[:18])\nPY\ntail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport joblib,numpy as np\nm=joblib.load('/app/solution/rerank_poisson_holdout.joblib');gain=np.zeros(38)\nfor tree in m._predictors:\n n=tree[0].nodes\n for f,g in zip(n['feature_idx'],n['gain']):\n if g>0:gain[f]+=g\nprint(sorted(enumerate(gain),key=lambda a:-a[1])[:18])\nPY\ntail /app/solution/tune_extended.log; tail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[(0, np.float64(134697.2671385402)), (6, np.float64(33197.630190711694)), (34, np.float64(30282.270894925256)), (20, np.float64(27103.005100455182)), (35, np.float64(26023.843583523394)), (21, np.float64(18420.257599302684)), (36, np.float64(15957.249507392115)), (37, np.float64(13999.619595031092)), (22, np.float64(13502.379931977548)), (31, np.float64(12583.023705082853)), (25, np.float64(11426.043434884363)), (1, np.float64(11393.164714454528)), (28, np.float64(10270.85774887428)), (32, np.float64(10112.618080137365)), (30, np.float64(9672.734152392142)), (26, np.float64(9620.17563707346)), (3, np.float64(8659.34582604769)), (33, np.float64(8499.429759261717))]\nbase OOF fold score 0.055597262296575266\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\n", "stderr": "", "aggregated_output": "[(0, np.float64(134697.2671385402)), (6, np.float64(33197.630190711694)), (34, np.float64(30282.270894925256)), (20, np.float64(27103.005100455182)), (35, np.float64(26023.843583523394)), (21, np.float64(18420.257599302684)), (36, np.float64(15957.249507392115)), (37, np.float64(13999.619595031092)), (22, np.float64(13502.379931977548)), (31, np.float64(12583.023705082853)), (25, np.float64(11426.043434884363)), (1, np.float64(11393.164714454528)), (28, np.float64(10270.85774887428)), (32, np.float64(10112.618080137365)), (30, np.float64(9672.734152392142)), (26, np.float64(9620.17563707346)), (3, np.float64(8659.34582604769)), (33, np.float64(8499.429759261717))]\nbase OOF fold score 0.055597262296575266\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 626609111 }, "formatted_output": "[(0, np.float64(134697.2671385402)), (6, np.float64(33197.630190711694)), (34, np.float64(30282.270894925256)), (20, np.float64(27103.005100455182)), (35, np.float64(26023.843583523394)), (21, np.float64(18420.257599302684)), (36, np.float64(15957.249507392115)), (37, np.float64(13999.619595031092)), (22, np.float64(13502.379931977548)), (31, np.float64(12583.023705082853)), (25, np.float64(11426.043434884363)), (1, np.float64(11393.164714454528)), (28, np.float64(10270.85774887428)), (32, np.float64(10112.618080137365)), (30, np.float64(9672.734152392142)), (26, np.float64(9620.17563707346)), (3, np.float64(8659.34582604769)), (33, np.float64(8499.429759261717))]\nbase OOF fold score 0.055597262296575266\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-23T05:38:12.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:12.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:13.494Z", "callId": "call_tBYcWLHNo26QB6TPCt1rtOF4", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/tune_extended.log; tail /app/solution/tune_rerank.log; tail /app/solution/final_rerank_base.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:38:13.640Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"cc3e08\",\"wall_time_seconds\":0.00000483,\"exit_code\":0,\"original_token_count\":79,\"output\":\"config 15 150 10 score 0.05540984083389711 time 156.41917371749878\\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\\nbase OOF fold score 0.055597262296575266\\nlow 0\\nhigh 0\\nmax 9932\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 0\\n\"}\n{\"current_time\":\"2026-09-23 05:38:13 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:38:13.551Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-336c5d86-4fff-490f-9550-82d1def423a2", "process_id": "65445", "command": [ "/bin/sh", "-lc", "tail /app/solution/tune_extended.log; tail /app/solution/tune_rerank.log; tail /app/solution/final_rerank_base.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/tune_extended.log", "name": "tune_extended.log", "path": "/app/solution/tune_extended.log" }, { "type": "read", "cmd": "tail /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" }, { "type": "read", "cmd": "tail /app/solution/final_rerank_base.log", "name": "final_rerank_base.log", "path": "/app/solution/final_rerank_base.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "config 15 150 10 score 0.05540984083389711 time 156.41917371749878\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nbase OOF fold score 0.055597262296575266\nlow 0\nhigh 0\nmax 9932\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "config 15 150 10 score 0.05540984083389711 time 156.41917371749878\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nbase OOF fold score 0.055597262296575266\nlow 0\nhigh 0\nmax 9932\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4830 }, "formatted_output": "config 15 150 10 score 0.05540984083389711 time 156.41917371749878\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nbase OOF fold score 0.055597262296575266\nlow 0\nhigh 0\nmax 9932\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:13.609Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab365ace6c487d2931b329c837e842d", "usage": { "input_tokens": 60818, "cached_input_tokens": 59520, "cache_write_input_tokens": 0, "output_tokens": 597, "reasoning_output_tokens": 516, "total_tokens": 61415 }, "turn_token_usage": { "input_tokens": 1892615, "cached_input_tokens": 1828480, "cache_write_input_tokens": 0, "output_tokens": 27737, "reasoning_output_tokens": 11814, "total_tokens": 1920352 }, "thread_token_usage": { "input_tokens": 1892615, "cached_input_tokens": 1828480, "cache_write_input_tokens": 0, "output_tokens": 27737, "reasoning_output_tokens": 11814, "total_tokens": 1920352 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-23T05:38:32.015Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:32.015Z", "text": "" } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-23T05:38:35.897Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:38:35.897Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:38:58.294Z", "callId": "call_GXfuLnlI4LKzrm0KjB3AqmGm", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,signal\\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\\n try:\\n a=p.read_bytes().split(b'\\\\0')\\n if len(a)>1 and a[1]==b'/app/solution/tune_extended.py':os.kill(int(p.parent.name),signal.SIGTERM)\\n except (OSError,ValueError):pass\\nPY\\ncat > /app/solution/augment.py <<'PY'\\nfrom rerank import *\\nextra_names=['outside_1000.0_1.0','session_0.6_short','session_0.6_strong']\\ndef augment(stage):\\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];x=np.lib.format.open_memmap(P/f'{stage}_features_augmented.npy',mode='w+',dtype=np.float32,shape=(*old.shape[:2],old.shape[2]+len(extra_names)*3));e=Eval(stage);mods=[np.load(P/f'{stage}_{name}.npy',mmap_mode='r') for name in extra_names]\\n for st in range(0,len(targets),64):\\n en=min(st+64,len(targets));f=[];mask=e.mask(st,en)\\n for mod in mods:\\n s=np.array(mod[st:en]);s[~mask]=-np.inf;v=np.take_along_axis(s,pool[st:en],axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\\n x[st:en]=np.concatenate([old[st:en],np.stack(f,axis=2)],axis=2)\\n x.flush();return x\\nif __name__=='__main__':\\n X=augment('validation');e=Eval();z=np.load(P/'validation_pool.npz');Y=z['Y'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;K=X.shape[1];model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);print('augmented score',metric(pred,Y,e,~fold),flush=True);joblib.dump(model,P/'rerank_augmented_holdout.joblib');np.save(P/'augmented_holdout_preds.npy',pred)\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/augment.py > /app/solution/augment.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/refit_bases.log; tail /app/solution/final_rerank_base.log; tail /app/solution/tune_rerank.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:38:59.719Z", "content": "Script completed\nWall time 1.4 seconds\nOutput:\n\n{\"chunk_id\":\"581b31\",\"wall_time_seconds\":1.000334913,\"session_id\":98873,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"d11631\",\"wall_time_seconds\":0.000070222,\"exit_code\":0,\"original_token_count\":144,\"output\":\"matrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\nsolved 87.70576524734497\\ntest_ease_hybrid_1000 NDCG 0.0\\nbase OOF fold score 0.055597262296575266\\nbase OOF fold score 0.05486647535596832\\nbase OOF score 0.05524052581442583\\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:38:58.404Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab365c5dd5887d289fd8ccaf5a715e0", "usage": { "input_tokens": 61625, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 1392, "reasoning_output_tokens": 620, "total_tokens": 63017 }, "turn_token_usage": { "input_tokens": 1954240, "cached_input_tokens": 1889152, "cache_write_input_tokens": 0, "output_tokens": 29129, "reasoning_output_tokens": 12434, "total_tokens": 1983369 }, "thread_token_usage": { "input_tokens": 1954240, "cached_input_tokens": 1889152, "cache_write_input_tokens": 0, "output_tokens": 29129, "reasoning_output_tokens": 12434, "total_tokens": 1983369 } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:58.796Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2c6e7143-0be9-4d44-9cd1-61b63ef9b217", "process_id": "77284", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\ns=Path('/app/solution/tune_rerank.py').read_text().replace(\"'validation_features.npy'\",\"'validation_features_extended.npy'\").replace(\"[(15,300,10),(31,250,10),(31,250,50),(7,300,10)]\",\"[(15,150,10),(15,300,10)]\").replace(\"f'rerank_tuned_\", \"f'rerank_extended_\");Path('/app/solution/tune_extended.py').write_text(s)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_extended.py > /app/solution/tune_extended.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\ns=Path('/app/solution/tune_rerank.py').read_text().replace(\"'validation_features.npy'\",\"'validation_features_extended.npy'\").replace(\"[(15,300,10),(31,250,10),(31,250,50),(7,300,10)]\",\"[(15,150,10),(15,300,10)]\").replace(\"f'rerank_tuned_\", \"f'rerank_extended_\");Path('/app/solution/tune_extended.py').write_text(s)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_extended.py > /app/solution/tune_extended.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 215, "nanos": 200713310 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:38:59.695Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09280fb2-ec15-4c67-94ec-0e124ab6fe26", "process_id": "15235", "command": [ "/bin/sh", "-lc", "tail /app/solution/refit_bases.log; tail /app/solution/final_rerank_base.log; tail /app/solution/tune_rerank.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/refit_bases.log", "name": "refit_bases.log", "path": "/app/solution/refit_bases.log" }, { "type": "read", "cmd": "tail /app/solution/final_rerank_base.log", "name": "final_rerank_base.log", "path": "/app/solution/final_rerank_base.log" }, { "type": "read", "cmd": "tail /app/solution/tune_rerank.log", "name": "tune_rerank.log", "path": "/app/solution/tune_rerank.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\n", "stderr": "", "aggregated_output": "matrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 70222 }, "formatted_output": "matrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-23T05:39:03.940Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:03.940Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:39:07.307Z", "callId": "call_u6Ud7eUNUNM56GBHk9wO7B3S", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_rerank_base.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/pool_test.py > /app/solution/pool_test.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:39:09.707Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"89f9a6\",\"wall_time_seconds\":1.000648678,\"session_id\":13670,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"8a32e7\",\"wall_time_seconds\":1.000927288,\"session_id\":15656,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-23 05:39:09 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:07.406Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab365f40f7087d2939d845fabdfdcc4", "usage": { "input_tokens": 63348, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 157, "reasoning_output_tokens": 36, "total_tokens": 63505 }, "turn_token_usage": { "input_tokens": 2017588, "cached_input_tokens": 1950592, "cache_write_input_tokens": 0, "output_tokens": 29286, "reasoning_output_tokens": 12470, "total_tokens": 2046874 }, "thread_token_usage": { "input_tokens": 2017588, "cached_input_tokens": 1950592, "cache_write_input_tokens": 0, "output_tokens": 29286, "reasoning_output_tokens": 12470, "total_tokens": 2046874 } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:15.895Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-33b2ec19-cd05-4abe-b6a1-6d5332da2676", "process_id": "13670", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_rerank_base.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_rerank_base.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_rerank_base.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055241,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_rerank_base.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055241,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 401081903 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_rerank_base.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055241,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-23T05:39:18.980Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:18.980Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:39:20.347Z", "text": "The two-fold check reached **0.05524 NDCG@10 overall**, with similar scores in both halves. I’m finishing the full-data reranker and testing whether a larger candidate shortlist improves its top ten.\n" } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-23T05:39:37.233Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:37.233Z", "text": "" } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-23T05:39:47.996Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:47.996Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:39:53.994Z", "callId": "call_hTmyIABSvlrrFZEpfdzEjq2p", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\\nfrom model import *\\nfor stage in ['validation','test']:\\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state=='liked'];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);expected=sparse.csr_matrix((np.ones(ok.sum()),(r[ok],c[ok])),shape=(N,M));actual=sparse.load_npz(P/f'{stage}_active_likes.npz');print(stage,'like state differences',(actual[tr]-expected[tr]).nnz)\\nPY\\ntail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/pool_test.log; tail /app/solution/augment.log\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:13670,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:39:55.299Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"a5dc44\",\"wall_time_seconds\":1.001033894,\"session_id\":9829,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"c25d19\",\"wall_time_seconds\":0.00000252,\"exit_code\":0,\"original_token_count\":35,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation_rerank_base.