yambda-discovery-ranking-v01 / base-astra-high-cli-0-153-4-r01-20260923T050949Z Recorded model: gpt-6-astra ## Step 1 · system · 2026-09-23T05:20:42.292Z 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. # When to ask the user for permission Use 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. User 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. You 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. Do 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. The 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. # Autonomy and persistence The 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. When 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. When 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. If 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. Do 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. # Personality As 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. ## Writing style Your 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. Use 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:...". Include 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. Default 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. Avoid 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. State 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. ## Technical communication In 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. Lead 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. Present 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. ### Writing PR descriptions Lead 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. Describe 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. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. When 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. The 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. When 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. Compaction 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. ## Intermediate commentary As 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. If 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. Do 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. Never 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 ". ## Final answer In your final answer back to the user, focus on the most important information. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. If 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. ### Visualizations Use 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. For 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. Use 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. Usually 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. # Rules for getting work done - 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. - 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. - 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. - 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. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - 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. - Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk. - Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision - 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. - 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. # Using skills A 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". Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. The 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. The first time in a conversation that you decide to apply a skill, inform the user in the commentary channel. If 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. ## When to use a skill If the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why. If your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill. ## How to use skills Open and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling `skills.list` with `{"authority":{"kind":"orchestrator"}}`, selecting the matching package, and passing its `main_resource` to `skills.read`. Avoid re-reading skills when possible. When a `SKILL.md` file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. # Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{{connector_id}})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An 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. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. # Plugins A plugin is a local bundle of skills, MCP servers, and apps. ## How to use plugins - Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list. - 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. - Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn. - Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task. - 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. - 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. ## Step 2 · developer · 2026-09-23T05:20:43.524Z ## Skills A 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. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - 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) - 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) - 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) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - 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) Filesystem 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. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. # Collaboration Mode: Default You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active. Your 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. ## request_user_input availability Use the `request_user_input` tool only when it is listed in the available tools for this turn. Use the `request_user_input` tool only for optional questions where the answer would materially improve the quality of the work. If `request_user_input` returns no answers, continue with best judgment instead of asking again or treating the turn as blocked. Never use the `request_user_input` tool for permission requests or permission-related escalations. ## Step 3 · developer · 2026-09-23T05:20:43.524Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All 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. You 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. `send_message` calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note 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. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-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. ## Step 4 · developer · 2026-09-23T05:20:43.525Z 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. ## Step 5 · user · 2026-09-23T05:20:43.525Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-23 Etc/UTC /app ### Harness context { "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 } } } ### Harness context { "type": "turn_context", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "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" } ## Step 6 · user · 2026-09-23T05:20:43.543Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-23T05:20:46.331Z I’ll inspect the data and evaluator, save a valid baseline, then use the public window to tune the ranker before refitting on all observed history. ### exec · 2026-09-23T05:20:48.080Z { "code": "text(await tools.exec_command({cmd:\"ls -lah /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py\",\"max_output_tokens\":8000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:20:48.150Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"744fd2","wall_time_seconds":0.00000368,"exit_code":0,"original_token_count":2672,"output":"total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:15 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:18 ..\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"} {"current_time":"2026-09-23 05:20:48 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361abb61887d28b3ee2c2006d5c4e", "usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 0, "total_tokens": 15509 }, "turn_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 0, "total_tokens": 15509 }, "thread_token_usage": { "input_tokens": 15406, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 0, "total_tokens": 15509 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4bc14903-1797-4d8b-860f-22c91e35edf8", "process_id": "10975", "command": [ "/bin/sh", "-lc", "ls -lah /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lah /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\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:15 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:18 ..\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\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:15 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:18 ..\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": 3680 }, "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:15 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 05:18 ..\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" } } ### exec · 2026-09-23T05:20:56.468Z { "code": "text(await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema); print(next(pf.iter_batches(batch_size=3)).to_pandas())\\nfor s in ['validation','test']:\\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]); print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\\nprint('cpu',os.cpu_count())\\nPY\\ncat /task/evaluate_public.py\",\"max_output_tokens\":6000}));\n" } ### Tool output · 2026-09-23T05:20:56.529Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"af39e7","wall_time_seconds":0.00000625,"exit_code":0,"original_token_count":1474,"output":"Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6632bef8-a1fd-48d8-b170-fd98e9a4519d", "process_id": "26910", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema); print(next(pf.iter_batches(batch_size=3)).to_pandas())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]); print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY\ncat /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema); print(next(pf.iter_batches(batch_size=3)).to_pandas())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]); print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY\ncat /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6250 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361b0833887d299d1798decf8ab75", "usage": { "input_tokens": 18835, "cached_input_tokens": 15232, "cache_write_input_tokens": 0, "output_tokens": 231, "reasoning_output_tokens": 0, "total_tokens": 19066 }, "turn_token_usage": { "input_tokens": 34241, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 0, "total_tokens": 34575 }, "thread_token_usage": { "input_tokens": 34241, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 0, "total_tokens": 34575 } } ### exec · 2026-09-23T05:20:59.267Z { "code": "text(await tools.exec_command({cmd:\"which python python3; ls -la /app /opt /usr/local/bin; printenv PATH\",\"max_output_tokens\":3000}));\n" } ### Tool output · 2026-09-23T05:20:59.349Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c13b6f","wall_time_seconds":0.0000039,"exit_code":0,"original_token_count":442,"output":"/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 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\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\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 53 Sep 23 05:18 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n/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\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bf3eab11-af79-4af4-9f49-26688984f22a", "process_id": "53301", "command": [ "/bin/sh", "-lc", "which python python3; ls -la /app /opt /usr/local/bin; printenv PATH" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python python3; ls -la /app /opt /usr/local/bin; printenv PATH" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 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\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\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 53 Sep 23 05:18 