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.055241,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:54.032Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab365fdfca487d2a33a8257bc4755ea", "usage": { "input_tokens": 63618, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 1351, "reasoning_output_tokens": 1020, "total_tokens": 64969 }, "turn_token_usage": { "input_tokens": 2081206, "cached_input_tokens": 2013824, "cache_write_input_tokens": 0, "output_tokens": 30637, "reasoning_output_tokens": 13490, "total_tokens": 2111843 }, "thread_token_usage": { "input_tokens": 2081206, "cached_input_tokens": 2013824, "cache_write_input_tokens": 0, "output_tokens": 30637, "reasoning_output_tokens": 13490, "total_tokens": 2111843 } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:56.901Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9ab2b97a-3a20-41b4-9c95-199aab5c2025", "process_id": "53039", "command": [ "/bin/sh", "-lc", "cat > /app/solution/finalize_reranker.py <<'PY'\nfrom rerank import *\nfrom extra_features import extend\n\ndef export(pred,pool,path):\n ix=np.argsort(-pred,axis=1)[:,:100];ranked=np.take_along_axis(pool,ix,axis=1);frame=pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranked.ravel()],'rank':np.tile(np.arange(1,101),len(targets))});frame.to_parquet(path,index=False)\n\ndef fit_full(kind='base',leaves=15,iters=150,l2=10):\n e=Eval();X=np.load(P/('validation_features_extended.npy' if kind=='extended' else 'validation_features.npy'),mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1]\n config=dict(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42)\n pred=np.zeros(Y.shape,np.float32)\n for trainfold in [fold,~fold]:\n sample=trainfold&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[~trainfold]=model.predict(np.asarray(X[~trainfold]).reshape(-1,X.shape[-1])).reshape(-1,K)\n print(kind,'OOF fold score',metric(pred,Y,e,~trainfold),flush=True)\n print(kind,'OOF score',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);export(pred,pool,P/f'validation_rerank_{kind}.parquet');np.save(P/f'oof_{kind}.npy',pred)\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/f'final_reranker_{kind}.joblib');print('full model fitted',kind,flush=True)\n json.dump(dict(kind=kind,names=z['names'].tolist(),config=config),open(P/f'final_config_{kind}.json','w'))\n\ndef predict(kind='base'):\n config=json.load(open(P/f'final_config_{kind}.json'));X,pool,Y,e=features('test',config['names']);\n if kind=='extended':X=extend('test')\n model=joblib.load(P/f'final_reranker_{kind}.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n export(pred,pool,P/f'submission_rerank_{kind}.parquet');np.save(P/f'test_predictions_{kind}.npy',pred);print('prediction saved',kind,flush=True)\nif __name__=='__main__':\n if sys.argv[1]=='fit':fit_full(sys.argv[2],int(sys.argv[3]),int(sys.argv[4]),float(sys.argv[5]))\n else:predict(sys.argv[2])\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize_reranker.py fit base 15 150 10 > /app/solution/final_rerank_base.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/finalize_reranker.py <<'PY'\nfrom rerank import *\nfrom extra_features import extend\n\ndef export(pred,pool,path):\n ix=np.argsort(-pred,axis=1)[:,:100];ranked=np.take_along_axis(pool,ix,axis=1);frame=pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranked.ravel()],'rank':np.tile(np.arange(1,101),len(targets))});frame.to_parquet(path,index=False)\n\ndef fit_full(kind='base',leaves=15,iters=150,l2=10):\n e=Eval();X=np.load(P/('validation_features_extended.npy' if kind=='extended' else 'validation_features.npy'),mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1]\n config=dict(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42)\n pred=np.zeros(Y.shape,np.float32)\n for trainfold in [fold,~fold]:\n sample=trainfold&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[~trainfold]=model.predict(np.asarray(X[~trainfold]).reshape(-1,X.shape[-1])).reshape(-1,K)\n print(kind,'OOF fold score',metric(pred,Y,e,~trainfold),flush=True)\n print(kind,'OOF score',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);export(pred,pool,P/f'validation_rerank_{kind}.parquet');np.save(P/f'oof_{kind}.npy',pred)\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/f'final_reranker_{kind}.joblib');print('full model fitted',kind,flush=True)\n json.dump(dict(kind=kind,names=z['names'].tolist(),config=config),open(P/f'final_config_{kind}.json','w'))\n\ndef predict(kind='base'):\n config=json.load(open(P/f'final_config_{kind}.json'));X,pool,Y,e=features('test',config['names']);\n if kind=='extended':X=extend('test')\n model=joblib.load(P/f'final_reranker_{kind}.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n export(pred,pool,P/f'submission_rerank_{kind}.parquet');np.save(P/f'test_predictions_{kind}.npy',pred);print('prediction saved',kind,flush=True)\nif __name__=='__main__':\n if sys.argv[1]=='fit':fit_full(sys.argv[2],int(sys.argv[3]),int(sys.argv[4]),float(sys.argv[5]))\n else:predict(sys.argv[2])\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize_reranker.py fit base 15 150 10 > /app/solution/final_rerank_base.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 211, "nanos": 613775397 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:39:57.275Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c112f53-b7ad-4f08-a256-367b081f42b3", "process_id": "9829", "command": [ "/bin/sh", "-lc", "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state=='liked'];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);expected=sparse.csr_matrix((np.ones(ok.sum()),(r[ok],c[ok])),shape=(N,M));actual=sparse.load_npz(P/f'{stage}_active_likes.npz');print(stage,'like state differences',(actual[tr]-expected[tr]).nnz)\nPY\ntail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/pool_test.log; tail /app/solution/augment.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state=='liked'];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);expected=sparse.csr_matrix((np.ones(ok.sum()),(r[ok],c[ok])),shape=(N,M));actual=sparse.load_npz(P/f'{stage}_active_likes.npz');print(stage,'like state differences',(actual[tr]-expected[tr]).nnz)\nPY\ntail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/pool_test.log; tail /app/solution/augment.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation like state differences 4467\ntest like state differences 4544\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nfull model fitted base\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\n", "stderr": "", "aggregated_output": "validation like state differences 4467\ntest like state differences 4544\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nfull model fitted base\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 980704580 }, "formatted_output": "validation like state differences 4467\ntest like state differences 4544\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nfull model fitted base\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\n" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-23T05:39:57.542Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:39:57.542Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:39:58.814Z", "callId": "call_SMmufP9l0Oqf6UDCQqd0VWH0", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:9829,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:39:58.909Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"2e7593\",\"wall_time_seconds\":0.00000195,\"exit_code\":0,\"original_token_count\":185,\"output\":\"validation like state differences 4467\\ntest like state differences 4544\\nbase OOF fold score 0.055597262296575266\\nbase OOF fold score 0.05486647535596832\\nbase OOF score 0.05524052581442583\\nfull model fitted base\\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\\nkernel 8.522367715835571\\nchol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\nsolved 87.70576524734497\\ntest_ease_hybrid_1000 NDCG 0.0\\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\\n\"}\n{\"current_time\":\"2026-09-23 05:39:58 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:39:58.899Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3662b816887d2bed110e208d7dd05", "usage": { "input_tokens": 65116, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 12, "total_tokens": 65179 }, "turn_token_usage": { "input_tokens": 2146322, "cached_input_tokens": 2077312, "cache_write_input_tokens": 0, "output_tokens": 30700, "reasoning_output_tokens": 13502, "total_tokens": 2177022 }, "thread_token_usage": { "input_tokens": 2146322, "cached_input_tokens": 2077312, "cache_write_input_tokens": 0, "output_tokens": 30700, "reasoning_output_tokens": 13502, "total_tokens": 2177022 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-23T05:40:17.685Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:40:17.685Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:40:19.415Z", "callId": "call_QVckfnAEEDWE3xsdJH2tRqXI", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:8287,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/augment.log; tail /app/solution/pool_test.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-23T05:40:24.777Z", "content": "Script completed\nWall time 5.3 seconds\nOutput:\n\n{\"chunk_id\":\"42087a\",\"wall_time_seconds\":5.000749594,\"session_id\":8287,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"eb9814\",\"wall_time_seconds\":0.00000439,\"exit_code\":0,\"original_token_count\":19,\"output\":\"low 0\\nhigh 0\\nmax 14474\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 4\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:40:19.489Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3662f1a6487d28b388ebdcace2419", "usage": { "input_tokens": 65544, "cached_input_tokens": 64896, "cache_write_input_tokens": 0, "output_tokens": 602, "reasoning_output_tokens": 516, "total_tokens": 66146 }, "turn_token_usage": { "input_tokens": 2211866, "cached_input_tokens": 2142208, "cache_write_input_tokens": 0, "output_tokens": 31302, "reasoning_output_tokens": 14018, "total_tokens": 2243168 }, "thread_token_usage": { "input_tokens": 2211866, "cached_input_tokens": 2142208, "cache_write_input_tokens": 0, "output_tokens": 31302, "reasoning_output_tokens": 14018, "total_tokens": 2243168 } } }, { "kind": "context", "timestamp": "2026-09-23T05:40:24.607Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5856ea7a-7931-451f-aabc-3010b5a4eedb", "process_id": "57987", "command": [ "/bin/sh", "-lc", "tail /app/solution/augment.log; tail /app/solution/pool_test.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/augment.log", "name": "augment.log", "path": "/app/solution/augment.log" }, { "type": "read", "cmd": "tail /app/solution/pool_test.log", "name": "pool_test.log", "path": "/app/solution/pool_test.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "low 0\nhigh 0\nmax 14474\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 4\n", "stderr": "", "aggregated_output": "low 0\nhigh 0\nmax 14474\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 4\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4390 }, "formatted_output": "low 0\nhigh 0\nmax 14474\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 4\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:40:31.752Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f21c7013-bc92-406b-b7ff-525a270e2dec", "process_id": "8287", "command": [ "/bin/sh", "-lc", "cat > /app/solution/refit_bases.py <<'PY'\nfrom model import ease\nfrom asymmetric import fit\nfor variant,reg in [('strong',300.0),('recent',300),('hybrid',1000)]:ease('test',variant,reg)\nfit('test',300.0,2.0,'organic')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_bases.py > /app/solution/refit_bases.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/refit_bases.py <<'PY'\nfrom model import ease\nfrom asymmetric import fit\nfor variant,reg in [('strong',300.0),('recent',300),('hybrid',1000)]:ease('test',variant,reg)\nfit('test',300.0,2.0,'organic')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_bases.py > /app/solution/refit_bases.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 371, "nanos": 538471974 }, "formatted_output": "" } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-23T05:41:00.695Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:00.695Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:41:09.068Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2b21e9a8-1181-4cc4-b73e-4689a1f4c2cc", "process_id": "98873", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n a=p.read_bytes().split(b'\\0')\n if len(a)>1 and a[1]==b'/app/solution/tune_extended.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\nPY\ncat > /app/solution/augment.py <<'PY'\nfrom rerank import *\nextra_names=['outside_1000.0_1.0','session_0.6_short','session_0.6_strong']\ndef augment(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];x=np.lib.format.open_memmap(P/f'{stage}_features_augmented.npy',mode='w+',dtype=np.float32,shape=(*old.shape[:2],old.shape[2]+len(extra_names)*3));e=Eval(stage);mods=[np.load(P/f'{stage}_{name}.npy',mmap_mode='r') for name in extra_names]\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));f=[];mask=e.mask(st,en)\n for mod in mods:\n s=np.array(mod[st:en]);s[~mask]=-np.inf;v=np.take_along_axis(s,pool[st:en],axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n x[st:en]=np.concatenate([old[st:en],np.stack(f,axis=2)],axis=2)\n x.flush();return x\nif __name__=='__main__':\n X=augment('validation');e=Eval();z=np.load(P/'validation_pool.npz');Y=z['Y'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;K=X.shape[1];model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);print('augmented score',metric(pred,Y,e,~fold),flush=True);joblib.dump(model,P/'rerank_augmented_holdout.joblib');np.save(P/'augmented_holdout_preds.npy',pred)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/augment.py > /app/solution/augment.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n a=p.read_bytes().split(b'\\0')\n if len(a)>1 and a[1]==b'/app/solution/tune_extended.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\nPY\ncat > /app/solution/augment.py <<'PY'\nfrom rerank import *\nextra_names=['outside_1000.0_1.0','session_0.6_short','session_0.6_strong']\ndef augment(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];x=np.lib.format.open_memmap(P/f'{stage}_features_augmented.npy',mode='w+',dtype=np.float32,shape=(*old.shape[:2],old.shape[2]+len(extra_names)*3));e=Eval(stage);mods=[np.load(P/f'{stage}_{name}.npy',mmap_mode='r') for name in extra_names]\n for st in range(0,len(targets),64):\n en=min(st+64,len(targets));f=[];mask=e.mask(st,en)\n for mod in mods:\n s=np.array(mod[st:en]);s[~mask]=-np.inf;v=np.take_along_axis(s,pool[st:en],axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n x[st:en]=np.concatenate([old[st:en],np.stack(f,axis=2)],axis=2)\n x.flush();return x\nif __name__=='__main__':\n X=augment('validation');e=Eval();z=np.load(P/'validation_pool.npz');Y=z['Y'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;K=X.shape[1];model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);print('augmented score',metric(pred,Y,e,~fold),flush=True);joblib.dump(model,P/'rerank_augmented_holdout.joblib');np.save(P/'augmented_holdout_preds.npy',pred)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/augment.py > /app/solution/augment.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 130, "nanos": 473843690 }, "formatted_output": "" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-23T05:41:14.218Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:14.218Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:41:14.831Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dfe63316-8cea-4508-8e46-77e32f0d3493", "process_id": "93512", "command": [ "/bin/sh", "-lc", "cat > /app/solution/tune_rerank.py <<'PY'\nfrom rerank import *\ne=Eval();X=np.load(P/'validation_features.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;hold=~fold;K=X.shape[1];w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\nfor leaves,iters,l2 in [(15,300,10),(31,250,10),(31,250,50),(7,300,10)]:\n t=time.time();model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42);model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);score=metric(pred,Y,e,hold);print('config',leaves,iters,l2,'score',score,'time',time.time()-t,flush=True);joblib.dump(model,P/f'rerank_tuned_{leaves}_{iters}_{l2}.joblib')\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank.py > /app/solution/tune_rerank.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/tune_rerank.py <<'PY'\nfrom rerank import *\ne=Eval();X=np.load(P/'validation_features.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;hold=~fold;K=X.shape[1];w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\nfor leaves,iters,l2 in [(15,300,10),(31,250,10),(31,250,50),(7,300,10)]:\n t=time.time();model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42);model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);score=metric(pred,Y,e,hold);print('config',leaves,iters,l2,'score',score,'time',time.time()-t,flush=True);joblib.dump(model,P/f'rerank_tuned_{leaves}_{iters}_{l2}.joblib')\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank.py > /app/solution/tune_rerank.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 427, "nanos": 144596304 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:41:30.754Z", "callId": "call_oqcGQJnsSP3eEdLMm4M4d9oT", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/validate_submission.py <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet');d=pd.read_parquet(p);z=np.load('/task/data/test_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];packed=z['packed_eligible'];d=validate_ranking(d,set(map(int,uids)));r=pd.Index(uids).get_indexer(d.uid);c=pd.Index(items).get_indexer(d.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(d):,} rows; {len(uids):,} users; 100 unique eligible tracks each.')\\nPY\\ncat > /app/solution/build.py <<'PY'\\n\\\"\\\"\\\"Rebuild the selected ranker without network access or additional packages.\\\"\\\"\\\"\\nimport os,subprocess,sys\\nfrom pathlib import Path\\nP=Path(__file__).resolve().parent\\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\\ndef run(script,*args):subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\\nrun('prepare.py')\\nrun('state.py')\\nfor variant,reg in [('strong','300.0'),('recent','300'),('hybrid','1000')]:run('model.py','validation',variant,reg)\\n# model.py parses numeric arguments as floats; normalize the two historical filenames.\\nfor variant,reg in [('recent',300),('hybrid',1000)]:\\n for ext in ['npy','parquet']:\\n (P/f'validation_ease_{variant}_{reg}.0.{ext}').replace(P/f'validation_ease_{variant}_{reg}.{ext}')\\nrun('asymmetric.py','validation','300','2','organic')\\nrun('rerank.py','ease_strong_300.0','ease_recent_300','ease_hybrid_1000','asym_organic_300.0_2.0')\\nrun('finalize_reranker.py','fit','base','15','150','10')\\nrun('refit_bases.py')\\nrun('finalize_reranker.py','predict','base')\\nrun('validate_submission.py',P/'submission_rerank_base.parquet')\\nimport shutil\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\\nPY\\ntail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/augment.log; tail /app/solution/pool_test.log\",\"max_output_tokens\":2200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:41:30.835Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"7f7016\",\"wall_time_seconds\":0.00000492,\"exit_code\":0,\"original_token_count\":173,\"output\":\"chol 9.85982370376587\\nsolved 59.81119728088379\\ntest_ease_recent_300 NDCG 0.0\\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\\nkernel 11.544252634048462\\nchol 13.856557607650757\\nsolved 87.70576524734497\\ntest_ease_hybrid_1000 NDCG 0.0\\ntest_asym_organic_300.0_2.0 149.31472516059875\\ntest_asym_organic_300.0_2.0 NDCG 0.0\\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\\nconfig 7 300 10 score 0.054897620823481284 time 87.85843539237976\\naugmented score 0.05569058501956698\\nfeatures validation (8456, 1000, 38) retrieved positives 15627\\n\"}\n{\"current_time\":\"2026-09-23 05:41:30 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:41:30.813Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0aabf135-7051-4d08-ba7b-5e77c5ee1d64", "process_id": "83032", "command": [ "/bin/sh", "-lc", "cat > /app/solution/validate_submission.py <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet');d=pd.read_parquet(p);z=np.load('/task/data/test_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];packed=z['packed_eligible'];d=validate_ranking(d,set(map(int,uids)));r=pd.Index(uids).get_indexer(d.uid);c=pd.Index(items).get_indexer(d.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(d):,} rows; {len(uids):,} users; 100 unique eligible tracks each.')\nPY\ncat > /app/solution/build.py <<'PY'\n\"\"\"Rebuild the selected ranker without network access or additional packages.\"\"\"\nimport os,subprocess,sys\nfrom pathlib import Path\nP=Path(__file__).resolve().parent\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\ndef run(script,*args):subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\nrun('prepare.py')\nrun('state.py')\nfor variant,reg in [('strong','300.0'),('recent','300'),('hybrid','1000')]:run('model.py','validation',variant,reg)\n# model.py parses numeric arguments as floats; normalize the two historical filenames.\nfor variant,reg in [('recent',300),('hybrid',1000)]:\n for ext in ['npy','parquet']:\n (P/f'validation_ease_{variant}_{reg}.0.{ext}').replace(P/f'validation_ease_{variant}_{reg}.{ext}')\nrun('asymmetric.py','validation','300','2','organic')\nrun('rerank.py','ease_strong_300.0','ease_recent_300','ease_hybrid_1000','asym_organic_300.0_2.0')\nrun('finalize_reranker.py','fit','base','15','150','10')\nrun('refit_bases.py')\nrun('finalize_reranker.py','predict','base')\nrun('validate_submission.py',P/'submission_rerank_base.parquet')\nimport shutil\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\nPY\ntail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/augment.log; tail /app/solution/pool_test.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/validate_submission.py <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet');d=pd.read_parquet(p);z=np.load('/task/data/test_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];packed=z['packed_eligible'];d=validate_ranking(d,set(map(int,uids)));r=pd.Index(uids).get_indexer(d.uid);c=pd.Index(items).get_indexer(d.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(d):,} rows; {len(uids):,} users; 100 unique eligible tracks each.')\nPY\ncat > /app/solution/build.py <<'PY'\n\"\"\"Rebuild the selected ranker without network access or additional packages.\"\"\"\nimport os,subprocess,sys\nfrom pathlib import Path\nP=Path(__file__).resolve().parent\nos.environ.update(OPENBLAS_NUM_THREADS='4',OMP_NUM_THREADS='4',PYTHONDONTWRITEBYTECODE='1')\ndef run(script,*args):subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\nrun('prepare.py')\nrun('state.py')\nfor variant,reg in [('strong','300.0'),('recent','300'),('hybrid','1000')]:run('model.py','validation',variant,reg)\n# model.py parses numeric arguments as floats; normalize the two historical filenames.\nfor variant,reg in [('recent',300),('hybrid',1000)]:\n for ext in ['npy','parquet']:\n (P/f'validation_ease_{variant}_{reg}.0.{ext}').replace(P/f'validation_ease_{variant}_{reg}.{ext}')\nrun('asymmetric.py','validation','300','2','organic')\nrun('rerank.py','ease_strong_300.0','ease_recent_300','ease_hybrid_1000','asym_organic_300.0_2.0')\nrun('finalize_reranker.py','fit','base','15','150','10')\nrun('refit_bases.py')\nrun('finalize_reranker.py','predict','base')\nrun('validate_submission.py',P/'submission_rerank_base.parquet')\nimport shutil\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\nPY\ntail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/augment.log; tail /app/solution/pool_test.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\ntest_asym_organic_300.0_2.0 149.31472516059875\ntest_asym_organic_300.0_2.0 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\nconfig 7 300 10 score 0.054897620823481284 time 87.85843539237976\naugmented score 0.05569058501956698\nfeatures validation (8456, 1000, 38) retrieved positives 15627\n", "stderr": "", "aggregated_output": "chol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\ntest_asym_organic_300.0_2.0 149.31472516059875\ntest_asym_organic_300.0_2.0 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\nconfig 7 300 10 score 0.054897620823481284 time 87.85843539237976\naugmented score 0.05569058501956698\nfeatures validation (8456, 1000, 38) retrieved positives 15627\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4920 }, "formatted_output": "chol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\ntest_asym_organic_300.0_2.0 149.31472516059875\ntest_asym_organic_300.0_2.0 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\nconfig 7 300 10 score 0.054897620823481284 time 87.85843539237976\naugmented score 0.05569058501956698\nfeatures validation (8456, 1000, 38) retrieved positives 15627\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:41:30.822Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36658370487d285d2c784f8876a04", "usage": { "input_tokens": 66312, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 1614, "reasoning_output_tokens": 922, "total_tokens": 67926 }, "turn_token_usage": { "input_tokens": 2278178, "cached_input_tokens": 2191872, "cache_write_input_tokens": 0, "output_tokens": 32916, "reasoning_output_tokens": 14940, "total_tokens": 2311094 }, "thread_token_usage": { "input_tokens": 2278178, "cached_input_tokens": 2191872, "cache_write_input_tokens": 0, "output_tokens": 32916, "reasoning_output_tokens": 14940, "total_tokens": 2311094 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-23T05:41:41.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:41:41.017Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:41:57.006Z", "callId": "call_b5KjsHikiaGc2lakBo6KK6sc", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/finalize_reranker.py predict base > /app/solution/predict_base.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_rerank_base.parquet && cp /app/solution/submission_rerank_base.parquet /app/solution/submission.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/ensemble_check.py <<'PY'\\nfrom rerank import *\\ne=Eval();a=np.load(P/'validation_features.npy',mmap_mode='r');b=np.load(P/'validation_features_extended.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;gold=Y[hold];K=Y.shape[1];preds=[]\\nfor name,X in [('rerank_poisson_holdout',a),('rerank_extended_15_150_10',b),('rerank_tuned_7_300_10',a)]:\\n m=joblib.load(P/(name+'.joblib'));p=m.predict(np.asarray(X[hold]).reshape(-1,X.shape[-1])).reshape(-1,K);p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\\np=np.load(P/'augmented_holdout_preds.npy')[hold];p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\\nfor j in [1,2,3]:\\n for weight in [.25,.5,.75]:\\n p=preds[0]*(1-weight)+preds[j]*weight;ix=np.argsort(-p,axis=1)[:,:10];s=((np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print('blend',j,weight,s,flush=True)\\nnp.save(P/'holdout_ensemble_predictions.npy',np.stack(preds))\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble_check.py > /app/solution/ensemble_check.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:41:59.346Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"f0b5fb\",\"wall_time_seconds\":1.000802545,\"session_id\":51049,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"7e1f10\",\"wall_time_seconds\":1.000915397,\"session_id\":17915,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:41:57.092Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3668b24a887d2903e9c5aa8ffd929", "usage": { "input_tokens": 68294, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 653, "reasoning_output_tokens": 99, "total_tokens": 68947 }, "turn_token_usage": { "input_tokens": 2346472, "cached_input_tokens": 2258048, "cache_write_input_tokens": 0, "output_tokens": 33569, "reasoning_output_tokens": 15039, "total_tokens": 2380041 }, "thread_token_usage": { "input_tokens": 2346472, "cached_input_tokens": 2258048, "cache_write_input_tokens": 0, "output_tokens": 33569, "reasoning_output_tokens": 15039, "total_tokens": 2380041 } } }, { "kind": "context", "timestamp": "2026-09-23T05:41:59.579Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-199cf778-92fd-4750-ab15-f1e70458d7dc", "process_id": "15656", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/pool_test.py > /app/solution/pool_test.