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n/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\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 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\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\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 53 Sep 23 05:18 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n/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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3900 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 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\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\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root 53 Sep 23 05:18 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12\nlrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3\n-rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12\nlrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3\nlrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12\n-rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config\n/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\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361b8bdac87d295c41ac82093c041", "usage": { "input_tokens": 20638, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 20684 }, "turn_token_usage": { "input_tokens": 54879, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 380, "reasoning_output_tokens": 0, "total_tokens": 55259 }, "thread_token_usage": { "input_tokens": 54879, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 380, "reasoning_output_tokens": 0, "total_tokens": 55259 } } ### exec · 2026-09-23T05:21:07.644Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,os\\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\\n pf=pq.ParquetFile('/task/data/'+f+'.parquet'); print(f,pf.metadata.num_rows);print(next(pf.iter_batches(batch_size=3)).to_pandas().to_string(index=False))\\nfor s in ['validation','test']:\\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\\nprint('cpu',os.cpu_count())\\nPY\",\"max_output_tokens\":3000}));\n" } ### Tool output · 2026-09-23T05:21:08.991Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"cfcf9d","wall_time_seconds":1.179036042,"exit_code":0,"original_token_count":423,"output":"target_users 8456\n uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\nvalidation_preference_state 469124\n uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\ntrain_listens 43484159\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\ntrain_likes 814831\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\nval_listens 1501697\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 24943430 9321269 0 100 195\n 100 24943605 5452713 0 100 175\n 100 24943765 6448777 0 100 160\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24552\ncpu 8\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361bba86887d296264ee426230c93", "usage": { "input_tokens": 21557, "cached_input_tokens": 20480, "cache_write_input_tokens": 0, "output_tokens": 221, "reasoning_output_tokens": 0, "total_tokens": 21778 }, "turn_token_usage": { "input_tokens": 76436, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 601, "reasoning_output_tokens": 0, "total_tokens": 77037 }, "thread_token_usage": { "input_tokens": 76436, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 601, "reasoning_output_tokens": 0, "total_tokens": 77037 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b848c02-bdea-4416-a015-a6ce3be14211", "process_id": "35457", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,os\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\n pf=pq.ParquetFile('/task/data/'+f+'.parquet'); print(f,pf.metadata.num_rows);print(next(pf.iter_batches(batch_size=3)).to_pandas().to_string(index=False))\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,os\nfor f in ['target_users','candidate_catalog','validation_preference_state','train_listens','train_likes','val_listens']:\n pf=pq.ParquetFile('/task/data/'+f+'.parquet'); print(f,pf.metadata.num_rows);print(next(pf.iter_batches(batch_size=3)).to_pandas().to_string(index=False))\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print('elig',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target_users 8456\n uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\nvalidation_preference_state 469124\n uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\ntrain_listens 43484159\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\ntrain_likes 814831\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\nval_listens 1501697\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 24943430 9321269 0 100 195\n 100 24943605 5452713 0 100 175\n 100 24943765 6448777 0 100 160\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24552\ncpu 8\n", "stderr": "", "aggregated_output": "target_users 8456\n uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\nvalidation_preference_state 469124\n uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\ntrain_listens 43484159\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\ntrain_likes 814831\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\nval_listens 1501697\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 24943430 9321269 0 100 195\n 100 24943605 5452713 0 100 175\n 100 24943765 6448777 0 100 160\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24552\ncpu 8\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 179024921 }, "formatted_output": "target_users 8456\n uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\ncandidate_catalog 30000\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\nvalidation_preference_state 469124\n uid item_id state\n 100 410868 liked\n 100 732449 liked\n 100 982903 liked\ntrain_listens 43484159\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 39420 8326270 0 100 170\n 100 39420 1441281 0 100 105\n 100 39625 286361 0 100 185\ntrain_likes 814831\n uid timestamp item_id is_organic\n 100 44755 732449 1\n 100 1155860 6568592 0\n 100 1259125 5411243 1\nval_listens 1501697\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n 100 24943430 9321269 0 100 195\n 100 24943605 5452713 0 100 175\n 100 24943765 6448777 0 100 160\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\nelig 24552\ncpu 8\n" } } ## Step 8 · agent · 2026-09-23T05:21:15.489Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:21.867Z { "code": "text(await tools.exec_command({cmd:\"cat /proc/meminfo | head -4; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nx=pd.read_parquet('/task/data/train_likes.parquet');print('users likes',x.uid.nunique(), x.timestamp.max());a=np.load('/task/data/validation_eligible_candidates.npz');u=pd.Index(a['uids']);i=pd.Index(a['item_ids']);r=u.get_indexer(x.uid);c=i.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];print('like eligible',np.mean((a['packed_eligible'][r,c//8]>>(c%8))&1));print(pd.read_parquet('/task/data/validation_preference_state.parquet').state.value_counts())\\nPY\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:21:22.802Z Script completed Wall time 0.9 seconds Output: {"chunk_id":"cbe06d","wall_time_seconds":0.766482559,"exit_code":0,"original_token_count":63,"output":"MemTotal: 15988972 kB\nMemFree: 5825936 kB\nMemAvailable: 15029496 kB\nBuffers: 176396 kB\n8589934592\n400000 100000\nusers likes 8144 24786790\nlike eligible 0.0\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361c5219487d2b50fc38da0694db0", "usage": { "input_tokens": 22321, "cached_input_tokens": 21376, "cache_write_input_tokens": 0, "output_tokens": 371, "reasoning_output_tokens": 146, "total_tokens": 22692 }, "turn_token_usage": { "input_tokens": 98757, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 972, "reasoning_output_tokens": 146, "total_tokens": 99729 }, "thread_token_usage": { "input_tokens": 98757, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 972, "reasoning_output_tokens": 146, "total_tokens": 99729 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5e58ebdb-e655-411b-8ced-e6ed657d3b67", "process_id": "64044", "command": [ "/bin/sh", "-lc", "cat /proc/meminfo | head -4; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nx=pd.read_parquet('/task/data/train_likes.parquet');print('users likes',x.uid.nunique(), x.timestamp.max());a=np.load('/task/data/validation_eligible_candidates.npz');u=pd.Index(a['uids']);i=pd.Index(a['item_ids']);r=u.get_indexer(x.uid);c=i.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];print('like eligible',np.mean((a['packed_eligible'][r,c//8]>>(c%8))&1));print(pd.read_parquet('/task/data/validation_preference_state.parquet').state.value_counts())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /proc/meminfo | head -4; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nx=pd.read_parquet('/task/data/train_likes.parquet');print('users likes',x.uid.nunique(), x.timestamp.max());a=np.load('/task/data/validation_eligible_candidates.npz');u=pd.Index(a['uids']);i=pd.Index(a['item_ids']);r=u.get_indexer(x.uid);c=i.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];print('like eligible',np.mean((a['packed_eligible'][r,c//8]>>(c%8))&1));print(pd.read_parquet('/task/data/validation_preference_state.parquet').state.value_counts())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users likes 8144 24786790\nlike eligible 0.0\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n", "stderr": "", "aggregated_output": "users likes 8144 24786790\nlike eligible 0.0\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 766452057 }, "formatted_output": "users likes 8144 24786790\nlike eligible 0.0\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n" } } ## Step 9 · agent · 2026-09-23T05:21:30.989Z ### Thinking unavailable — no text recorded There are 8,456 target users and 30,000 catalog tracks. The eligibility masks exclude all historical likes I checked, so I’ll start with collaborative models that can find unfamiliar tracks from users’ listening patterns and recent activity. ### exec · 2026-09-23T05:21:59.988Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport numpy as np,pandas as pd, scipy.sparse as sp,pyarrow.parquet as pq,time\\nfrom pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());items=np.load(D/'test_eligible_candidates.npz')['item_ids'];np.savez(O/'ids.npz',uids=uids,items=items)\\nU=pd.Index(uids);I=pd.Index(items);shape=(len(U),len(I));print('shape',shape,flush=True)\\nfor stage in ['train','val']:\\n cutoff=24786800 if stage=='train' else 24786800+1800+604800\\n mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes']}\\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=1500000):\\n x=batch.to_pandas();c=I.get_indexer(x.item_id);m=(c>=0)&(x.played_ratio_pct.values>=50);x=x[m];c=c[m];r=U.get_indexer(x.uid);age=(cutoff-x.timestamp.values)/86400;org=x.is_organic.values.astype(np.float32);strong=np.where(x.played_ratio_pct.values>=80,1,.5).astype(np.float32)\\n for k,w in [('count',np.ones(len(r),np.float32)),('organic',org*strong),('recent',np.exp2(-age/30)*strong*(.4+.6*org)),('recentorg',np.exp2(-age/30)*strong*org),('short',np.exp2(-age/7)*strong*(.4+.6*org))]:\\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\\n print(stage,'batch',round(time.time()-t),flush=True)\\n x=pd.read_parquet(D/f'{stage}_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);m=(c>=0)&(r>=0);mats['likes']=sp.coo_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=shape).tocsr()\\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{stage}_{k}.npz',v)\\n print(stage,'done',mats['count'].nnz,round(time.time()-t),flush=True)\\n# popular valid fallback\\nfor stage in ['validation','test']:\\n a=np.load(D/f'{stage}_eligible_candidates.npz');p=np.asarray(sp.load_npz(O/'train_short.npz').sum(axis=0)).ravel()\\n if stage=='test':p=p*.5+np.asarray(sp.load_npz(O/'val_short.npz').sum(axis=0)).ravel()\\n rows=[]\\n for r,uid in enumerate(a['uids']):\\n mask=np.unpackbits(a['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(mask,p,-np.inf);ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix])];rows.append(ix)\\n ix=np.array(rows);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':items[ix.ravel()],'rank':np.tile(np.arange(1,101),len(rows))}).to_parquet(O/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\\nprint('saved baseline',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,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:22:01.156Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"de87d2","wall_time_seconds":1.001121084,"session_id":33365,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361d2f6e887d2a04432ba652530ef", "usage": { "input_tokens": 22843, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 1201, "reasoning_output_tokens": 230, "total_tokens": 24044 }, "turn_token_usage": { "input_tokens": 121600, "cached_input_tokens": 110080, "cache_write_input_tokens": 0, "output_tokens": 2173, "reasoning_output_tokens": 376, "total_tokens": 123773 }, "thread_token_usage": { "input_tokens": 121600, "cached_input_tokens": 110080, "cache_write_input_tokens": 0, "output_tokens": 2173, "reasoning_output_tokens": 376, "total_tokens": 123773 } } ## Step 10 · agent · 2026-09-23T05:22:12.615Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:13.697Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/prepare.log; ls -lh /app/solution\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:22:13.750Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"3ee2db","wall_time_seconds":0.000004381,"exit_code":0,"original_token_count":109,"output":"train batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntotal 20M\n-rw-r--r-- 1 agent agent 154K Sep 23 05:22 ids.npz\n-rw-r--r-- 1 agent agent 426 Sep 23 05:22 prepare.log\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:22 prepare.py\n-rw-r--r-- 1 agent agent 14M Sep 23 05:22 train_count.npz\n-rw-r--r-- 1 agent agent 5.6M Sep 23 05:22 train_organic.npz\n-rw-r--r-- 1 agent agent 0 Sep 23 05:22 train_recent.npz\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-15a7e21b-ca19-4639-a1c3-e6d2dd7c9415", "process_id": "95375", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/prepare.log; ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/prepare.log; ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntotal 20M\n-rw-r--r-- 1 agent agent 154K Sep 23 05:22 ids.npz\n-rw-r--r-- 1 agent agent 426 Sep 23 05:22 prepare.log\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:22 prepare.py\n-rw-r--r-- 1 agent agent 14M Sep 23 05:22 train_count.npz\n-rw-r--r-- 1 agent agent 5.6M Sep 23 05:22 train_organic.npz\n-rw-r--r-- 1 agent agent 0 Sep 23 05:22 train_recent.npz\n", "stderr": "", "aggregated_output": "train batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntotal 20M\n-rw-r--r-- 1 agent agent 154K Sep 23 05:22 ids.npz\n-rw-r--r-- 1 agent agent 426 Sep 23 05:22 prepare.log\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:22 prepare.py\n-rw-r--r-- 1 agent agent 14M Sep 23 05:22 train_count.npz\n-rw-r--r-- 1 agent agent 5.6M Sep 23 05:22 train_organic.npz\n-rw-r--r-- 1 agent agent 0 Sep 23 05:22 train_recent.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4381 }, "formatted_output": "train batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntotal 20M\n-rw-r--r-- 1 agent agent 154K Sep 23 05:22 ids.npz\n-rw-r--r-- 1 agent agent 426 Sep 23 05:22 prepare.log\n-rw-r--r-- 1 agent agent 2.5K Sep 23 05:22 prepare.py\n-rw-r--r-- 1 agent agent 14M Sep 23 05:22 train_count.npz\n-rw-r--r-- 1 agent agent 5.6M Sep 23 05:22 train_organic.npz\n-rw-r--r-- 1 agent agent 0 Sep 23 05:22 train_recent.npz\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab361f954ec87d280bfb92051ffdcbe", "usage": { "input_tokens": 24102, "cached_input_tokens": 22656, "cache_write_input_tokens": 0, "output_tokens": 383, "reasoning_output_tokens": 337, "total_tokens": 24485 }, "turn_token_usage": { "input_tokens": 145702, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 2556, "reasoning_output_tokens": 713, "total_tokens": 148258 }, "thread_token_usage": { "input_tokens": 145702, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 2556, "reasoning_output_tokens": 713, "total_tokens": 148258 } } ## Step 11 · agent · 2026-09-23T05:22:20.817Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:22.563Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/prepare.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:22:23.731Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fd2a83","wall_time_seconds":1.001555482,"session_id":6657,"original_token_count":21,"output":"train batch 8\ntrain done 5564926 19\nval batch 19\nval batch 19\nval done 485745 20\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36206031487d2908c52c6caba9227", "usage": { "input_tokens": 24728, "cached_input_tokens": 23936, "cache_write_input_tokens": 0, "output_tokens": 247, "reasoning_output_tokens": 179, "total_tokens": 24975 }, "turn_token_usage": { "input_tokens": 170430, "cached_input_tokens": 156672, "cache_write_input_tokens": 0, "output_tokens": 2803, "reasoning_output_tokens": 892, "total_tokens": 173233 }, "thread_token_usage": { "input_tokens": 170430, "cached_input_tokens": 156672, "cache_write_input_tokens": 0, "output_tokens": 2803, "reasoning_output_tokens": 892, "total_tokens": 173233 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1fd3f36f-c471-46a0-b53f-9ffcd584a9e0", "process_id": "6657", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/prepare.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/prepare.log; /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": 161431701 }, "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" } } ### exec · 2026-09-23T05:23:10.872Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/common.py <<'PY'\\nimport numpy as np,pandas as pd,scipy.sparse as sp\\nfrom pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution')\\na=np.load(O/'ids.npz');uids=a['uids'];items=a['items'];U=pd.Index(uids);I=pd.Index(items)\\ndef matrix(k,stage='validation'):\\n x=sp.load_npz(O/f'train_{k}.npz')\\n if stage=='test':\\n decay={'recent':30,'recentorg':30,'short':7}.get(k)\\n if decay:x=x*2**(-(7+1800/86400)/decay)\\n x=x+sp.load_npz(O/f'val_{k}.npz')\\n return x\\n\\ndef profile(stage='validation',kind='base'):\\n x=matrix('count',stage);x.data=np.log1p(x.data)\\n o=matrix('organic',stage);o.data=np.log1p(o.data)\\n l=matrix('likes',stage);l.data[:]=1\\n if kind=='binary':x.data[:]=1;return x+l*1\\n if kind=='organic':return o+l*3\\n return x*.4+o*.6+l*3\\n\\ndef context(stage):\\n a=np.load(D/f'{stage}_eligible_candidates.npz');tr=U.get_indexer(a['uids']);mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\n return a['uids'],tr,mask\\n\\ndef truth():\\n _,tr,mask=context('validation');tu=pd.Index(uids[tr]);ys=[]\\n for name,grade in [('listens',1),('likes',7)]:\\n x=pd.read_parquet(D/f'val_{name}.parquet');m=x.is_organic.values==1\\n if name=='listens':m &= x.played_ratio_pct.values>=80\\n x=x[m];r=tu.get_indexer(x.uid);c=I.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=mask[r,c];z=sp.coo_matrix((np.full(m.sum(),grade,np.float32),(r[m],c[m])),shape=mask.shape).tocsr();z.data[:]=grade;ys.append(z)\\n y=ys[0].maximum(ys[1]);idcg=np.zeros(len(tr));discount=1/np.log2(np.arange(2,12))\\n for r in range(len(tr)):\\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\\n return y,idcg\\nY,IDCG=None,None\\ndef evaluate(ix):\\n global Y,IDCG\\n if Y is None:Y,IDCG=truth()\\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\\n return np.mean(dcg[m]/IDCG[m])\\n\\ndef top(s,k=100):\\n ix=np.argpartition(s,-k,axis=1)[:,-k:];v=np.take_along_axis(s,ix,axis=1);return np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1)\\ndef save(ix,stage,name=None):\\n tu,_,_=context(stage);pd.DataFrame({'uid':np.repeat(tu,100),'item_id':items[ix[:,:100].ravel()],'rank':np.tile(np.arange(1,101),len(tu))}).to_parquet(O/(name or ('submission.parquet' if stage=='test' else 'validation.parquet')),index=False)\\nPY\\ncat > /app/solution/knn.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport time,sys,numpy as np,scipy.sparse as sp\\nfrom common import *\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage);recent=matrix('recent',stage);recent.data=np.log1p(recent.data);short=matrix('short',stage);short.data=np.log1p(short.data)\\np=recent+short*.5+matrix('likes',stage)*.3\\n# asymmetric item neighborhood, inverse user activity weighting\\nfor power in [.5]:\\n z=x.copy();z.data=np.minimum(z.data,3);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+10))@z\\n norms=np.asarray(z.power(2).sum(axis=0)).ravel();rows=[];cols=[];vals=[]\\n for start in range(0,len(items),500):\\n stop=min(start+500,len(items));s=(z[:,start:stop].T@z).toarray();s/=np.maximum(norms[start:stop,None],1e-8)**power*np.maximum(norms[None,:],1e-8)**(1-power);s[np.arange(stop-start),np.arange(start,stop)]=0\\n ix=top(s,200);v=np.take_along_axis(s,ix,axis=1);rows.append(np.repeat(np.arange(start,stop),200));cols.append(ix.ravel());vals.append(v.ravel())\\n if start%5000==0:print('knn',start,round(time.time()-t),flush=True)\\n sim=sp.coo_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(len(items),len(items))).tocsr();sp.save_npz(O/f'{stage}_itemsim.npz',sim)\\n for kind,pred in [('recent',p),('all',x)]:\\n out=np.lib.format.open_memmap(O/f'{stage}_knn_{kind}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n s=(pred[tr[b:b+256]]@sim).toarray();s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\\n ix=np.concatenate(ixs);print(stage,kind,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_knn_{kind}.parquet');out.flush()\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation > /app/solution/knn.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:12.043Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"56fb43","wall_time_seconds":1.000998862,"session_id":90230,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362100d4887d2a6d90b93fc41bee1", "usage": { "input_tokens": 25064, "cached_input_tokens": 24576, "cache_write_input_tokens": 0, "output_tokens": 1524, "reasoning_output_tokens": 0, "total_tokens": 26588 }, "turn_token_usage": { "input_tokens": 195494, "cached_input_tokens": 181248, "cache_write_input_tokens": 0, "output_tokens": 4327, "reasoning_output_tokens": 892, "total_tokens": 199821 }, "thread_token_usage": { "input_tokens": 195494, "cached_input_tokens": 181248, "cache_write_input_tokens": 0, "output_tokens": 4327, "reasoning_output_tokens": 892, "total_tokens": 199821 } } ### exec · 2026-09-23T05:23:15.974Z { "code": "text(await tools.write_stdin({session_id:6657,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"head -2 /app/solution/prepare.log; tail -3 /app/solution/knn.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:16.030Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"73962d","wall_time_seconds":0.00000229,"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"} {"chunk_id":"b130aa","wall_time_seconds":0.00000441,"exit_code":2,"original_token_count":31,"output":"shape (9238, 30000)\ntrain batch 1\nknn 0 3\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17d45fc7-7801-41f0-82a5-8295291fb434", "process_id": "45381", "command": [ "/bin/sh", "-lc", "head -2 /app/solution/prepare.log; tail -3 /app/solution/knn.