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/pool_test.py > /app/solution/pool_test.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 170, "nanos": 880936121 }, "formatted_output": "" } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-23T05:42:17.257Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:42:17.257Z", "text": "" } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-23T05:42:17.962Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:42:17.962Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:42:19.234Z", "text": "The refitted reranker is generating the final submission now. I’m running the eligibility and schema checks, then making one last comparison of blends and shortlist sizes before selecting the saved artifact.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:42:20.980Z", "callId": "call_wDr7Zl6ARWYgBYuVKHFkVKWv", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/pool_test.log; tail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:42:21.072Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"27fe82\",\"wall_time_seconds\":0.00000408,\"exit_code\":0,\"original_token_count\":45,\"output\":\"features validation (8456, 1000, 38) retrieved positives 15627\\n1000-pool holdout 0.05561850619188768\\n-rw-r--r-- 1 agent agent 1.7M Sep 23 05:33 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-23 05:42:21 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:42:21.030Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d8d02b81-3878-474b-8c70-4231a070f5c3", "process_id": "66054", "command": [ "/bin/sh", "-lc", "tail /app/solution/pool_test.log; tail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/pool_test.log", "name": "pool_test.log", "path": "/app/solution/pool_test.log" }, { "type": "read", "cmd": "tail /app/solution/ensemble_check.log", "name": "ensemble_check.log", "path": "/app/solution/ensemble_check.log" }, { "type": "read", "cmd": "tail /app/solution/predict_base.log", "name": "predict_base.log", "path": "/app/solution/predict_base.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/submission.parquet", "path": "submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 1000, 38) retrieved positives 15627\n1000-pool holdout 0.05561850619188768\n-rw-r--r-- 1 agent agent 1.7M Sep 23 05:33 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "features validation (8456, 1000, 38) retrieved positives 15627\n1000-pool holdout 0.05561850619188768\n-rw-r--r-- 1 agent agent 1.7M Sep 23 05:33 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "features validation (8456, 1000, 38) retrieved positives 15627\n1000-pool holdout 0.05561850619188768\n-rw-r--r-- 1 agent agent 1.7M Sep 23 05:33 /app/solution/submission.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:42:21.060Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab366a7860087d282f745f329ff57aa", "usage": { "input_tokens": 69042, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 661, "reasoning_output_tokens": 539, "total_tokens": 69703 }, "turn_token_usage": { "input_tokens": 2415514, "cached_input_tokens": 2326144, "cache_write_input_tokens": 0, "output_tokens": 34230, "reasoning_output_tokens": 15578, "total_tokens": 2449744 }, "thread_token_usage": { "input_tokens": 2415514, "cached_input_tokens": 2326144, "cache_write_input_tokens": 0, "output_tokens": 34230, "reasoning_output_tokens": 15578, "total_tokens": 2449744 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-23T05:42:31.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:42:31.017Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:42:39.378Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-22a929ed-e056-4ff4-949b-380c4195f0d8", "process_id": "17915", "command": [ "/bin/sh", "-lc", "cat > /app/solution/ensemble_check.py <<'PY'\nfrom rerank import *\ne=Eval();a=np.load(P/'validation_features.npy',mmap_mode='r');b=np.load(P/'validation_features_extended.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;gold=Y[hold];K=Y.shape[1];preds=[]\nfor name,X in [('rerank_poisson_holdout',a),('rerank_extended_15_150_10',b),('rerank_tuned_7_300_10',a)]:\n m=joblib.load(P/(name+'.joblib'));p=m.predict(np.asarray(X[hold]).reshape(-1,X.shape[-1])).reshape(-1,K);p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\np=np.load(P/'augmented_holdout_preds.npy')[hold];p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\nfor j in [1,2,3]:\n for weight in [.25,.5,.75]:\n p=preds[0]*(1-weight)+preds[j]*weight;ix=np.argsort(-p,axis=1)[:,:10];s=((np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print('blend',j,weight,s,flush=True)\nnp.save(P/'holdout_ensemble_predictions.npy',np.stack(preds))\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble_check.py > /app/solution/ensemble_check.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/ensemble_check.py <<'PY'\nfrom rerank import *\ne=Eval();a=np.load(P/'validation_features.npy',mmap_mode='r');b=np.load(P/'validation_features_extended.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;gold=Y[hold];K=Y.shape[1];preds=[]\nfor name,X in [('rerank_poisson_holdout',a),('rerank_extended_15_150_10',b),('rerank_tuned_7_300_10',a)]:\n m=joblib.load(P/(name+'.joblib'));p=m.predict(np.asarray(X[hold]).reshape(-1,X.shape[-1])).reshape(-1,K);p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\np=np.load(P/'augmented_holdout_preds.npy')[hold];p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\nfor j in [1,2,3]:\n for weight in [.25,.5,.75]:\n p=preds[0]*(1-weight)+preds[j]*weight;ix=np.argsort(-p,axis=1)[:,:10];s=((np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print('blend',j,weight,s,flush=True)\nnp.save(P/'holdout_ensemble_predictions.npy',np.stack(preds))\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble_check.py > /app/solution/ensemble_check.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 41, "nanos": 33858858 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:42:44.879Z", "callId": "call_AfguOiB4XIIoMizBuVKccW9a", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# Music discovery ranker\\n\\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\\n\\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\\n\\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.055241 NDCG@10** across 4,263 relevant users for this cross-fitted ranker. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\\n\\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\\n\\nRebuild from the provided inputs, without installing anything:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/build.py\\n```\\n\\nCheck the final artifact:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/validate_submission.py\\n```\\n\\n`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\\nMD\\ntail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:42:44.972Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"8fe5ab\",\"wall_time_seconds\":0.00000484,\"exit_code\":0,\"original_token_count\":88,\"output\":\"blend 1 0.25 0.05608893608091213\\nblend 1 0.5 0.05580149475287233\\nblend 1 0.75 0.055403247053400315\\nblend 2 0.25 0.055928476425902554\\nblend 2 0.5 0.056356208254790034\\nblend 2 0.75 0.0555498432071441\\nblend 3 0.25 0.056312153245645445\\nblend 3 0.5 0.05710146909655971\\nblend 3 0.75 0.05469770994113412\\nfeatures test (8456, 500, 38) retrieved positives 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:42:44.920Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1801e031-1d8e-4fe7-978e-bb2a5fda26d2", "process_id": "21916", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.055241 NDCG@10** across 4,263 relevant users for this cross-fitted ranker. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\nMD\ntail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.055241 NDCG@10** across 4,263 relevant users for this cross-fitted ranker. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\nMD\ntail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend 1 0.25 0.05608893608091213\nblend 1 0.5 0.05580149475287233\nblend 1 0.75 0.055403247053400315\nblend 2 0.25 0.055928476425902554\nblend 2 0.5 0.056356208254790034\nblend 2 0.75 0.0555498432071441\nblend 3 0.25 0.056312153245645445\nblend 3 0.5 0.05710146909655971\nblend 3 0.75 0.05469770994113412\nfeatures test (8456, 500, 38) retrieved positives 0\n", "stderr": "", "aggregated_output": "blend 1 0.25 0.05608893608091213\nblend 1 0.5 0.05580149475287233\nblend 1 0.75 0.055403247053400315\nblend 2 0.25 0.055928476425902554\nblend 2 0.5 0.056356208254790034\nblend 2 0.75 0.0555498432071441\nblend 3 0.25 0.056312153245645445\nblend 3 0.5 0.05710146909655971\nblend 3 0.75 0.05469770994113412\nfeatures test (8456, 500, 38) retrieved positives 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4840 }, "formatted_output": "blend 1 0.25 0.05608893608091213\nblend 1 0.5 0.05580149475287233\nblend 1 0.75 0.055403247053400315\nblend 2 0.25 0.055928476425902554\nblend 2 0.5 0.056356208254790034\nblend 2 0.75 0.0555498432071441\nblend 3 0.25 0.056312153245645445\nblend 3 0.5 0.05710146909655971\nblend 3 0.75 0.05469770994113412\nfeatures test (8456, 500, 38) retrieved positives 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:42:44.970Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab366c2c58887d2885db3f2ddb68ac6", "usage": { "input_tokens": 69852, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 526, "reasoning_output_tokens": 55, "total_tokens": 70378 }, "turn_token_usage": { "input_tokens": 2485366, "cached_input_tokens": 2392320, "cache_write_input_tokens": 0, "output_tokens": 34756, "reasoning_output_tokens": 15633, "total_tokens": 2520122 }, "thread_token_usage": { "input_tokens": 2485366, "cached_input_tokens": 2392320, "cache_write_input_tokens": 0, "output_tokens": 34756, "reasoning_output_tokens": 15633, "total_tokens": 2520122 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-23T05:42:49.382Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:42:49.382Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T05:42:53.662Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-346d607c-2d44-40e1-8230-10d5d8a3293c", "process_id": "51049", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/finalize_reranker.py predict base > /app/solution/predict_base.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_rerank_base.parquet && cp /app/solution/submission_rerank_base.parquet /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/finalize_reranker.py predict base > /app/solution/predict_base.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_rerank_base.parquet && cp /app/solution/submission_rerank_base.parquet /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "exit_code": 0, "duration": { "secs": 56, "nanos": 485536872 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T05:42:59.798Z", "callId": "call_vJLqpnXvm0Ox2zIetE7BL88y", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\\nfrom rerank import *\\np=np.load(P/'holdout_ensemble_predictions.npy');e=Eval();hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;Y=np.load(P/'validation_pool.npz')['Y'][hold]\\nfor w in [[.25,.25,.25,.25],[.5,0,.25,.25],[.4,0,.2,.4],[.6,0,0,.4],[.5,.25,0,.25],[.4,.1,.1,.4]]:\\n pred=np.einsum('i,ijk->jk',w,p);ix=np.argsort(-pred,axis=1)[:,:10];score=((np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print(w,score)\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py test 1000 1 > /app/solution/final_outside.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py test 0.6 > /app/solution/final_session.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:43:03.303Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\"chunk_id\":\"3b314f\",\"wall_time_seconds\":1.001348082,\"session_id\":57833,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"88808e\",\"wall_time_seconds\":1.001260921,\"session_id\":72013,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"73ae5c\",\"wall_time_seconds\":1.0011786,\"session_id\":8267,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:42:59.901Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab366d5406887d2a84088f4fbee82e0", "usage": { "input_tokens": 70604, "cached_input_tokens": 69632, "cache_write_input_tokens": 0, "output_tokens": 432, "reasoning_output_tokens": 69, "total_tokens": 71036 }, "turn_token_usage": { "input_tokens": 2555970, "cached_input_tokens": 2461952, "cache_write_input_tokens": 0, "output_tokens": 35188, "reasoning_output_tokens": 15702, "total_tokens": 2591158 }, "thread_token_usage": { "input_tokens": 2555970, "cached_input_tokens": 2461952, "cache_write_input_tokens": 0, "output_tokens": 35188, "reasoning_output_tokens": 15702, "total_tokens": 2591158 } } }, { "kind": "context", "timestamp": "2026-09-23T05:43:01.727Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f36f36b7-021e-4b50-aed1-ee91388efe0d", "process_id": "57833", "command": [ "/bin/sh", "-lc", "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom rerank import *\np=np.load(P/'holdout_ensemble_predictions.npy');e=Eval();hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;Y=np.load(P/'validation_pool.npz')['Y'][hold]\nfor w in [[.25,.25,.25,.25],[.5,0,.25,.25],[.4,0,.2,.4],[.6,0,0,.4],[.5,.25,0,.25],[.4,.1,.1,.4]]:\n pred=np.einsum('i,ijk->jk',w,p);ix=np.argsort(-pred,axis=1)[:,:10];score=((np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print(w,score)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom rerank import *\np=np.load(P/'holdout_ensemble_predictions.npy');e=Eval();hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;Y=np.load(P/'validation_pool.npz')['Y'][hold]\nfor w in [[.25,.25,.25,.25],[.5,0,.25,.25],[.4,0,.2,.4],[.6,0,0,.4],[.5,.25,0,.25],[.4,.1,.1,.4]]:\n pred=np.einsum('i,ijk->jk',w,p);ix=np.argsort(-pred,axis=1)[:,:10];score=((np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print(w,score)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[0.25, 0.25, 0.25, 0.25] 0.056562235832124816\n[0.5, 0, 0.25, 0.25] 0.05669699133120211\n[0.4, 0, 0.2, 0.4] 0.056733271279730095\n[0.6, 0, 0, 0.4] 0.05662318525664434\n[0.5, 0.25, 0, 0.25] 0.05641781965811713\n[0.4, 0.1, 0.1, 0.4] 0.05697840976583812\n", "stderr": "", "aggregated_output": "[0.25, 0.25, 0.25, 0.25] 0.056562235832124816\n[0.5, 0, 0.25, 0.25] 0.05669699133120211\n[0.4, 0, 0.2, 0.4] 0.056733271279730095\n[0.6, 0, 0, 0.4] 0.05662318525664434\n[0.5, 0.25, 0, 0.25] 0.05641781965811713\n[0.4, 0.1, 0.1, 0.4] 0.05697840976583812\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 758585272 }, "formatted_output": "[0.25, 0.25, 0.25, 0.25] 0.056562235832124816\n[0.5, 0, 0.25, 0.25] 0.05669699133120211\n[0.4, 0, 0.2, 0.4] 0.056733271279730095\n[0.6, 0, 0, 0.4] 0.05662318525664434\n[0.5, 0.25, 0, 0.25] 0.05641781965811713\n[0.4, 0.1, 0.1, 0.4] 0.05697840976583812\n" } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-23T05:43:06.164Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:43:06.164Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:43:30.010Z", "callId": "call_Z7lfgOBHE5SA34i0izjoWwBZ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/final_augmented.py <<'PY'\\nfrom finalize_reranker import export\\nfrom augment import *\\ndef train():\\n e=Eval();X=np.load(P/'validation_features_augmented.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1];config=dict(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\\n pred=np.load(P/'augmented_holdout_preds.npy');sample=(~fold)&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[fold]=model.predict(np.asarray(X[fold]).reshape(-1,X.shape[-1])).reshape(-1,K);np.save(P/'oof_augmented.npy',pred)\\n base=np.load(P/'oof_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);combined=.5*base+.5*pred;print('augmented OOF',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);print('ensemble OOF',metric(combined,Y,e,np.ones(len(targets),bool)),flush=True);export(combined,pool,P/'validation_ensemble.parquet')\\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/'final_reranker_augmented.joblib');print('augmented full fit complete',flush=True)\\ndef predict():\\n X=augment('test');pool=np.load(P/'test_pool.npz')['pool'];model=joblib.load(P/'final_reranker_augmented.joblib');pred=np.empty(pool.shape,np.float32)\\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\\n np.save(P/'test_predictions_augmented.npy',pred);base=np.load(P/'test_predictions_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);export(.5*base+.5*pred,pool,P/'submission_ensemble.parquet');print('ensemble test ranking saved',flush=True)\\nif __name__=='__main__':train() if sys.argv[1]=='train' else predict()\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/final_augmented.py train > /app/solution/final_augmented.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.write_stdin({session_id:57833,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/predict_base.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:43:31.248Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a3a7b6\",\"wall_time_seconds\":1.001095215,\"session_id\":88627,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"d0513a\",\"wall_time_seconds\":0.00000294,\"exit_code\":0,\"original_token_count\":62,\"output\":\"[0.25, 0.25, 0.25, 0.25] 0.056562235832124816\\n[0.5, 0, 0.25, 0.25] 0.05669699133120211\\n[0.4, 0, 0.2, 0.4] 0.056733271279730095\\n[0.6, 0, 0, 0.4] 0.05662318525664434\\n[0.5, 0.25, 0, 0.25] 0.05641781965811713\\n[0.4, 0.1, 0.1, 0.4] 0.05697840976583812\\n\"}\n{\"chunk_id\":\"0e0872\",\"wall_time_seconds\":0.0000053,\"exit_code\":0,\"original_token_count\":38,\"output\":\"features test (8456, 500, 38) retrieved positives 0\\nprediction saved base\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-23 05:43:31 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:43:30.106Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab366e7797487d2911ca135ae156f72", "usage": { "input_tokens": 71163, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 830, "reasoning_output_tokens": 27, "total_tokens": 71993 }, "turn_token_usage": { "input_tokens": 2627133, "cached_input_tokens": 2532352, "cache_write_input_tokens": 0, "output_tokens": 36018, "reasoning_output_tokens": 15729, "total_tokens": 2663151 }, "thread_token_usage": { "input_tokens": 2627133, "cached_input_tokens": 2532352, "cache_write_input_tokens": 0, "output_tokens": 36018, "reasoning_output_tokens": 15729, "total_tokens": 2663151 } } }, { "kind": "context", "timestamp": "2026-09-23T05:43:31.229Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d2f8845d-af94-4773-b20d-9bacef5c17b6", "process_id": "79543", "command": [ "/bin/sh", "-lc", "tail /app/solution/predict_base.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/predict_base.log", "name": "predict_base.log", "path": "/app/solution/predict_base.log" }, { "type": "list_files", "cmd": "ls -lh /app/solution/submission.parquet", "path": "submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features test (8456, 500, 38) retrieved positives 0\nprediction saved base\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "features test (8456, 500, 38) retrieved positives 0\nprediction saved base\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": 5300 }, "formatted_output": "features test (8456, 500, 38) retrieved positives 0\nprediction saved base\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission.parquet\n" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-23T05:43:49.607Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:43:49.607Z", "text": "" } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-23T05:44:06.359Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:44:06.359Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:44:07.516Z", "text": "The refitted base submission has passed all structural and eligibility checks and is saved. A blend that adds session and outside-catalog features improved the held-out score to **0.0571**; I’m checking that improvement on the second fold before replacing the base submission.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:44:08.601Z", "callId": "call_qMKigmF83Rf4uJTUY6SU0FyE", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/final_augmented.log; tail /app/solution/final_outside.log; tail /app/solution/final_session.log; /opt/task-venv/bin/python /app/solution/validate_submission.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:44:10.124Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\n{\"chunk_id\":\"7807f8\",\"wall_time_seconds\":1.001149246,\"session_id\":28369,\"original_token_count\":16,\"output\":\"sim built 35.220258474349976\\ntest_session_0.6_short NDCG 0.0\\n\"}\n{\"current_time\":\"2026-09-23 05:44:10 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:44:08.662Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36703b2c887d2b682c38506042542", "usage": { "input_tokens": 72359, "cached_input_tokens": 71040, "cache_write_input_tokens": 0, "output_tokens": 1187, "reasoning_output_tokens": 1034, "total_tokens": 73546 }, "turn_token_usage": { "input_tokens": 2699492, "cached_input_tokens": 2603392, "cache_write_input_tokens": 0, "output_tokens": 37205, "reasoning_output_tokens": 16763, "total_tokens": 2736697 }, "thread_token_usage": { "input_tokens": 2699492, "cached_input_tokens": 2603392, "cache_write_input_tokens": 0, "output_tokens": 37205, "reasoning_output_tokens": 16763, "total_tokens": 2736697 } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:10.316Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-52a067bb-1966-4283-8c3e-2c938870a73c", "process_id": "28369", "command": [ "/bin/sh", "-lc", "tail /app/solution/final_augmented.log; tail /app/solution/final_outside.log; tail /app/solution/final_session.log; /opt/task-venv/bin/python /app/solution/validate_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail /app/solution/final_augmented.log; tail /app/solution/final_outside.log; tail /app/solution/final_session.log; /opt/task-venv/bin/python /app/solution/validate_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 321410566 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:14.360Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c0ca0d96-6796-4ba9-82ac-98b841386727", "process_id": "8267", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py test 0.6 > /app/solution/final_session.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/session_knn.py test 0.6 > /app/solution/final_session.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 72, "nanos": 58674003 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:22.040Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b0990ca5-104a-4f92-9b2b-9384aaac73fc", "process_id": "72013", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py test 1000 1 > /app/solution/final_outside.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/outside.py test 1000 1 > /app/solution/final_outside.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 80, "nanos": 905310313 }, "formatted_output": "" } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-23T05:44:27.863Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:44:27.863Z", "text": "" } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-23T05:44:37.183Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:44:37.183Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:44:52.533Z", "callId": "call_DvVY0valYL8BLEBZdkUEnXYA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/wide_pool.py <<'PY'\\nfrom rerank import *\\nfrom finalize_reranker import export\\nfrom augment import extra_names\\nstage=sys.argv[1];names=np.load(P/'validation_pool.npz')['names'].tolist()+extra_names;X,pool,Y,e=features(stage,names,800)\\nbasecols=list(range(12))+list(range(21,47));augcols=basecols+list(range(12,21));mods=[joblib.load(P/('rerank_poisson_holdout.joblib' if stage=='validation' else 'final_reranker_base.joblib')),joblib.load(P/('rerank_augmented_holdout.joblib' if stage=='validation' else 'final_reranker_augmented.joblib'))];preds=[]\\nfor model,cols in zip(mods,[basecols,augcols]):\\n pred=np.empty(pool.shape,np.float32)\\n for st in range(0,len(targets),128):\\n a=np.asarray(X[st:st+128])[:,:,cols];pred[st:st+128]=model.predict(a.reshape(-1,len(cols))).reshape(-1,pool.shape[1])\\n pred/=np.maximum(pred.max(1,keepdims=True),1e-6);preds.append(pred)\\npred=.5*preds[0]+.5*preds[1]\\nif stage=='validation':\\n hold=np.random.default_rng(2026).random(len(targets))>=.5;print('wide ensemble holdout',metric(pred,Y,e,hold),flush=True)\\nelse:export(pred,pool,P/'submission_wide.parquet');print('wide submission written',flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py validation > /app/solution/wide_pool.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"tail /app/solution/final_augmented.log; tail /app/solution/final_outside.log; tail /app/solution/final_session.log\",\"max_output_tokens\":1200}));\ntext(await tools.write_stdin({session_id:28369,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:44:53.761Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"3191a1\",\"wall_time_seconds\":1.000619311,\"session_id\":55713,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"de28dc\",\"wall_time_seconds\":0.00000464,\"exit_code\":0,\"original_token_count\":67,\"output\":\"augmented OOF 0.056486058135766314\\nensemble OOF 0.05686210128731122\\ntest_outside_1000.0_1.0 time 78.88469362258911\\ntest_outside_1000.0_1.0 NDCG 0.0\\nsim built 35.220258474349976\\ntest_session_0.6_short NDCG 0.0\\ntest_session_0.6_strong NDCG 0.0\\ndone 71.20409846305847\\n\"}\n{\"chunk_id\":\"bddfff\",\"wall_time_seconds\":0.00000185,\"exit_code\":0,\"original_token_count\":17,\"output\":\"VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\\n\"}\n{\"current_time\":\"2026-09-23 05:44:53 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:44:52.640Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3672a617c87d2a1fa5d5ea50a24c6", "usage": { "input_tokens": 73648, "cached_input_tokens": 72192, "cache_write_input_tokens": 0, "output_tokens": 1342, "reasoning_output_tokens": 821, "total_tokens": 74990 }, "turn_token_usage": { "input_tokens": 2773140, "cached_input_tokens": 2675584, "cache_write_input_tokens": 0, "output_tokens": 38547, "reasoning_output_tokens": 17584, "total_tokens": 2811687 }, "thread_token_usage": { "input_tokens": 2773140, "cached_input_tokens": 2675584, "cache_write_input_tokens": 0, "output_tokens": 38547, "reasoning_output_tokens": 17584, "total_tokens": 2811687 } } }, { "kind": "context", "timestamp": "2026-09-23T05:44:53.743Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e9135d77-20ca-40e5-92a8-27bc6aa04f22", "process_id": "31593", "command": [ "/bin/sh", "-lc", "tail /app/solution/final_augmented.log; tail /app/solution/final_outside.log; tail /app/solution/final_session.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/final_augmented.log", "name": "final_augmented.log", "path": "/app/solution/final_augmented.log" }, { "type": "read", "cmd": "tail /app/solution/final_outside.log", "name": "final_outside.log", "path": "/app/solution/final_outside.log" }, { "type": "read", "cmd": "tail /app/solution/final_session.log", "name": "final_session.log", "path": "/app/solution/final_session.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\ntest_outside_1000.0_1.0 time 78.88469362258911\ntest_outside_1000.0_1.0 NDCG 0.0\nsim built 35.220258474349976\ntest_session_0.6_short NDCG 0.0\ntest_session_0.6_strong NDCG 0.0\ndone 71.20409846305847\n", "stderr": "", "aggregated_output": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\ntest_outside_1000.0_1.0 time 78.88469362258911\ntest_outside_1000.0_1.0 NDCG 0.0\nsim built 35.220258474349976\ntest_session_0.6_short NDCG 0.0\ntest_session_0.6_strong NDCG 0.0\ndone 71.20409846305847\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4640 }, "formatted_output": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\ntest_outside_1000.0_1.0 time 78.88469362258911\ntest_outside_1000.0_1.0 NDCG 0.0\nsim built 35.220258474349976\ntest_session_0.6_short NDCG 0.0\ntest_session_0.6_strong NDCG 0.0\ndone 71.20409846305847\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:45:05.029Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b30ef64-5ef1-49f9-88aa-fa1aed0acbae", "process_id": "88627", "command": [ "/bin/sh", "-lc", "cat > /app/solution/final_augmented.py <<'PY'\nfrom finalize_reranker import export\nfrom augment import *\ndef train():\n e=Eval();X=np.load(P/'validation_features_augmented.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1];config=dict(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n pred=np.load(P/'augmented_holdout_preds.npy');sample=(~fold)&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[fold]=model.predict(np.asarray(X[fold]).reshape(-1,X.shape[-1])).reshape(-1,K);np.save(P/'oof_augmented.npy',pred)\n base=np.load(P/'oof_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);combined=.5*base+.5*pred;print('augmented OOF',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);print('ensemble OOF',metric(combined,Y,e,np.ones(len(targets),bool)),flush=True);export(combined,pool,P/'validation_ensemble.parquet')\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/'final_reranker_augmented.joblib');print('augmented full fit complete',flush=True)\ndef predict():\n X=augment('test');pool=np.load(P/'test_pool.npz')['pool'];model=joblib.load(P/'final_reranker_augmented.