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "head -2 /app/solution/prepare.log; tail -3 /app/solution/knn.log; ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "shape (9238, 30000)\ntrain batch 1\nknn 0 3\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "shape (9238, 30000)\ntrain batch 1\nknn 0 3\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4410 }, "formatted_output": "shape (9238, 30000)\ntrain batch 1\nknn 0 3\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3624044b487d283a068f824d2d847", "usage": { "input_tokens": 26645, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 0, "total_tokens": 26735 }, "turn_token_usage": { "input_tokens": 222139, "cached_input_tokens": 206080, "cache_write_input_tokens": 0, "output_tokens": 4417, "reasoning_output_tokens": 892, "total_tokens": 226556 }, "thread_token_usage": { "input_tokens": 222139, "cached_input_tokens": 206080, "cache_write_input_tokens": 0, "output_tokens": 4417, "reasoning_output_tokens": 892, "total_tokens": 226556 } } ## Step 12 · agent · 2026-09-23T05:23:20.742Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:22.818Z { "code": "text(await tools.write_stdin({session_id:33365,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -12 /app/solution/prepare.log; tail -5 /app/solution/knn.log\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:27.875Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"00d33d","wall_time_seconds":5.000522333,"session_id":33365,"original_token_count":0,"output":""} {"chunk_id":"4d6063","wall_time_seconds":0.00000417,"exit_code":0,"original_token_count":50,"output":"train batch 6\ntrain batch 6\ntrain batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntrain done 5564926 19\nval batch 19\nval batch 19\nval done 485745 20\nknn 0 3\nknn 5000 13\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36244467487d2b6b0f302c645b553", "usage": { "input_tokens": 27247, "cached_input_tokens": 26496, "cache_write_input_tokens": 0, "output_tokens": 93, "reasoning_output_tokens": 13, "total_tokens": 27340 }, "turn_token_usage": { "input_tokens": 249386, "cached_input_tokens": 232576, "cache_write_input_tokens": 0, "output_tokens": 4510, "reasoning_output_tokens": 905, "total_tokens": 253896 }, "thread_token_usage": { "input_tokens": 249386, "cached_input_tokens": 232576, "cache_write_input_tokens": 0, "output_tokens": 4510, "reasoning_output_tokens": 905, "total_tokens": 253896 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-52cd9b3c-e3f0-4b52-bbc5-7790b7e7f7f0", "process_id": "41577", "command": [ "/bin/sh", "-lc", "tail -12 /app/solution/prepare.log; tail -5 /app/solution/knn.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -12 /app/solution/prepare.log; tail -5 /app/solution/knn.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train batch 6\ntrain batch 6\ntrain batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntrain done 5564926 19\nval batch 19\nval batch 19\nval done 485745 20\nknn 0 3\nknn 5000 13\n", "stderr": "", "aggregated_output": "train batch 6\ntrain batch 6\ntrain batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntrain done 5564926 19\nval batch 19\nval batch 19\nval done 485745 20\nknn 0 3\nknn 5000 13\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4170 }, "formatted_output": "train batch 6\ntrain batch 6\ntrain batch 6\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 7\ntrain batch 8\ntrain done 5564926 19\nval batch 19\nval batch 19\nval done 485745 20\nknn 0 3\nknn 5000 13\n" } } ## Step 13 · agent · 2026-09-23T05:23:30.376Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:36.936Z { "code": "text(await tools.exec_command({cmd:\"kill $(pgrep -f '^/opt/task-venv/bin/python /app/solution/prepare.py$)\\npython - <<'PY'\\np='/app/solution/prepare.py'\\ns=open(p).read().replace(\\\"rows=[]\\\\n for\\\", \\\"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\\\n for\\\").replace(\\\"enumerate(a['uids'])\\\", \\\"enumerate(targetids)\\\").replace(\\\"a['packed_eligible'][r]\\\",\\\"packed[r]\\\")\\nopen(p,'w').write(s)\\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\\\"from common import *\\\\n\\\"+s)\\nPY\\n/opt/task-venv/bin/python /app/solution/baseline.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:37.012Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"5f8c43","wall_time_seconds":0.00000381,"exit_code":2,"original_token_count":14,"output":"/bin/sh: 9: Syntax error: Unterminated quoted string\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-59461bce-0dae-489b-85ad-bce578af66ec", "process_id": "92147", "command": [ "/bin/sh", "-lc", "kill $(pgrep -f '^/opt/task-venv/bin/python /app/solution/prepare.py$)\npython - <<'PY'\np='/app/solution/prepare.py'\ns=open(p).read().replace(\"rows=[]\\n for\", \"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\n for\").replace(\"enumerate(a['uids'])\", \"enumerate(targetids)\").replace(\"a['packed_eligible'][r]\",\"packed[r]\")\nopen(p,'w').write(s)\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\"from common import *\\n\"+s)\nPY\n/opt/task-venv/bin/python /app/solution/baseline.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill $(pgrep -f '^/opt/task-venv/bin/python /app/solution/prepare.py$)\npython - <<'PY'\np='/app/solution/prepare.py'\ns=open(p).read().replace(\"rows=[]\\n for\", \"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\n for\").replace(\"enumerate(a['uids'])\", \"enumerate(targetids)\").replace(\"a['packed_eligible'][r]\",\"packed[r]\")\nopen(p,'w').write(s)\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\"from common import *\\n\"+s)\nPY\n/opt/task-venv/bin/python /app/solution/baseline.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 9: Syntax error: Unterminated quoted string\n", "stderr": "", "aggregated_output": "/bin/sh: 9: Syntax error: Unterminated quoted string\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3810 }, "formatted_output": "/bin/sh: 9: Syntax error: Unterminated quoted string\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36250074487d2bb28ff29a87a3186", "usage": { "input_tokens": 27513, "cached_input_tokens": 27008, "cache_write_input_tokens": 0, "output_tokens": 265, "reasoning_output_tokens": 37, "total_tokens": 27778 }, "turn_token_usage": { "input_tokens": 276899, "cached_input_tokens": 259584, "cache_write_input_tokens": 0, "output_tokens": 4775, "reasoning_output_tokens": 942, "total_tokens": 281674 }, "thread_token_usage": { "input_tokens": 276899, "cached_input_tokens": 259584, "cache_write_input_tokens": 0, "output_tokens": 4775, "reasoning_output_tokens": 942, "total_tokens": 281674 } } ## Step 14 · agent · 2026-09-23T05:23:40.730Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:23:47.218Z { "code": "text(await tools.exec_command({cmd:\"pkill -f '^/opt/task-venv/bin/python /app/solution/prepare.py$'\\npython - <<'PY'\\np='/app/solution/prepare.py'\\ns=open(p).read().replace(\\\"rows=[]\\\\n for\\\", \\\"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\\\n for\\\").replace(\\\"enumerate(a['uids'])\\\", \\\"enumerate(targetids)\\\").replace(\\\"a['packed_eligible'][r]\\\",\\\"packed[r]\\\")\\nopen(p,'w').write(s)\\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\\\"from common import *\\\\n\\\"+s)\\nPY\\n/opt/task-venv/bin/python /app/solution/baseline.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:48.411Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5c2d60","wall_time_seconds":1.001883509,"session_id":63468,"original_token_count":8,"output":"/bin/sh: 1: pkill: not found\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3625949b087d2b7f6f6b9e785ae06", "usage": { "input_tokens": 27851, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 262, "reasoning_output_tokens": 36, "total_tokens": 28113 }, "turn_token_usage": { "input_tokens": 304750, "cached_input_tokens": 286976, "cache_write_input_tokens": 0, "output_tokens": 5037, "reasoning_output_tokens": 978, "total_tokens": 309787 }, "thread_token_usage": { "input_tokens": 304750, "cached_input_tokens": 286976, "cache_write_input_tokens": 0, "output_tokens": 5037, "reasoning_output_tokens": 978, "total_tokens": 309787 } } ## Step 15 · agent · 2026-09-23T05:23:50.502Z ### Thinking unavailable — no text recorded I’m testing an item-neighborhood model using organic listening, likes, and recent activity. I’ll compare it with a regularized collaborative model to see which gives better discovery rankings on the public window. ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5fe703b-efd5-49e7-b75a-6c2fe2d414c1", "process_id": "63468", "command": [ "/bin/sh", "-lc", "pkill -f '^/opt/task-venv/bin/python /app/solution/prepare.py$'\npython - <<'PY'\np='/app/solution/prepare.py'\ns=open(p).read().replace(\"rows=[]\\n for\", \"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\n for\").replace(\"enumerate(a['uids'])\", \"enumerate(targetids)\").replace(\"a['packed_eligible'][r]\",\"packed[r]\")\nopen(p,'w').write(s)\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\"from common import *\\n\"+s)\nPY\n/opt/task-venv/bin/python /app/solution/baseline.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pkill -f '^/opt/task-venv/bin/python /app/solution/prepare.py$'\npython - <<'PY'\np='/app/solution/prepare.py'\ns=open(p).read().replace(\"rows=[]\\n for\", \"rows=[];packed=a['packed_eligible'];targetids=a['uids']\\n for\").replace(\"enumerate(a['uids'])\", \"enumerate(targetids)\").replace(\"a['packed_eligible'][r]\",\"packed[r]\")\nopen(p,'w').write(s)\ns=s[s.index('# popular valid fallback'):];open('/app/solution/baseline.py','w').write(\"from common import *\\n\"+s)\nPY\n/opt/task-venv/bin/python /app/solution/baseline.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved baseline\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025879,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "saved baseline\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025879,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 85357337 }, "formatted_output": "saved baseline\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025879,\n \"quality_users\": 4263\n }\n}\n" } } ### exec · 2026-09-23T05:23:53.644Z { "code": "text(await tools.exec_command({cmd:\"ps -eo pid,args | head -20; tail -6 /app/solution/knn.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:23:53.691Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"014469","wall_time_seconds":0.0000043,"exit_code":0,"original_token_count":44,"output":"/bin/sh: 1: ps: not found\nknn 0 3\nknn 5000 13\nknn 10000 20\nknn 15000 26\nknn 20000 32\nknn 25000 38\n-rw-r--r-- 1 agent agent 634K Sep 23 05:23 /app/solution/submission.parquet\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-03a9be37-4d1d-4665-968f-c2479dfbbf6b", "process_id": "33417", "command": [ "/bin/sh", "-lc", "ps -eo pid,args | head -20; tail -6 /app/solution/knn.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -eo pid,args | head -20; tail -6 /app/solution/knn.log; ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\nknn 0 3\nknn 5000 13\nknn 10000 20\nknn 15000 26\nknn 20000 32\nknn 25000 38\n-rw-r--r-- 1 agent agent 634K Sep 23 05:23 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\nknn 0 3\nknn 5000 13\nknn 10000 20\nknn 15000 26\nknn 20000 32\nknn 25000 38\n-rw-r--r-- 1 agent agent 634K Sep 23 05:23 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4300 }, "formatted_output": "/bin/sh: 1: ps: not found\nknn 0 3\nknn 5000 13\nknn 10000 20\nknn 15000 26\nknn 20000 32\nknn 25000 38\n-rw-r--r-- 1 agent agent 634K Sep 23 05:23 /app/solution/submission.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36264932487d294ba5707a6b5137c", "usage": { "input_tokens": 28184, "cached_input_tokens": 27648, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 26, "total_tokens": 28318 }, "turn_token_usage": { "input_tokens": 332934, "cached_input_tokens": 314624, "cache_write_input_tokens": 0, "output_tokens": 5171, "reasoning_output_tokens": 1004, "total_tokens": 338105 }, "thread_token_usage": { "input_tokens": 332934, "cached_input_tokens": 314624, "cache_write_input_tokens": 0, "output_tokens": 5171, "reasoning_output_tokens": 1004, "total_tokens": 338105 } } ## Step 16 · agent · 2026-09-23T05:24:06.915Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9d83c041-f062-41fc-8827-bf41d7518f63", "process_id": "90230", "command": [ "/bin/sh", "-lc", "cat > /app/solution/common.py <<'PY'\nimport numpy as np,pandas as pd,scipy.sparse as sp\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution')\na=np.load(O/'ids.npz');uids=a['uids'];items=a['items'];U=pd.Index(uids);I=pd.Index(items)\ndef matrix(k,stage='validation'):\n x=sp.load_npz(O/f'train_{k}.npz')\n if stage=='test':\n decay={'recent':30,'recentorg':30,'short':7}.get(k)\n if decay:x=x*2**(-(7+1800/86400)/decay)\n x=x+sp.load_npz(O/f'val_{k}.npz')\n return x\n\ndef profile(stage='validation',kind='base'):\n x=matrix('count',stage);x.data=np.log1p(x.data)\n o=matrix('organic',stage);o.data=np.log1p(o.data)\n l=matrix('likes',stage);l.data[:]=1\n if kind=='binary':x.data[:]=1;return x+l*1\n if kind=='organic':return o+l*3\n return x*.4+o*.6+l*3\n\ndef context(stage):\n a=np.load(D/f'{stage}_eligible_candidates.npz');tr=U.get_indexer(a['uids']);mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n return a['uids'],tr,mask\n\ndef truth():\n _,tr,mask=context('validation');tu=pd.Index(uids[tr]);ys=[]\n for name,grade in [('listens',1),('likes',7)]:\n x=pd.read_parquet(D/f'val_{name}.parquet');m=x.is_organic.values==1\n if name=='listens':m &= x.played_ratio_pct.values>=80\n x=x[m];r=tu.get_indexer(x.uid);c=I.