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n np.save(P/'test_predictions_augmented.npy',pred);base=np.load(P/'test_predictions_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);export(.5*base+.5*pred,pool,P/'submission_ensemble.parquet');print('ensemble test ranking saved',flush=True)\nif __name__=='__main__':train() if sys.argv[1]=='train' else predict()\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/final_augmented.py train > /app/solution/final_augmented.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/final_augmented.py <<'PY'\nfrom finalize_reranker import export\nfrom augment import *\ndef train():\n e=Eval();X=np.load(P/'validation_features_augmented.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1];config=dict(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n pred=np.load(P/'augmented_holdout_preds.npy');sample=(~fold)&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[fold]=model.predict(np.asarray(X[fold]).reshape(-1,X.shape[-1])).reshape(-1,K);np.save(P/'oof_augmented.npy',pred)\n base=np.load(P/'oof_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);combined=.5*base+.5*pred;print('augmented OOF',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);print('ensemble OOF',metric(combined,Y,e,np.ones(len(targets),bool)),flush=True);export(combined,pool,P/'validation_ensemble.parquet')\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/'final_reranker_augmented.joblib');print('augmented full fit complete',flush=True)\ndef predict():\n X=augment('test');pool=np.load(P/'test_pool.npz')['pool'];model=joblib.load(P/'final_reranker_augmented.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n np.save(P/'test_predictions_augmented.npy',pred);base=np.load(P/'test_predictions_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);export(.5*base+.5*pred,pool,P/'submission_ensemble.parquet');print('ensemble test ranking saved',flush=True)\nif __name__=='__main__':train() if sys.argv[1]=='train' else predict()\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/final_augmented.py train > /app/solution/final_augmented.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 94, "nanos": 844471001 }, "formatted_output": "" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-23T05:45:09.774Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:45:09.774Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:45:13.454Z", "callId": "call_QTMt8As0Uo99ipeT363TM8Bw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"while ! rg -q 'augmented full fit complete' /app/solution/final_augmented.log; do sleep 2; done\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/final_augmented.py predict > /app/solution/predict_ensemble.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_ensemble.parquet && cp /app/solution/submission_ensemble.parquet /app/solution/submission.parquet\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:45:15.842Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"81c167\",\"wall_time_seconds\":1.001537919,\"session_id\":45062,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"62e79a\",\"wall_time_seconds\":1.001479827,\"session_id\":52582,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:45:13.526Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36755f52087d2a2c33530f0076d2c", "usage": { "input_tokens": 75277, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 515, "reasoning_output_tokens": 326, "total_tokens": 75792 }, "turn_token_usage": { "input_tokens": 2848417, "cached_input_tokens": 2749056, "cache_write_input_tokens": 0, "output_tokens": 39062, "reasoning_output_tokens": 17910, "total_tokens": 2887479 }, "thread_token_usage": { "input_tokens": 2848417, "cached_input_tokens": 2749056, "cache_write_input_tokens": 0, "output_tokens": 39062, "reasoning_output_tokens": 17910, "total_tokens": 2887479 } } }, { "kind": "context", "timestamp": "2026-09-23T05:45:17.443Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9055c3b8-9f01-4d84-a34c-80d6d2609f3e", "process_id": "52582", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_ensemble.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.056862,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_ensemble.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.056862,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 603286809 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_ensemble.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.056862,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-23T05:45:35.956Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:45:35.956Z", "text": "" } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-23T05:45:44.552Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:45:44.552Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:45:45.937Z", "text": "The blend improved the full two-fold result to **0.05686 NDCG@10**. I’m selecting it for the final submission and finishing its test-cutoff predictions, validation checks, and reproducible build script.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:02.335Z", "callId": "call_xRHVGOmA6NBy4ZC7cRLrgfFS", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/build.py');s=p.read_text();s=s.replace(\\\"run('validate_submission.py',P/'submission_rerank_base.parquet')\\\\nimport shutil\\\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\\\", \\\"run('validate_submission.py',P/'submission_rerank_base.parquet')\\\\nimport shutil\\\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\\\\nrun('extra_prepare.py')\\\\nrun('outside.py','validation','1000','1')\\\\nrun('session_knn.py','validation','0.6')\\\\nrun('augment.py')\\\\nrun('final_augmented.py','train')\\\\nrun('outside.py','test','1000','1')\\\\nrun('session_knn.py','test','0.6')\\\\nrun('final_augmented.py','predict')\\\\nrun('validate_submission.py',P/'submission_ensemble.parquet')\\\\nshutil.copyfile(P/'submission_ensemble.parquet',P/'submission.parquet')\\\")\\np.write_text(s)\\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics.', 'Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged.').replace('**0.055241 NDCG@10**','**0.056862 NDCG@10**').replace('`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline.', '`build.py` orchestrates the selected pipeline; `final_augmented.py` creates the final ensemble.');p.write_text(s)\\nimport json\\njson.dump({'public_cross_fitted_ndcg_at_10':0.056862,'quality_users':4263,'target_users':8456,'rows':845600,'train_cutoff':24786800,'test_history_cutoff':25393400,'reranker_folds':2,'ensemble_weights':[0.5,0.5]},open('/app/solution/metrics.json','w'),indent=2)\\nPY\\ntail /app/solution/final_augmented.log; tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log\",\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:52582,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:46:02.581Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"7366ab\",\"wall_time_seconds\":0.055952039,\"exit_code\":0,\"original_token_count\":24,\"output\":\"augmented OOF 0.056486058135766314\\nensemble OOF 0.05686210128731122\\naugmented full fit complete\\n\"}\n{\"chunk_id\":\"b5d5c4\",\"wall_time_seconds\":0.0000012,\"exit_code\":0,\"original_token_count\":35,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation_ensemble.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.056862,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}\n{\"current_time\":\"2026-09-23 05:46:02 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:02.396Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3676c1c8c87d2863d534a265ea1a9", "usage": { "input_tokens": 75884, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 1441, "reasoning_output_tokens": 762, "total_tokens": 77325 }, "turn_token_usage": { "input_tokens": 2924301, "cached_input_tokens": 2824192, "cache_write_input_tokens": 0, "output_tokens": 40503, "reasoning_output_tokens": 18672, "total_tokens": 2964804 }, "thread_token_usage": { "input_tokens": 2924301, "cached_input_tokens": 2824192, "cache_write_input_tokens": 0, "output_tokens": 40503, "reasoning_output_tokens": 18672, "total_tokens": 2964804 } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:02.567Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c49b257d-5601-4beb-ac70-6b53a26a2916", "process_id": "17645", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s=s.replace(\"run('validate_submission.py',P/'submission_rerank_base.parquet')\\nimport shutil\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\", \"run('validate_submission.py',P/'submission_rerank_base.parquet')\\nimport shutil\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\\nrun('extra_prepare.py')\\nrun('outside.py','validation','1000','1')\\nrun('session_knn.py','validation','0.6')\\nrun('augment.py')\\nrun('final_augmented.py','train')\\nrun('outside.py','test','1000','1')\\nrun('session_knn.py','test','0.6')\\nrun('final_augmented.py','predict')\\nrun('validate_submission.py',P/'submission_ensemble.parquet')\\nshutil.copyfile(P/'submission_ensemble.parquet',P/'submission.parquet')\")\np.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics.', 'Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged.').replace('**0.055241 NDCG@10**','**0.056862 NDCG@10**').replace('`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline.', '`build.py` orchestrates the selected pipeline; `final_augmented.py` creates the final ensemble.');p.write_text(s)\nimport json\njson.dump({'public_cross_fitted_ndcg_at_10':0.056862,'quality_users':4263,'target_users':8456,'rows':845600,'train_cutoff':24786800,'test_history_cutoff':25393400,'reranker_folds':2,'ensemble_weights':[0.5,0.5]},open('/app/solution/metrics.json','w'),indent=2)\nPY\ntail /app/solution/final_augmented.log; tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s=s.replace(\"run('validate_submission.py',P/'submission_rerank_base.parquet')\\nimport shutil\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\", \"run('validate_submission.py',P/'submission_rerank_base.parquet')\\nimport shutil\\nshutil.copyfile(P/'submission_rerank_base.parquet',P/'submission.parquet')\\nrun('extra_prepare.py')\\nrun('outside.py','validation','1000','1')\\nrun('session_knn.py','validation','0.6')\\nrun('augment.py')\\nrun('final_augmented.py','train')\\nrun('outside.py','test','1000','1')\\nrun('session_knn.py','test','0.6')\\nrun('final_augmented.py','predict')\\nrun('validate_submission.py',P/'submission_ensemble.parquet')\\nshutil.copyfile(P/'submission_ensemble.parquet',P/'submission.parquet')\")\np.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text().replace('A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics.', 'Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged.').replace('**0.055241 NDCG@10**','**0.056862 NDCG@10**').replace('`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline.', '`build.py` orchestrates the selected pipeline; `final_augmented.py` creates the final ensemble.');p.write_text(s)\nimport json\njson.dump({'public_cross_fitted_ndcg_at_10':0.056862,'quality_users':4263,'target_users':8456,'rows':845600,'train_cutoff':24786800,'test_history_cutoff':25393400,'reranker_folds':2,'ensemble_weights':[0.5,0.5]},open('/app/solution/metrics.json','w'),indent=2)\nPY\ntail /app/solution/final_augmented.log; tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\naugmented full fit complete\n", "stderr": "", "aggregated_output": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\naugmented full fit complete\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 55901268 }, "formatted_output": "augmented OOF 0.056486058135766314\nensemble OOF 0.05686210128731122\naugmented full fit complete\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:19.772Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-65ac9127-9aee-4f68-bab9-8b406c479863", "process_id": "45062", "command": [ "/bin/sh", "-lc", "while ! rg -q 'augmented full fit complete' /app/solution/final_augmented.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/final_augmented.py predict > /app/solution/predict_ensemble.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_ensemble.parquet && cp /app/solution/submission_ensemble.parquet /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "while ! rg -q 'augmented full fit complete' /app/solution/final_augmented.log; do sleep 2; done\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 /opt/task-venv/bin/python /app/solution/final_augmented.py predict > /app/solution/predict_ensemble.log 2>&1 && /opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_ensemble.parquet && cp /app/solution/submission_ensemble.parquet /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "exit_code": 0, "duration": { "secs": 66, "nanos": 117789832 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n" } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-23T05:46:22.330Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:46:22.330Z", "text": "" } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-23T05:46:38.968Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:46:38.968Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:40.041Z", "callId": "call_KcOIrFR2Kw5IYajD1oWRRNY6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log; ls -lh /app/solution/submission*.parquet; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:46:40.134Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"62c084\",\"wall_time_seconds\":0.00000396,\"exit_code\":0,\"original_token_count\":104,\"output\":\"ensemble test ranking saved\\nfeatures validation (8456, 800, 47) retrieved positives 14381\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission.parquet\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission_ensemble.parquet\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission_rerank_base.parquet\\nlow 0\\nhigh 0\\nmax 24257\\noom 0\\noom_kill 0\\noom_group_kill 0\\nsock_throttled 8\\n\"}\n{\"current_time\":\"2026-09-23 05:46:40 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:40.093Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86f89fda-aaf2-4c29-a2ab-9534760afcc7", "process_id": "92255", "command": [ "/bin/sh", "-lc", "tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log; ls -lh /app/solution/submission*.parquet; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail /app/solution/predict_ensemble.log; tail /app/solution/wide_pool.log; ls -lh /app/solution/submission*.parquet; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ensemble test ranking saved\nfeatures validation (8456, 800, 47) retrieved positives 14381\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission_ensemble.