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=mask[r,c];z=sp.coo_matrix((np.full(m.sum(),grade,np.float32),(r[m],c[m])),shape=mask.shape).tocsr();z.data[:]=grade;ys.append(z)\n y=ys[0].maximum(ys[1]);idcg=np.zeros(len(tr));discount=1/np.log2(np.arange(2,12))\n for r in range(len(tr)):\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\n return y,idcg\nY,IDCG=None,None\ndef evaluate(ix):\n global Y,IDCG\n if Y is None:Y,IDCG=truth()\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n return np.mean(dcg[m]/IDCG[m])\n\ndef top(s,k=100):\n ix=np.argpartition(s,-k,axis=1)[:,-k:];v=np.take_along_axis(s,ix,axis=1);return np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1)\ndef save(ix,stage,name=None):\n tu,_,_=context(stage);pd.DataFrame({'uid':np.repeat(tu,100),'item_id':items[ix[:,:100].ravel()],'rank':np.tile(np.arange(1,101),len(tu))}).to_parquet(O/(name or ('submission.parquet' if stage=='test' else 'validation.parquet')),index=False)\nPY\ncat > /app/solution/knn.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);recent=matrix('recent',stage);recent.data=np.log1p(recent.data);short=matrix('short',stage);short.data=np.log1p(short.data)\np=recent+short*.5+matrix('likes',stage)*.3\n# asymmetric item neighborhood, inverse user activity weighting\nfor power in [.5]:\n z=x.copy();z.data=np.minimum(z.data,3);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+10))@z\n norms=np.asarray(z.power(2).sum(axis=0)).ravel();rows=[];cols=[];vals=[]\n for start in range(0,len(items),500):\n stop=min(start+500,len(items));s=(z[:,start:stop].T@z).toarray();s/=np.maximum(norms[start:stop,None],1e-8)**power*np.maximum(norms[None,:],1e-8)**(1-power);s[np.arange(stop-start),np.arange(start,stop)]=0\n ix=top(s,200);v=np.take_along_axis(s,ix,axis=1);rows.append(np.repeat(np.arange(start,stop),200));cols.append(ix.ravel());vals.append(v.ravel())\n if start%5000==0:print('knn',start,round(time.time()-t),flush=True)\n sim=sp.coo_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(len(items),len(items))).tocsr();sp.save_npz(O/f'{stage}_itemsim.npz',sim)\n for kind,pred in [('recent',p),('all',x)]:\n out=np.lib.format.open_memmap(O/f'{stage}_knn_{kind}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=(pred[tr[b:b+256]]@sim).toarray();s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\n ix=np.concatenate(ixs);print(stage,kind,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_knn_{kind}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation > /app/solution/knn.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/common.py <<'PY'\nimport numpy as np,pandas as pd,scipy.sparse as sp\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution')\na=np.load(O/'ids.npz');uids=a['uids'];items=a['items'];U=pd.Index(uids);I=pd.Index(items)\ndef matrix(k,stage='validation'):\n x=sp.load_npz(O/f'train_{k}.npz')\n if stage=='test':\n decay={'recent':30,'recentorg':30,'short':7}.get(k)\n if decay:x=x*2**(-(7+1800/86400)/decay)\n x=x+sp.load_npz(O/f'val_{k}.npz')\n return x\n\ndef profile(stage='validation',kind='base'):\n x=matrix('count',stage);x.data=np.log1p(x.data)\n o=matrix('organic',stage);o.data=np.log1p(o.data)\n l=matrix('likes',stage);l.data[:]=1\n if kind=='binary':x.data[:]=1;return x+l*1\n if kind=='organic':return o+l*3\n return x*.4+o*.6+l*3\n\ndef context(stage):\n a=np.load(D/f'{stage}_eligible_candidates.npz');tr=U.get_indexer(a['uids']);mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n return a['uids'],tr,mask\n\ndef truth():\n _,tr,mask=context('validation');tu=pd.Index(uids[tr]);ys=[]\n for name,grade in [('listens',1),('likes',7)]:\n x=pd.read_parquet(D/f'val_{name}.parquet');m=x.is_organic.values==1\n if name=='listens':m &= x.played_ratio_pct.values>=80\n x=x[m];r=tu.get_indexer(x.uid);c=I.get_indexer(x.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=mask[r,c];z=sp.coo_matrix((np.full(m.sum(),grade,np.float32),(r[m],c[m])),shape=mask.shape).tocsr();z.data[:]=grade;ys.append(z)\n y=ys[0].maximum(ys[1]);idcg=np.zeros(len(tr));discount=1/np.log2(np.arange(2,12))\n for r in range(len(tr)):\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\n return y,idcg\nY,IDCG=None,None\ndef evaluate(ix):\n global Y,IDCG\n if Y is None:Y,IDCG=truth()\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n return np.mean(dcg[m]/IDCG[m])\n\ndef top(s,k=100):\n ix=np.argpartition(s,-k,axis=1)[:,-k:];v=np.take_along_axis(s,ix,axis=1);return np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1)\ndef save(ix,stage,name=None):\n tu,_,_=context(stage);pd.DataFrame({'uid':np.repeat(tu,100),'item_id':items[ix[:,:100].ravel()],'rank':np.tile(np.arange(1,101),len(tu))}).to_parquet(O/(name or ('submission.parquet' if stage=='test' else 'validation.parquet')),index=False)\nPY\ncat > /app/solution/knn.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);recent=matrix('recent',stage);recent.data=np.log1p(recent.data);short=matrix('short',stage);short.data=np.log1p(short.data)\np=recent+short*.5+matrix('likes',stage)*.3\n# asymmetric item neighborhood, inverse user activity weighting\nfor power in [.5]:\n z=x.copy();z.data=np.minimum(z.data,3);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+10))@z\n norms=np.asarray(z.power(2).sum(axis=0)).ravel();rows=[];cols=[];vals=[]\n for start in range(0,len(items),500):\n stop=min(start+500,len(items));s=(z[:,start:stop].T@z).toarray();s/=np.maximum(norms[start:stop,None],1e-8)**power*np.maximum(norms[None,:],1e-8)**(1-power);s[np.arange(stop-start),np.arange(start,stop)]=0\n ix=top(s,200);v=np.take_along_axis(s,ix,axis=1);rows.append(np.repeat(np.arange(start,stop),200));cols.append(ix.ravel());vals.append(v.ravel())\n if start%5000==0:print('knn',start,round(time.time()-t),flush=True)\n sim=sp.coo_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(len(items),len(items))).tocsr();sp.save_npz(O/f'{stage}_itemsim.npz',sim)\n for kind,pred in [('recent',p),('all',x)]:\n out=np.lib.format.open_memmap(O/f'{stage}_knn_{kind}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=(pred[tr[b:b+256]]@sim).toarray();s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\n ix=np.concatenate(ixs);print(stage,kind,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_knn_{kind}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/knn.py validation > /app/solution/knn.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 56, "nanos": 435383850 }, "formatted_output": "" } } ### exec · 2026-09-23T05:24:23.495Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/ease.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport time,sys,numpy as np,scipy.sparse as sp,scipy.linalg as la\\nfrom common import *\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';kind=sys.argv[2] if len(sys.argv)>2 else 'base';regs=[float(v) for v in sys.argv[3:]] or [100,500,2000];t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage,kind).astype(np.float32).tocsr();g=(x@x.T).toarray();print('gram',round(time.time()-t),flush=True)\\nfor reg in regs:\\n a=g.copy();a.flat[::len(a)+1]+=reg;cf=la.cho_factor(a,overwrite_a=True,check_finite=False);print('chol',reg,round(time.time()-t),flush=True)\\n out=np.lib.format.open_memmap(O/f'{stage}_ease_{kind}_{int(reg)}.npy',mode='w+',dtype='float16',shape=mask.shape)\\n for b in range(0,len(items),1000):\\n rhs=x[:,b:b+1000].toarray();sol=la.cho_solve(cf,rhs,check_finite=False);den=np.maximum(1-np.sum(rhs*sol,axis=0),1e-5);s=rhs[tr]-reg*sol[tr]/den;s[~mask[:,b:b+1000]]=-np.inf;out[:,b:b+1000]=s\\n out.flush();ixs=[]\\n for b in range(0,len(tu),256):ixs.append(top(np.array(out[b:b+256],dtype=np.float32)))\\n ix=np.concatenate(ixs);print(stage,kind,reg,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_ease_{kind}_{int(reg)}.parquet')\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 100 500 2000 > /app/solution/ease.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/knn.log; ls -lh /app/solution/validation.parquet\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:24:24.702Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d78893","wall_time_seconds":1.001645895,"session_id":2268,"original_token_count":0,"output":""} {"chunk_id":"33e095","wall_time_seconds":0.000004431,"exit_code":0,"original_token_count":101,"output":" ^^^^^^^^^^^^\n File \"/app/solution/common.py\", line 39, in evaluate\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'csr_matrix' object has no attribute 'A'\n-rw-r--r-- 1 agent agent 611K Sep 23 05:23 /app/solution/validation.parquet\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36269f2e487d2a534a5a48d769aa4", "usage": { "input_tokens": 28460, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 951, "reasoning_output_tokens": 383, "total_tokens": 29411 }, "turn_token_usage": { "input_tokens": 361394, "cached_input_tokens": 342656, "cache_write_input_tokens": 0, "output_tokens": 6122, "reasoning_output_tokens": 1387, "total_tokens": 367516 }, "thread_token_usage": { "input_tokens": 361394, "cached_input_tokens": 342656, "cache_write_input_tokens": 0, "output_tokens": 6122, "reasoning_output_tokens": 1387, "total_tokens": 367516 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-078a2f14-9eac-460a-84e9-2cc95164f6de", "process_id": "19607", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/knn.log; ls -lh /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/knn.log; ls -lh /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " ^^^^^^^^^^^^\n File \"/app/solution/common.py\", line 39, in evaluate\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'csr_matrix' object has no attribute 'A'\n-rw-r--r-- 1 agent agent 611K Sep 23 05:23 /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": " ^^^^^^^^^^^^\n File \"/app/solution/common.py\", line 39, in evaluate\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'csr_matrix' object has no attribute 'A'\n-rw-r--r-- 1 agent agent 611K Sep 23 05:23 /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4431 }, "formatted_output": " ^^^^^^^^^^^^\n File \"/app/solution/common.py\", line 39, in evaluate\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'csr_matrix' object has no attribute 'A'\n-rw-r--r-- 1 agent agent 611K Sep 23 05:23 /app/solution/validation.parquet\n" } } ## Step 17 · agent · 2026-09-23T05:24:28.163Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:35.645Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/common.py';s=open(p).read().replace(\\\"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;\\\", \\\"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].toarray();\\\");open(p,'w').write(s)\\np='/app/solution/knn.py';s=open(p).read();s=s[:s.index('for power')] + \\\"for power in [.5]:\\\\n sim=sp.load_npz(O/f'{stage}_itemsim.npz')\\\\n\\\"+s[s.index(' for kind,pred'):];open('/app/solution/knnpredict.py','w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/knnpredict.py validation > /app/solution/knnpredict.