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission_rerank_base.parquet\nlow 0\nhigh 0\nmax 24257\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 8\n", "stderr": "", "aggregated_output": "ensemble test ranking saved\nfeatures validation (8456, 800, 47) retrieved positives 14381\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission_ensemble.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission_rerank_base.parquet\nlow 0\nhigh 0\nmax 24257\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3960 }, "formatted_output": "ensemble test ranking saved\nfeatures validation (8456, 800, 47) retrieved positives 14381\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:46 /app/solution/submission_ensemble.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:42 /app/solution/submission_rerank_base.parquet\nlow 0\nhigh 0\nmax 24257\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 8\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:40.133Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3679ad07487d2a901860ac0482953", "usage": { "input_tokens": 77520, "cached_input_tokens": 75648, "cache_write_input_tokens": 0, "output_tokens": 1114, "reasoning_output_tokens": 1034, "total_tokens": 78634 }, "turn_token_usage": { "input_tokens": 3001821, "cached_input_tokens": 2899840, "cache_write_input_tokens": 0, "output_tokens": 41617, "reasoning_output_tokens": 19706, "total_tokens": 3043438 }, "thread_token_usage": { "input_tokens": 3001821, "cached_input_tokens": 2899840, "cache_write_input_tokens": 0, "output_tokens": 41617, "reasoning_output_tokens": 19706, "total_tokens": 3043438 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-23T05:46:47.696Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:46:47.696Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:46:53.237Z", "callId": "call_RISs8fW8AopJzyT5KREdy4Dr", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py test > /app/solution/wide_test.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport ast\\nfrom pathlib import Path\\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\\nprint('All Python sources parse successfully.')\\nPY\\n/opt/task-venv/bin/python /app/solution/validate_submission.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:45062,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:46:55.588Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"ce0b76\",\"wall_time_seconds\":1.00139019,\"session_id\":50483,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"7e3e36\",\"wall_time_seconds\":1.000891781,\"session_id\":96500,\"original_token_count\":10,\"output\":\"All Python sources parse successfully.\\n\"}\n{\"chunk_id\":\"d61ddc\",\"wall_time_seconds\":0.00000221,\"exit_code\":0,\"original_token_count\":17,\"output\":\"VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:46:53.314Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab367c0875c87d2a49e86347648a3dc", "usage": { "input_tokens": 78869, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 370, "reasoning_output_tokens": 170, "total_tokens": 79239 }, "turn_token_usage": { "input_tokens": 3080690, "cached_input_tokens": 2977152, "cache_write_input_tokens": 0, "output_tokens": 41987, "reasoning_output_tokens": 19876, "total_tokens": 3122677 }, "thread_token_usage": { "input_tokens": 3080690, "cached_input_tokens": 2977152, "cache_write_input_tokens": 0, "output_tokens": 41987, "reasoning_output_tokens": 19876, "total_tokens": 3122677 } } }, { "kind": "context", "timestamp": "2026-09-23T05:46:55.945Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8c3b6b9a-f15e-49b5-ae88-bf7c3a26bdea", "process_id": "96500", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport ast\nfrom pathlib import Path\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\nprint('All Python sources parse successfully.')\nPY\n/opt/task-venv/bin/python /app/solution/validate_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport ast\nfrom pathlib import Path\nfor p in Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\nprint('All Python sources parse successfully.')\nPY\n/opt/task-venv/bin/python /app/solution/validate_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 361444616 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n" } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-23T05:47:14.254Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:14.254Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:47:14.960Z", "callId": "call_XBpfmTHberIr7V81qflSd638", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/wide_pool.log; tail /app/solution/wide_test.log; du -sh /app/solution\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:47:15.053Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2f9d67\",\"wall_time_seconds\":0.00000456,\"exit_code\":0,\"original_token_count\":20,\"output\":\"features validation (8456, 800, 47) retrieved positives 14381\\n36G\\t/app/solution\\n\"}\n{\"current_time\":\"2026-09-23 05:47:15 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:47:15.009Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5fd2302-a9de-4815-a071-214be356d8ad", "process_id": "65515", "command": [ "/bin/sh", "-lc", "tail /app/solution/wide_pool.log; tail /app/solution/wide_test.log; du -sh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/wide_pool.log", "name": "wide_pool.log", "path": "/app/solution/wide_pool.log" }, { "type": "read", "cmd": "tail /app/solution/wide_test.log", "name": "wide_test.log", "path": "/app/solution/wide_test.log" }, { "type": "list_files", "cmd": "du -sh /app/solution", "path": "solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 800, 47) retrieved positives 14381\n36G\t/app/solution\n", "stderr": "", "aggregated_output": "features validation (8456, 800, 47) retrieved positives 14381\n36G\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4560 }, "formatted_output": "features validation (8456, 800, 47) retrieved positives 14381\n36G\t/app/solution\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:15.051Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab367cfc2e087d2b2f50f12d02e3f80", "usage": { "input_tokens": 79394, "cached_input_tokens": 78720, "cache_write_input_tokens": 0, "output_tokens": 580, "reasoning_output_tokens": 516, "total_tokens": 79974 }, "turn_token_usage": { "input_tokens": 3160084, "cached_input_tokens": 3055872, "cache_write_input_tokens": 0, "output_tokens": 42567, "reasoning_output_tokens": 20392, "total_tokens": 3202651 }, "thread_token_usage": { "input_tokens": 3160084, "cached_input_tokens": 3055872, "cache_write_input_tokens": 0, "output_tokens": 42567, "reasoning_output_tokens": 20392, "total_tokens": 3202651 } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:24.580Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-57424c0a-2a42-4a64-ada6-b852b8be1717", "process_id": "55713", "command": [ "/bin/sh", "-lc", "cat > /app/solution/wide_pool.py <<'PY'\nfrom rerank import *\nfrom finalize_reranker import export\nfrom augment import extra_names\nstage=sys.argv[1];names=np.load(P/'validation_pool.npz')['names'].tolist()+extra_names;X,pool,Y,e=features(stage,names,800)\nbasecols=list(range(12))+list(range(21,47));augcols=basecols+list(range(12,21));mods=[joblib.load(P/('rerank_poisson_holdout.joblib' if stage=='validation' else 'final_reranker_base.joblib')),joblib.load(P/('rerank_augmented_holdout.joblib' if stage=='validation' else 'final_reranker_augmented.joblib'))];preds=[]\nfor model,cols in zip(mods,[basecols,augcols]):\n pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):\n a=np.asarray(X[st:st+128])[:,:,cols];pred[st:st+128]=model.predict(a.reshape(-1,len(cols))).reshape(-1,pool.shape[1])\n pred/=np.maximum(pred.max(1,keepdims=True),1e-6);preds.append(pred)\npred=.5*preds[0]+.5*preds[1]\nif stage=='validation':\n hold=np.random.default_rng(2026).random(len(targets))>=.5;print('wide ensemble holdout',metric(pred,Y,e,hold),flush=True)\nelse:export(pred,pool,P/'submission_wide.parquet');print('wide submission written',flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py validation > /app/solution/wide_pool.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/wide_pool.py <<'PY'\nfrom rerank import *\nfrom finalize_reranker import export\nfrom augment import extra_names\nstage=sys.argv[1];names=np.load(P/'validation_pool.npz')['names'].tolist()+extra_names;X,pool,Y,e=features(stage,names,800)\nbasecols=list(range(12))+list(range(21,47));augcols=basecols+list(range(12,21));mods=[joblib.load(P/('rerank_poisson_holdout.joblib' if stage=='validation' else 'final_reranker_base.joblib')),joblib.load(P/('rerank_augmented_holdout.joblib' if stage=='validation' else 'final_reranker_augmented.joblib'))];preds=[]\nfor model,cols in zip(mods,[basecols,augcols]):\n pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):\n a=np.asarray(X[st:st+128])[:,:,cols];pred[st:st+128]=model.predict(a.reshape(-1,len(cols))).reshape(-1,pool.shape[1])\n pred/=np.maximum(pred.max(1,keepdims=True),1e-6);preds.append(pred)\npred=.5*preds[0]+.5*preds[1]\nif stage=='validation':\n hold=np.random.default_rng(2026).random(len(targets))>=.5;print('wide ensemble holdout',metric(pred,Y,e,hold),flush=True)\nelse:export(pred,pool,P/'submission_wide.parquet');print('wide submission written',flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py validation > /app/solution/wide_pool.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 151, "nanos": 877761391 }, "formatted_output": "" } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-23T05:47:35.685Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:35.685Z", "text": "" } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-23T05:47:57.323Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:47:57.323Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:47:58.484Z", "text": "The ensemble submission is saved and validated: **845,600 rows**, with 100 distinct eligible tracks for each of **8,456 users**. The README and rebuild script are complete. I’m checking one final expanded-shortlist variant; the validated ensemble remains saved.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:47:59.427Z", "callId": "call_iOOhRl9FmLzoQJ4qcgkt1yoM", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/wide_pool.log; tail /app/solution/wide_test.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:47:59.508Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"150536\",\"wall_time_seconds\":0.00000384,\"exit_code\":0,\"original_token_count\":42,\"output\":\"features validation (8456, 800, 47) retrieved positives 14381\\nwide ensemble holdout 0.05753474208173146\\nfeatures test (8456, 800, 47) retrieved positives 0\\n8588738560\\n\"}\n{\"current_time\":\"2026-09-23 05:47:59 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:47:59.476Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9695a4d3-8416-4d25-99b1-39b89ebdb0a2", "process_id": "31460", "command": [ "/bin/sh", "-lc", "tail /app/solution/wide_pool.log; tail /app/solution/wide_test.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/wide_pool.log", "name": "wide_pool.log", "path": "/app/solution/wide_pool.log" }, { "type": "read", "cmd": "tail /app/solution/wide_test.log", "name": "wide_test.log", "path": "/app/solution/wide_test.log" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features validation (8456, 800, 47) retrieved positives 14381\nwide ensemble holdout 0.05753474208173146\nfeatures test (8456, 800, 47) retrieved positives 0\n8588738560\n", "stderr": "", "aggregated_output": "features validation (8456, 800, 47) retrieved positives 14381\nwide ensemble holdout 0.05753474208173146\nfeatures test (8456, 800, 47) retrieved positives 0\n8588738560\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3840 }, "formatted_output": "features validation (8456, 800, 47) retrieved positives 14381\nwide ensemble holdout 0.05753474208173146\nfeatures test (8456, 800, 47) retrieved positives 0\n8588738560\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:47:59.505Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab367e350a087d2ae5e3dbe7ea3a46c", "usage": { "input_tokens": 80074, "cached_input_tokens": 79232, "cache_write_input_tokens": 0, "output_tokens": 1143, "reasoning_output_tokens": 1016, "total_tokens": 81217 }, "turn_token_usage": { "input_tokens": 3240158, "cached_input_tokens": 3135104, "cache_write_input_tokens": 0, "output_tokens": 43710, "reasoning_output_tokens": 21408, "total_tokens": 3283868 }, "thread_token_usage": { "input_tokens": 3240158, "cached_input_tokens": 3135104, "cache_write_input_tokens": 0, "output_tokens": 43710, "reasoning_output_tokens": 21408, "total_tokens": 3283868 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-23T05:48:16.837Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:16.837Z", "text": "" } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-23T05:48:25.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:25.152Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:48:37.450Z", "callId": "call_GgKfqWuBQLTEeHo4WqbQsB8q", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/build.py');s=p.read_text();s += \\\"\\\\nrun('wide_pool.py','test')\\\\nrun('validate_submission.py',P/'submission_wide.parquet')\\\\nshutil.copyfile(P/'submission_wide.parquet',P/'submission.parquet')\\\\n\\\";p.write_text(s)\\np=Path('/app/solution/README.md');s=p.read_text();s=s.replace('The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for this cross-fitted ranker.', 'The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score.');s=s.replace('`final_augmented.py` creates the final ensemble.', '`final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking.');p.write_text(s)\\nimport json\\np=Path('/app/solution/metrics.json');d=json.loads(p.read_text());d.update(final_shortlist_size=800,wide_holdout_ndcg_at_10=0.05753474208173146,compact_holdout_ndcg_at_10=0.05710146909655971);p.write_text(json.dumps(d,indent=2))\\nPY\\ntail /app/solution/wide_test.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:48:37.631Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"cf208d\",\"wall_time_seconds\":0.00000295,\"exit_code\":0,\"original_token_count\":13,\"output\":\"features test (8456, 800, 47) retrieved positives 0\\n\"}\n{\"current_time\":\"2026-09-23 05:48:37 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:48:37.556Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3680fdb3487d28b24a605671fb387", "usage": { "input_tokens": 81343, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 1207, "reasoning_output_tokens": 787, "total_tokens": 82550 }, "turn_token_usage": { "input_tokens": 3321501, "cached_input_tokens": 3214976, "cache_write_input_tokens": 0, "output_tokens": 44917, "reasoning_output_tokens": 22195, "total_tokens": 3366418 }, "thread_token_usage": { "input_tokens": 3321501, "cached_input_tokens": 3214976, "cache_write_input_tokens": 0, "output_tokens": 44917, "reasoning_output_tokens": 22195, "total_tokens": 3366418 } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:37.613Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ea6f7df-6f61-40a3-9340-01e27f1d2541", "process_id": "21870", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s += \"\\nrun('wide_pool.py','test')\\nrun('validate_submission.py',P/'submission_wide.parquet')\\nshutil.copyfile(P/'submission_wide.parquet',P/'submission.parquet')\\n\";p.