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:63468,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:24:36.843Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"dd456e","wall_time_seconds":1.001852778,"session_id":32624,"original_token_count":0,"output":""} {"chunk_id":"600ab9","wall_time_seconds":0.00000224,"exit_code":0,"original_token_count":36,"output":"saved baseline\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025879,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36288e9c087d29b9a06066209d20d", "usage": { "input_tokens": 29622, "cached_input_tokens": 28288, "cache_write_input_tokens": 0, "output_tokens": 304, "reasoning_output_tokens": 45, "total_tokens": 29926 }, "turn_token_usage": { "input_tokens": 391016, "cached_input_tokens": 370944, "cache_write_input_tokens": 0, "output_tokens": 6426, "reasoning_output_tokens": 1432, "total_tokens": 397442 }, "thread_token_usage": { "input_tokens": 391016, "cached_input_tokens": 370944, "cache_write_input_tokens": 0, "output_tokens": 6426, "reasoning_output_tokens": 1432, "total_tokens": 397442 } } ## Step 18 · agent · 2026-09-23T05:24:44.671Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:45.701Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; du -sh /app/solution\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:24:45.820Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"6594fd","wall_time_seconds":0.00000545,"exit_code":0,"original_token_count":10,"output":"gram 8\nchol 100.0 9\n486M\t/app/solution\n"} {"current_time":"2026-09-23 05:24:45 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36295175087d2bff62d9b596e5cfe", "usage": { "input_tokens": 30070, "cached_input_tokens": 29440, "cache_write_input_tokens": 0, "output_tokens": 248, "reasoning_output_tokens": 180, "total_tokens": 30318 }, "turn_token_usage": { "input_tokens": 421086, "cached_input_tokens": 400384, "cache_write_input_tokens": 0, "output_tokens": 6674, "reasoning_output_tokens": 1612, "total_tokens": 427760 }, "thread_token_usage": { "input_tokens": 421086, "cached_input_tokens": 400384, "cache_write_input_tokens": 0, "output_tokens": 6674, "reasoning_output_tokens": 1612, "total_tokens": 427760 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1854c7e3-b8a7-4b23-bb51-f27c2a65dcd3", "process_id": "92498", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; du -sh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; du -sh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "gram 8\nchol 100.0 9\n486M\t/app/solution\n", "stderr": "", "aggregated_output": "gram 8\nchol 100.0 9\n486M\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5450 }, "formatted_output": "gram 8\nchol 100.0 9\n486M\t/app/solution\n" } } ## Step 19 · agent · 2026-09-23T05:24:50.617Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:25:05.429Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/userknn.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport time,sys,numpy as np,scipy.sparse as sp\\nfrom common import *\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1))\\nz=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\\nr=matrix('recentorg',stage);r.data=np.log1p(r.data);r=r+matrix('likes',stage)*2\\nfor k in [100,400]:\\n out=np.lib.format.open_memmap(O/f'{stage}_user_{k}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,k);v=np.take_along_axis(s,ix,axis=1)**2\\n sim=sp.coo_matrix((v.ravel(),(np.repeat(np.arange(len(s)),k),ix.ravel())),shape=(len(s),len(uids))).tocsr();scores=(sim@r).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\\n ix=np.concatenate(ixs);print(stage,k,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_user_{k}.parquet');out.flush()\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/userknn.py validation > /app/solution/userknn.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:25:06.621Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"bb9adf","wall_time_seconds":1.003149744,"session_id":45687,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3629e0e8887d29dcc119d1a27d975", "usage": { "input_tokens": 30410, "cached_input_tokens": 29952, "cache_write_input_tokens": 0, "output_tokens": 560, "reasoning_output_tokens": 57, "total_tokens": 30970 }, "turn_token_usage": { "input_tokens": 451496, "cached_input_tokens": 430336, "cache_write_input_tokens": 0, "output_tokens": 7234, "reasoning_output_tokens": 1669, "total_tokens": 458730 }, "thread_token_usage": { "input_tokens": 451496, "cached_input_tokens": 430336, "cache_write_input_tokens": 0, "output_tokens": 7234, "reasoning_output_tokens": 1669, "total_tokens": 458730 } } ## Step 20 · agent · 2026-09-23T05:25:10.674Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:25:15.875Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os,glob\\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\\n try:\\n c=open(p,'rb').read().split(b'\\\\0')\\n if len(c)>1 and c[0]==b'/opt/task-venv/bin/python' and c[1]==b'/app/solution/prepare.py':os.kill(int(p.split('/')[2]),15);print('stopped stale baseline process')\\n except (OSError,ProcessLookupError):pass\\nPY\\ntail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:25:16.013Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"61b3b1","wall_time_seconds":0.000005091,"exit_code":0,"original_token_count":24,"output":"stopped stale baseline process\ngram 8\nchol 100.0 9\nvalidation recent 0.017896316597549682 18\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362b2e3bc87d2bceef486b1bfe32c", "usage": { "input_tokens": 31028, "cached_input_tokens": 30208, "cache_write_input_tokens": 0, "output_tokens": 225, "reasoning_output_tokens": 42, "total_tokens": 31253 }, "turn_token_usage": { "input_tokens": 482524, "cached_input_tokens": 460544, "cache_write_input_tokens": 0, "output_tokens": 7459, "reasoning_output_tokens": 1711, "total_tokens": 489983 }, "thread_token_usage": { "input_tokens": 482524, "cached_input_tokens": 460544, "cache_write_input_tokens": 0, "output_tokens": 7459, "reasoning_output_tokens": 1711, "total_tokens": 489983 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e6bd297-6857-4fd3-b7a2-d6f0f8ce57e6", "process_id": "1483", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os,glob\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n c=open(p,'rb').read().split(b'\\0')\n if len(c)>1 and c[0]==b'/opt/task-venv/bin/python' and c[1]==b'/app/solution/prepare.py':os.kill(int(p.split('/')[2]),15);print('stopped stale baseline process')\n except (OSError,ProcessLookupError):pass\nPY\ntail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os,glob\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n c=open(p,'rb').read().split(b'\\0')\n if len(c)>1 and c[0]==b'/opt/task-venv/bin/python' and c[1]==b'/app/solution/prepare.py':os.kill(int(p.split('/')[2]),15);print('stopped stale baseline process')\n except (OSError,ProcessLookupError):pass\nPY\ntail -6 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "stopped stale baseline process\ngram 8\nchol 100.0 9\nvalidation recent 0.017896316597549682 18\n", "stderr": "", "aggregated_output": "stopped stale baseline process\ngram 8\nchol 100.0 9\nvalidation recent 0.017896316597549682 18\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5091 }, "formatted_output": "stopped stale baseline process\ngram 8\nchol 100.0 9\nvalidation recent 0.017896316597549682 18\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-de17232a-25ab-4872-9d48-ec4890fae006", "process_id": "33365", "command": [ "/bin/sh", "-lc", "cat > /app/solution/prepare.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport numpy as np,pandas as pd, scipy.sparse as sp,pyarrow.parquet as pq,time\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());items=np.load(D/'test_eligible_candidates.npz')['item_ids'];np.savez(O/'ids.npz',uids=uids,items=items)\nU=pd.Index(uids);I=pd.Index(items);shape=(len(U),len(I));print('shape',shape,flush=True)\nfor stage in ['train','val']:\n cutoff=24786800 if stage=='train' else 24786800+1800+604800\n mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes']}\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=1500000):\n x=batch.to_pandas();c=I.get_indexer(x.item_id);m=(c>=0)&(x.played_ratio_pct.values>=50);x=x[m];c=c[m];r=U.get_indexer(x.uid);age=(cutoff-x.timestamp.values)/86400;org=x.is_organic.values.astype(np.float32);strong=np.where(x.played_ratio_pct.values>=80,1,.5).astype(np.float32)\n for k,w in [('count',np.ones(len(r),np.float32)),('organic',org*strong),('recent',np.exp2(-age/30)*strong*(.4+.6*org)),('recentorg',np.exp2(-age/30)*strong*org),('short',np.exp2(-age/7)*strong*(.4+.6*org))]:\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\n print(stage,'batch',round(time.time()-t),flush=True)\n x=pd.read_parquet(D/f'{stage}_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);m=(c>=0)&(r>=0);mats['likes']=sp.coo_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=shape).tocsr()\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{stage}_{k}.npz',v)\n print(stage,'done',mats['count'].nnz,round(time.time()-t),flush=True)\n# popular valid fallback\nfor stage in ['validation','test']:\n a=np.load(D/f'{stage}_eligible_candidates.npz');p=np.asarray(sp.load_npz(O/'train_short.npz').sum(axis=0)).ravel()\n if stage=='test':p=p*.5+np.asarray(sp.load_npz(O/'val_short.npz').sum(axis=0)).ravel()\n rows=[]\n for r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(mask,p,-np.inf);ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix])];rows.append(ix)\n ix=np.array(rows);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':items[ix.ravel()],'rank':np.tile(np.arange(1,101),len(rows))}).to_parquet(O/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('saved baseline',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\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport numpy as np,pandas as pd, scipy.sparse as sp,pyarrow.parquet as pq,time\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());items=np.load(D/'test_eligible_candidates.npz')['item_ids'];np.savez(O/'ids.npz',uids=uids,items=items)\nU=pd.Index(uids);I=pd.Index(items);shape=(len(U),len(I));print('shape',shape,flush=True)\nfor stage in ['train','val']:\n cutoff=24786800 if stage=='train' else 24786800+1800+604800\n mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes']}\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=1500000):\n x=batch.to_pandas();c=I.get_indexer(x.item_id);m=(c>=0)&(x.played_ratio_pct.values>=50);x=x[m];c=c[m];r=U.get_indexer(x.uid);age=(cutoff-x.timestamp.values)/86400;org=x.is_organic.values.astype(np.float32);strong=np.where(x.played_ratio_pct.values>=80,1,.5).astype(np.float32)\n for k,w in [('count',np.ones(len(r),np.float32)),('organic',org*strong),('recent',np.exp2(-age/30)*strong*(.4+.6*org)),('recentorg',np.exp2(-age/30)*strong*org),('short',np.exp2(-age/7)*strong*(.4+.6*org))]:\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\n print(stage,'batch',round(time.time()-t),flush=True)\n x=pd.read_parquet(D/f'{stage}_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);m=(c>=0)&(r>=0);mats['likes']=sp.coo_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=shape).tocsr()\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{stage}_{k}.npz',v)\n print(stage,'done',mats['count'].nnz,round(time.time()-t),flush=True)\n# popular valid fallback\nfor stage in ['validation','test']:\n a=np.load(D/f'{stage}_eligible_candidates.npz');p=np.asarray(sp.load_npz(O/'train_short.npz').sum(axis=0)).ravel()\n if stage=='test':p=p*.5+np.asarray(sp.load_npz(O/'val_short.npz').sum(axis=0)).ravel()\n rows=[]\n for r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(mask,p,-np.inf);ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix])];rows.append(ix)\n ix=np.array(rows);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':items[ix.ravel()],'rank':np.tile(np.arange(1,101),len(rows))}).to_parquet(O/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('saved baseline',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": 195, "nanos": 858271530 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1476c286-1aaf-44b2-9767-53b202134eef", "process_id": "32624", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;\", \"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].toarray();\");open(p,'w').write(s)\np='/app/solution/knn.py';s=open(p).read();s=s[:s.index('for power')] + \"for power in [.5]:\\n sim=sp.load_npz(O/f'{stage}_itemsim.npz')\\n\"+s[s.index(' for kind,pred'):];open('/app/solution/knnpredict.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/knnpredict.py validation > /app/solution/knnpredict.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].A;\", \"gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].toarray();\");open(p,'w').write(s)\np='/app/solution/knn.py';s=open(p).read();s=s[:s.index('for power')] + \"for power in [.5]:\\n sim=sp.load_npz(O/f'{stage}_itemsim.npz')\\n\"+s[s.index(' for kind,pred'):];open('/app/solution/knnpredict.