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text();s=s.replace('The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for this cross-fitted ranker.', 'The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score.');s=s.replace('`final_augmented.py` creates the final ensemble.', '`final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking.');p.write_text(s)\nimport json\np=Path('/app/solution/metrics.json');d=json.loads(p.read_text());d.update(final_shortlist_size=800,wide_holdout_ndcg_at_10=0.05753474208173146,compact_holdout_ndcg_at_10=0.05710146909655971);p.write_text(json.dumps(d,indent=2))\nPY\ntail /app/solution/wide_test.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/build.py');s=p.read_text();s += \"\\nrun('wide_pool.py','test')\\nrun('validate_submission.py',P/'submission_wide.parquet')\\nshutil.copyfile(P/'submission_wide.parquet',P/'submission.parquet')\\n\";p.write_text(s)\np=Path('/app/solution/README.md');s=p.read_text();s=s.replace('The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for this cross-fitted ranker.', 'The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score.');s=s.replace('`final_augmented.py` creates the final ensemble.', '`final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking.');p.write_text(s)\nimport json\np=Path('/app/solution/metrics.json');d=json.loads(p.read_text());d.update(final_shortlist_size=800,wide_holdout_ndcg_at_10=0.05753474208173146,compact_holdout_ndcg_at_10=0.05710146909655971);p.write_text(json.dumps(d,indent=2))\nPY\ntail /app/solution/wide_test.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features test (8456, 800, 47) retrieved positives 0\n", "stderr": "", "aggregated_output": "features test (8456, 800, 47) retrieved positives 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2950 }, "formatted_output": "features test (8456, 800, 47) retrieved positives 0\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:48:56.329Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c7e31edf-4757-4e8f-8b83-626011e288b6", "process_id": "50483", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py test > /app/solution/wide_test.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/wide_pool.py test > /app/solution/wide_test.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 122, "nanos": 916840699 }, "formatted_output": "" } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-23T05:48:57.632Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:48:57.632Z", "text": "" } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-23T05:49:18.233Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:18.233Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:49:19.198Z", "callId": "call_mtuhatFAeXzs5PSVwxCckiee", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/wide_test.log; ls -l /app/solution/submission_wide.parquet\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-23T05:49:19.278Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"8fe3b1\",\"wall_time_seconds\":0.000004089,\"exit_code\":0,\"original_token_count\":40,\"output\":\"features test (8456, 800, 47) retrieved positives 0\\nwide submission written\\n-rw-r--r-- 1 agent agent 1785990 Sep 23 05:48 /app/solution/submission_wide.parquet\\n\"}\n{\"current_time\":\"2026-09-23 05:49:19 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:19.243Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b1988804-0ce4-4173-90c6-082e4425b59d", "process_id": "96305", "command": [ "/bin/sh", "-lc", "tail /app/solution/wide_test.log; ls -l /app/solution/submission_wide.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "tail /app/solution/wide_test.log", "name": "wide_test.log", "path": "/app/solution/wide_test.log" }, { "type": "list_files", "cmd": "ls -l /app/solution/submission_wide.parquet", "path": "submission_wide.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "features test (8456, 800, 47) retrieved positives 0\nwide submission written\n-rw-r--r-- 1 agent agent 1785990 Sep 23 05:48 /app/solution/submission_wide.parquet\n", "stderr": "", "aggregated_output": "features test (8456, 800, 47) retrieved positives 0\nwide submission written\n-rw-r--r-- 1 agent agent 1785990 Sep 23 05:48 /app/solution/submission_wide.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4089 }, "formatted_output": "features test (8456, 800, 47) retrieved positives 0\nwide submission written\n-rw-r--r-- 1 agent agent 1785990 Sep 23 05:48 /app/solution/submission_wide.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:19.276Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36835ddb887d29e46061d379ca636", "usage": { "input_tokens": 82640, "cached_input_tokens": 81152, "cache_write_input_tokens": 0, "output_tokens": 1091, "reasoning_output_tokens": 1031, "total_tokens": 83731 }, "turn_token_usage": { "input_tokens": 3404141, "cached_input_tokens": 3296128, "cache_write_input_tokens": 0, "output_tokens": 46008, "reasoning_output_tokens": 23226, "total_tokens": 3450149 }, "thread_token_usage": { "input_tokens": 3404141, "cached_input_tokens": 3296128, "cache_write_input_tokens": 0, "output_tokens": 46008, "reasoning_output_tokens": 23226, "total_tokens": 3450149 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-23T05:49:25.227Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:25.227Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T05:49:26.718Z", "text": "The expanded shortlist gave a small held-out improvement, so I’m selecting it. Its final ranking is generated; I’m running the last eligibility check before saving it as `submission.parquet`.\n" }, { "kind": "tool", "timestamp": "2026-09-23T05:49:34.410Z", "callId": "call_jm3Tmly1Sgg0gB0CDogRKmKy", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_wide.parquet && cp /app/solution/submission_wide.parquet /app/solution/submission.parquet && cp /app/solution/validation_ensemble.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport ast,json\\np=Path('/app/solution')\\nfor name in ['build.py','wide_pool.py','final_augmented.py','finalize_reranker.py','rerank.py']:ast.parse((p/name).read_text())\\nprint('Build sources parse; final model files:',(p/'final_reranker_base.joblib').exists(),(p/'final_reranker_augmented.joblib').exists())\\nprint((p/'README.md').read_text())\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-23T05:49:35.769Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"e019f2\",\"wall_time_seconds\":1.001284033,\"session_id\":59442,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"261aa3\",\"wall_time_seconds\":0.00000443,\"exit_code\":0,\"original_token_count\":622,\"output\":\"Build sources parse; final model files: True True\\n# Music discovery ranker\\n\\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\\n\\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\\n\\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\\n\\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\\n\\nRebuild from the provided inputs, without installing anything:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/build.py\\n```\\n\\nCheck the final artifact:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/validate_submission.py\\n```\\n\\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\\n\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:34.534Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3685f866c87d29fedf7d4d96f2dce", "usage": { "input_tokens": 83859, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 326, "reasoning_output_tokens": 17, "total_tokens": 84185 }, "turn_token_usage": { "input_tokens": 3488000, "cached_input_tokens": 3378560, "cache_write_input_tokens": 0, "output_tokens": 46334, "reasoning_output_tokens": 23243, "total_tokens": 3534334 }, "thread_token_usage": { "input_tokens": 3488000, "cached_input_tokens": 3378560, "cache_write_input_tokens": 0, "output_tokens": 46334, "reasoning_output_tokens": 23243, "total_tokens": 3534334 } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:35.755Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31716359-9177-4751-b87a-8d352020e3a0", "process_id": "76836", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport ast,json\np=Path('/app/solution')\nfor name in ['build.py','wide_pool.py','final_augmented.py','finalize_reranker.py','rerank.py']:ast.parse((p/name).read_text())\nprint('Build sources parse; final model files:',(p/'final_reranker_base.joblib').exists(),(p/'final_reranker_augmented.joblib').exists())\nprint((p/'README.md').read_text())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport ast,json\np=Path('/app/solution')\nfor name in ['build.py','wide_pool.py','final_augmented.py','finalize_reranker.py','rerank.py']:ast.parse((p/name).read_text())\nprint('Build sources parse; final model files:',(p/'final_reranker_base.joblib').exists(),(p/'final_reranker_augmented.joblib').exists())\nprint((p/'README.md').read_text())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n", "stderr": "", "aggregated_output": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4430 }, "formatted_output": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:36.952Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-951252ea-d538-4979-8cbd-6c37b9f80724", "process_id": "59442", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_wide.parquet && cp /app/solution/submission_wide.parquet /app/solution/submission.parquet && cp /app/solution/validation_ensemble.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/validate_submission.py /app/solution/submission_wide.parquet && cp /app/solution/submission_wide.parquet /app/solution/submission.parquet && cp /app/solution/validation_ensemble.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/validate_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\nVALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\nVALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 358078831 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\nVALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n" } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-23T05:49:52.341Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T05:49:52.341Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T05:49:53.448Z", "callId": "call_uHWnFsQiMxZCbCzXXIaEOt7V", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:59442,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build.py\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-23T05:49:53.539Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"9dfda7\",\"wall_time_seconds\":0.00000226,\"exit_code\":0,\"original_token_count\":34,\"output\":\"VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\\nVALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\\n\"}\n{\"chunk_id\":\"553b9e\",\"wall_time_seconds\":0.00000339,\"exit_code\":0,\"original_token_count\":53,\"output\":\"-rw-r--r-- 1 agent agent 2.4K Sep 23 05:48 /app/solution/README.md\\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:48 /app/solution/build.py\\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:49 /app/solution/submission.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T05:49:53.492Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a6102f4-5142-4fd1-8c74-52fb19c80f6b", "process_id": "74451", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build.py", "path": "submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 2.4K Sep 23 05:48 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:48 /app/solution/build.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:49 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 2.4K Sep 23 05:48 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:48 /app/solution/build.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:49 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3390 }, "formatted_output": "-rw-r--r-- 1 agent agent 2.4K Sep 23 05:48 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 23 05:48 /app/solution/build.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 05:49 /app/solution/submission.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T05:49:53.537Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab3686ffa6087d29c134dfed52a0d7a", "usage": { "input_tokens": 84794, "cached_input_tokens": 83712, "cache_write_input_tokens": 0, "output_tokens": 519, "reasoning_output_tokens": 437, "total_tokens": 85313 }, "turn_token_usage": { "input_tokens": 3572794, "cached_input_tokens": 3462272, "cache_write_input_tokens": 0, "output_tokens": 46853, "reasoning_output_tokens": 23680, "total_tokens": 3619647 }, "thread_token_usage": { "input_tokens": 3572794, "cached_input_tokens": 3462272, "cache_write_input_tokens": 0, "output_tokens": 46853, "reasoning_output_tokens": 23680, "total_tokens": 3619647 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-23T05:50:00.074Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T05:50:00.074Z", "text": "Saved [submission.parquet](/app/solution/submission.parquet), with 100 unique eligible tracks for all 8,456 users. All structural and eligibility checks passed.\n\nReproducible code and validation results are documented in [README.md](/app/solution/README.md)." }, { "kind": "context", "timestamp": "2026-09-23T05:50:00.143Z", "data": { "type": "token_usage_record", "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d", "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994", "response_id": "resp_0b6a1f754489cb4e016ab36881d46c87d2a841aefcfc6f284d", "usage": { "input_tokens": 85536, "cached_input_tokens": 84608, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 0, "total_tokens": 85599 }, "turn_token_usage": { "input_tokens": 3658330, "cached_input_tokens": 3546880, "cache_write_input_tokens": 0, "output_tokens": 46916, "reasoning_output_tokens": 23680, "total_tokens": 3705246 }, "thread_token_usage": { "input_tokens": 3658330, "cached_input_tokens": 3546880, "cache_write_input_tokens": 0, "output_tokens": 46916, "reasoning_output_tokens": 23680, "total_tokens": 3705246 } } } ] } ] }