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/knnpredict.py validation > /app/solution/knnpredict.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 44, "nanos": 490680112 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b669f1de-c538-48a1-adc2-d474174fdaf0", "process_id": "2268", "command": [ "/bin/sh", "-lc", "cat > /app/solution/ease.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp,scipy.linalg as la\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';kind=sys.argv[2] if len(sys.argv)>2 else 'base';regs=[float(v) for v in sys.argv[3:]] or [100,500,2000];t=time.time();tu,tr,mask=context(stage)\nx=profile(stage,kind).astype(np.float32).tocsr();g=(x@x.T).toarray();print('gram',round(time.time()-t),flush=True)\nfor reg in regs:\n a=g.copy();a.flat[::len(a)+1]+=reg;cf=la.cho_factor(a,overwrite_a=True,check_finite=False);print('chol',reg,round(time.time()-t),flush=True)\n out=np.lib.format.open_memmap(O/f'{stage}_ease_{kind}_{int(reg)}.npy',mode='w+',dtype='float16',shape=mask.shape)\n for b in range(0,len(items),1000):\n rhs=x[:,b:b+1000].toarray();sol=la.cho_solve(cf,rhs,check_finite=False);den=np.maximum(1-np.sum(rhs*sol,axis=0),1e-5);s=rhs[tr]-reg*sol[tr]/den;s[~mask[:,b:b+1000]]=-np.inf;out[:,b:b+1000]=s\n out.flush();ixs=[]\n for b in range(0,len(tu),256):ixs.append(top(np.array(out[b:b+256],dtype=np.float32)))\n ix=np.concatenate(ixs);print(stage,kind,reg,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_ease_{kind}_{int(reg)}.parquet')\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 100 500 2000 > /app/solution/ease.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/ease.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp,scipy.linalg as la\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';kind=sys.argv[2] if len(sys.argv)>2 else 'base';regs=[float(v) for v in sys.argv[3:]] or [100,500,2000];t=time.time();tu,tr,mask=context(stage)\nx=profile(stage,kind).astype(np.float32).tocsr();g=(x@x.T).toarray();print('gram',round(time.time()-t),flush=True)\nfor reg in regs:\n a=g.copy();a.flat[::len(a)+1]+=reg;cf=la.cho_factor(a,overwrite_a=True,check_finite=False);print('chol',reg,round(time.time()-t),flush=True)\n out=np.lib.format.open_memmap(O/f'{stage}_ease_{kind}_{int(reg)}.npy',mode='w+',dtype='float16',shape=mask.shape)\n for b in range(0,len(items),1000):\n rhs=x[:,b:b+1000].toarray();sol=la.cho_solve(cf,rhs,check_finite=False);den=np.maximum(1-np.sum(rhs*sol,axis=0),1e-5);s=rhs[tr]-reg*sol[tr]/den;s[~mask[:,b:b+1000]]=-np.inf;out[:,b:b+1000]=s\n out.flush();ixs=[]\n for b in range(0,len(tu),256):ixs.append(top(np.array(out[b:b+256],dtype=np.float32)))\n ix=np.concatenate(ixs);print(stage,kind,reg,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_ease_{kind}_{int(reg)}.parquet')\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 100 500 2000 > /app/solution/ease.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 58, "nanos": 370288356 }, "formatted_output": "" } } ## Step 21 · agent · 2026-09-23T05:25:22.429Z ### Thinking unavailable — no text recorded The recent-popularity baseline is saved and scores 0.025879 NDCG@10. The first item-neighborhood model scores lower, so I’m testing broader collaborative models and popularity blends before choosing the final approach. ### exec · 2026-09-23T05:25:25.827Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_recent.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:25:27.011Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"63616b","wall_time_seconds":1.001099262,"session_id":22556,"original_token_count":173,"output":"Traceback (most recent call last):\n File \"/app/solution/ease.py\", line 14, in \n ix=np.concatenate(ixs);print(stage,kind,reg,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_ease_{kind}_{int(reg)}.parquet')\n ^^^^^^^^^^^^\n File \"/app/solution/common.py\", line 39, in evaluate\n gain=Y[np.arange(len(ix))[:,None],ix[:,:10]].toarray();dcg=(gain/np.log2(np.arange(2,12))).sum(axis=1);m=IDCG>0\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'csr_matrix' object has no attribute 'A'\nvalidation recent 0.017896316597549682 18\nvalidation all 0.013646570297161284 40\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362bc337c87d29922004dbc30b0db", "usage": { "input_tokens": 31343, "cached_input_tokens": 30848, "cache_write_input_tokens": 0, "output_tokens": 290, "reasoning_output_tokens": 142, "total_tokens": 31633 }, "turn_token_usage": { "input_tokens": 513867, "cached_input_tokens": 491392, "cache_write_input_tokens": 0, "output_tokens": 7749, "reasoning_output_tokens": 1853, "total_tokens": 521616 }, "thread_token_usage": { "input_tokens": 513867, "cached_input_tokens": 491392, "cache_write_input_tokens": 0, "output_tokens": 7749, "reasoning_output_tokens": 1853, "total_tokens": 521616 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76be36d4-a426-4b32-b8dd-e1e3af717108", "process_id": "22556", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_recent.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/ease.log; tail -5 /app/solution/knnpredict.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_recent.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_knn_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017896,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_knn_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017896,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 807547214 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_knn_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017896,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 22 · agent · 2026-09-23T05:25:41.656Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:25:44.839Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 500 2000 > /app/solution/ease2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/userknn.log; ls -lh /app/solution/*npy\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:25:46.051Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"1ac9f5","wall_time_seconds":1.000561495,"session_id":25069,"original_token_count":0,"output":""} {"chunk_id":"975e28","wall_time_seconds":0.00000583,"exit_code":0,"original_token_count":112,"output":"validation 100 0.0280309873202581 25\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_ease_base_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_knn_all.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:24 /app/solution/validation_knn_recent.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_400.npy\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362c73b3c87d2a8fa57f0984fb0a9", "usage": { "input_tokens": 31895, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 545, "reasoning_output_tokens": 430, "total_tokens": 32440 }, "turn_token_usage": { "input_tokens": 545762, "cached_input_tokens": 522496, "cache_write_input_tokens": 0, "output_tokens": 8294, "reasoning_output_tokens": 2283, "total_tokens": 554056 }, "thread_token_usage": { "input_tokens": 545762, "cached_input_tokens": 522496, "cache_write_input_tokens": 0, "output_tokens": 8294, "reasoning_output_tokens": 2283, "total_tokens": 554056 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cd89f99b-5cf9-41f0-a867-8261656b2c7e", "process_id": "16655", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/userknn.log; ls -lh /app/solution/*npy" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/userknn.log; ls -lh /app/solution/*npy" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation 100 0.0280309873202581 25\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_ease_base_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_knn_all.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:24 /app/solution/validation_knn_recent.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_400.npy\n", "stderr": "", "aggregated_output": "validation 100 0.0280309873202581 25\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_ease_base_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_knn_all.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:24 /app/solution/validation_knn_recent.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_400.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5830 }, "formatted_output": "validation 100 0.0280309873202581 25\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_ease_base_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_knn_all.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:24 /app/solution/validation_knn_recent.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_100.npy\n-rw-r--r-- 1 agent agent 484M Sep 23 05:25 /app/solution/validation_user_400.npy\n" } } ## Step 23 · agent · 2026-09-23T05:25:51.157Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9d76a4af-7712-4454-a258-ad7e1874cbd9", "process_id": "45687", "command": [ "/bin/sh", "-lc", "cat > /app/solution/userknn.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1))\nz=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\nr=matrix('recentorg',stage);r.data=np.log1p(r.data);r=r+matrix('likes',stage)*2\nfor k in [100,400]:\n out=np.lib.format.open_memmap(O/f'{stage}_user_{k}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,k);v=np.take_along_axis(s,ix,axis=1)**2\n sim=sp.coo_matrix((v.ravel(),(np.repeat(np.arange(len(s)),k),ix.ravel())),shape=(len(s),len(uids))).tocsr();scores=(sim@r).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n ix=np.concatenate(ixs);print(stage,k,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_user_{k}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/userknn.py validation > /app/solution/userknn.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/userknn.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1))\nz=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\nr=matrix('recentorg',stage);r.data=np.log1p(r.data);r=r+matrix('likes',stage)*2\nfor k in [100,400]:\n out=np.lib.format.open_memmap(O/f'{stage}_user_{k}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,k);v=np.take_along_axis(s,ix,axis=1)**2\n sim=sp.coo_matrix((v.ravel(),(np.repeat(np.arange(len(s)),k),ix.ravel())),shape=(len(s),len(uids))).tocsr();scores=(sim@r).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n ix=np.concatenate(ixs);print(stage,k,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_user_{k}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/userknn.py validation > /app/solution/userknn.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 57, "nanos": 877235909 }, "formatted_output": "" } } ### exec · 2026-09-23T05:26:15.938Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport time,json,sys\\nstage='validation';tu,tr,mask=context(stage);t=time.time()\\nnames=sys.argv[1:] or ['knn_recent','knn_all','user_100','user_400','ease_base_100'];ixs=[]\\nfor name in names:\\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');ix=np.concatenate([top(np.array(s[b:b+256],dtype=np.float32),100) for b in range(0,len(tu),256)]);ixs.append(ix);print(name,evaluate(ix),flush=True)\\npops={}\\nfor k in ['count','organic','recent','recentorg','short','likes']:\\n p=np.asarray(matrix(k).sum(axis=0)).ravel();pops[k]=p;s=np.broadcast_to(p,mask.shape).copy();s[~mask]=-np.inf;ix=top(s);ixs.append(ix);print('pop',k,evaluate(ix),flush=True);del s\\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\\nfor name in names:\\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.array(a[np.arange(len(tu))[:,None],pool],np.float32);v[~np.isfinite(v)]=0;v=np.maximum(v,0);den=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v/den\\nfor name,p in pops.items():\\n v=p[pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8);features['pop_'+name]=v\\nnp.savez_compressed(O/'tuning_pool.npz',pool=pool,valid=valid,**features)\\ndef score(s):\\n s=s.copy();s[~valid]=-np.inf;ix=top(s,10);return evaluate(np.take_along_axis(pool,ix,axis=1))\\nbest=(0,None,None)\\nfor name in names:\\n for pn in ['pop_short','pop_recentorg','pop_likes']:\\n for p in [.25,.5,1,2]:\\n for w in [.1,.3,.6,1,2,4]:\\n s=features[name]+w*features[pn]**p;v=score(s)\\n if v>best[0]:best=(v,[name,pn,w,p],s);print('best',best[:2],flush=True)\\nbase=best[2];config=best[1]\\nfor name in names:\\n for w in [.1,.3,.6,1,2]:\\n v=score(base+w*features[name]);\\n if v>best[0]:best=(v,config+[name,w],base+w*features[name]);print('best',best[:2],flush=True)\\ns=best[2];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,'validation','validation_blend.parquet');json.dump({'score':best[0],'blend':best[1]},open(O/'blend.json','w'));print('done',time.time()-t,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py > /app/solution/tune.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:26:17.125Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"54371b","wall_time_seconds":1.000454251,"session_id":94033,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362da3e5c87d2bccd4bb7df73fa86", "usage": { "input_tokens": 32703, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 917, "reasoning_output_tokens": 82, "total_tokens": 33620 }, "turn_token_usage": { "input_tokens": 578465, "cached_input_tokens": 554240, "cache_write_input_tokens": 0, "output_tokens": 9211, "reasoning_output_tokens": 2365, "total_tokens": 587676 }, "thread_token_usage": { "input_tokens": 578465, "cached_input_tokens": 554240, "cache_write_input_tokens": 0, "output_tokens": 9211, "reasoning_output_tokens": 2365, "total_tokens": 587676 } } ## Step 24 · agent · 2026-09-23T05:26:20.996Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:26:28.242Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/ease2.log; tail -8 /app/solution/tune.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,sys\\nsys.path.insert(0,'/app/solution');from common import *\\ntu,tr,m=context('validation');x=matrix('count')[tr].tocoo();print('historical positives eligible',m[x.row,x.col].mean());s=pd.read_parquet(D/'validation_preference_state.parquet');r=pd.Index(tu).get_indexer(s.uid);c=I.get_indexer(s.item_id);v=(r>=0)&(c>=0);s=s[v];r=r[v];c=c[v];print(pd.crosstab(s.state,m[r,c]));y,idcg=truth();print('relevance',y.nnz,'likes',(y.data==7).sum(),'users',(idcg>0).sum())\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:26:29.426Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fe60a8","wall_time_seconds":1.000426065,"session_id":55061,"original_token_count":55,"output":"gram 9\nchol 500.0 11\nvalidation base 500.0 0.023610378723540347 39\nchol 2000.0 41\nknn_recent 0.017887546037918915\nknn_all 0.013689069003928028\nvalidation 100 0.0280309873202581 25\nvalidation 400 0.026251905182018684 53\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab362f9597c87d28276ccdf55b4ce02", "usage": { "input_tokens": 33677, "cached_input_tokens": 32512, "cache_write_input_tokens": 0, "output_tokens": 332, "reasoning_output_tokens": 81, "total_tokens": 34009 }, "turn_token_usage": { "input_tokens": 612142, "cached_input_tokens": 586752, "cache_write_input_tokens": 0, "output_tokens": 9543, "reasoning_output_tokens": 2446, "total_tokens": 621685 }, "thread_token_usage": { "input_tokens": 612142, "cached_input_tokens": 586752, "cache_write_input_tokens": 0, "output_tokens": 9543, "reasoning_output_tokens": 2446, "total_tokens": 621685 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ed00868a-1ffb-4c6c-b3bf-401a68c709f2", "process_id": "55061", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/ease2.log; tail -8 /app/solution/tune.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/app/solution');from common import *\ntu,tr,m=context('validation');x=matrix('count')[tr].tocoo();print('historical positives eligible',m[x.row,x.col].mean());s=pd.read_parquet(D/'validation_preference_state.parquet');r=pd.Index(tu).get_indexer(s.uid);c=I.get_indexer(s.item_id);v=(r>=0)&(c>=0);s=s[v];r=r[v];c=c[v];print(pd.crosstab(s.state,m[r,c]));y,idcg=truth();print('relevance',y.nnz,'likes',(y.data==7).sum(),'users',(idcg>0).sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/ease2.log; tail -8 /app/solution/tune.log; tail -5 /app/solution/userknn.log; /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/app/solution');from common import *\ntu,tr,m=context('validation');x=matrix('count')[tr].tocoo();print('historical positives eligible',m[x.row,x.col].mean());s=pd.read_parquet(D/'validation_preference_state.parquet');r=pd.Index(tu).get_indexer(s.uid);c=I.get_indexer(s.item_id);v=(r>=0)&(c>=0);s=s[v];r=r[v];c=c[v];print(pd.crosstab(s.state,m[r,c]));y,idcg=truth();print('relevance',y.nnz,'likes',(y.data==7).sum(),'users',(idcg>0).sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "historical positives eligible 0.0\ncol_0 False\nstate \ndisliked 50689\nliked 418435\nrelevance 37769 likes 4417 users 4263\n", "stderr": "", "aggregated_output": "historical positives eligible 0.0\ncol_0 False\nstate \ndisliked 50689\nliked 418435\nrelevance 37769 likes 4417 users 4263\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 408046519 }, "formatted_output": "historical positives eligible 0.0\ncol_0 False\nstate \ndisliked 50689\nliked 418435\nrelevance 37769 likes 4417 users 4263\n" } } ## Step 25 · agent · 2026-09-23T05:26:49.858Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86768243-26ba-4c9e-b46e-12be1272696c", "process_id": "25069", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 500 2000 > /app/solution/ease2.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/ease.py validation base 500 2000 > /app/solution/ease2.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 77, "nanos": 991846005 }, "formatted_output": "" } } ### exec · 2026-09-23T05:27:03.442Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/svd.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport time,sys,numpy as np,scipy.sparse as sp\\nfrom sklearn.utils.extmath import randomized_svd\\nfrom common import *\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(len(uids)/(df+1));idf=np.maximum(idf,0)\\nlength=np.asarray(x.sum(axis=1)).ravel();row=np.repeat(np.arange(len(uids)),np.diff(x.indptr));x.data=x.data*2.2/(x.data+1.2*(.25+.75*length[row]/length.mean()));x=x@sp.diags(idf**.5)\\na,s,vt=randomized_svd(x,n_components=256,n_iter=4,random_state=42);np.savez(O/f'{stage}_svd.npz',a=a,s=s,vt=vt);print('svd',time.time()-t,flush=True)\\nfor rank in [64,128,256]:\\n out=np.lib.format.open_memmap(O/f'{stage}_svd_{rank}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n score=(a[tr[b:b+256],:rank]*s[:rank])@vt[:rank];score[~mask[b:b+256]]=-np.inf;ixs.append(top(score));out[b:b+256]=score\\n ix=np.concatenate(ixs);print(stage,rank,evaluate(ix) if stage=='validation' else '',time.time()-t,flush=True);save(ix,stage,f'{stage}_svd_{rank}.parquet');out.flush()\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/svd.py validation > /app/solution/svd.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -10 /app/solution/tune.log; tail -6 /app/solution/ease2.log\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:27:04.669Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e692f4","wall_time_seconds":1.00122673,"session_id":54927,"original_token_count":0,"output":""} {"chunk_id":"30f751","wall_time_seconds":0.00000549,"exit_code":0,"original_token_count":215,"output":"pop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nbest (np.float64(0.019500274468408022), ['knn_recent', 'pop_short', 0.3, 0.25])\nbest (np.float64(0.020568038690359784), ['knn_recent', 'pop_short', 0.6, 0.25])\nbest (np.float64(0.022619374930117595), ['knn_recent', 'pop_short', 1, 0.25])\nbest (np.float64(0.02450680325463104), ['knn_recent', 'pop_short', 2, 0.25])\nbest (np.float64(0.026528612630242154), ['knn_recent', 'pop_short', 4, 0.25])\nbest (np.float64(0.02725790739379613), ['knn_recent', 'pop_short', 4, 0.5])\nbest (np.float64(0.02749107380421639), ['knn_recent', 'pop_short', 2, 1])\nbest (np.float64(0.028752951239796206), ['knn_recent', 'pop_short', 2, 2])\ngram 9\nchol 500.0 11\nvalidation base 500.0 0.023610378723540347 39\nchol 2000.0 41\nvalidation base 2000.0 0.025146204633484406 77\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36305b70c87d2a2153896fb594ab5", "usage": { "input_tokens": 34161, "cached_input_tokens": 33536, "cache_write_input_tokens": 0, "output_tokens": 1044, "reasoning_output_tokens": 516, "total_tokens": 35205 }, "turn_token_usage": { "input_tokens": 646303, "cached_input_tokens": 620288, "cache_write_input_tokens": 0, "output_tokens": 10587, "reasoning_output_tokens": 2962, "total_tokens": 656890 }, "thread_token_usage": { "input_tokens": 646303, "cached_input_tokens": 620288, "cache_write_input_tokens": 0, "output_tokens": 10587, "reasoning_output_tokens": 2962, "total_tokens": 656890 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-426d6447-82e2-4a61-8940-dcaa4dec28de", "process_id": "48661", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/tune.log; tail -6 /app/solution/ease2.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/tune.log; tail -6 /app/solution/ease2.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nbest (np.float64(0.019500274468408022), ['knn_recent', 'pop_short', 0.3, 0.25])\nbest (np.float64(0.020568038690359784), ['knn_recent', 'pop_short', 0.6, 0.25])\nbest (np.float64(0.022619374930117595), ['knn_recent', 'pop_short', 1, 0.25])\nbest (np.float64(0.02450680325463104), ['knn_recent', 'pop_short', 2, 0.25])\nbest (np.float64(0.026528612630242154), ['knn_recent', 'pop_short', 4, 0.25])\nbest (np.float64(0.02725790739379613), ['knn_recent', 'pop_short', 4, 0.5])\nbest (np.float64(0.02749107380421639), ['knn_recent', 'pop_short', 2, 1])\nbest (np.float64(0.028752951239796206), ['knn_recent', 'pop_short', 2, 2])\ngram 9\nchol 500.0 11\nvalidation base 500.0 0.023610378723540347 39\nchol 2000.0 41\nvalidation base 2000.0 0.025146204633484406 77\n", "stderr": "", "aggregated_output": "pop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nbest (np.float64(0.019500274468408022), ['knn_recent', 'pop_short', 0.3, 0.25])\nbest (np.float64(0.020568038690359784), ['knn_recent', 'pop_short', 0.6, 0.25])\nbest (np.float64(0.022619374930117595), ['knn_recent', 'pop_short', 1, 0.25])\nbest (np.float64(0.02450680325463104), ['knn_recent', 'pop_short', 2, 0.25])\nbest (np.float64(0.026528612630242154), ['knn_recent', 'pop_short', 4, 0.25])\nbest (np.float64(0.02725790739379613), ['knn_recent', 'pop_short', 4, 0.5])\nbest (np.float64(0.02749107380421639), ['knn_recent', 'pop_short', 2, 1])\nbest (np.float64(0.028752951239796206), ['knn_recent', 'pop_short', 2, 2])\ngram 9\nchol 500.0 11\nvalidation base 500.0 0.023610378723540347 39\nchol 2000.0 41\nvalidation base 2000.0 0.025146204633484406 77\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5490 }, "formatted_output": "pop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nbest (np.float64(0.019500274468408022), ['knn_recent', 'pop_short', 0.3, 0.25])\nbest (np.float64(0.020568038690359784), ['knn_recent', 'pop_short', 0.6, 0.25])\nbest (np.float64(0.022619374930117595), ['knn_recent', 'pop_short', 1, 0.25])\nbest (np.float64(0.02450680325463104), ['knn_recent', 'pop_short', 2, 0.25])\nbest (np.float64(0.026528612630242154), ['knn_recent', 'pop_short', 4, 0.25])\nbest (np.float64(0.02725790739379613), ['knn_recent', 'pop_short', 4, 0.5])\nbest (np.float64(0.02749107380421639), ['knn_recent', 'pop_short', 2, 1])\nbest (np.float64(0.028752951239796206), ['knn_recent', 'pop_short', 2, 2])\ngram 9\nchol 500.0 11\nvalidation base 500.0 0.023610378723540347 39\nchol 2000.0 41\nvalidation base 2000.0 0.025146204633484406 77\n" } } ## Step 26 · agent · 2026-09-23T05:27:20.489Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:27:45.058Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/als.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='4'\\nimport numpy as np,scipy.sparse as sp,time,sys\\nfrom common import *\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';f=int(sys.argv[2]) if len(sys.argv)>2 else 96;alpha=float(sys.argv[3]) if len(sys.argv)>3 else 10.;t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage);x=x+matrix('recentorg',stage)*.5;x.data=np.minimum(x.data,8)*alpha;x=x.tocsr();xt=x.T.tocsr()\\nrng=np.random.RandomState(42);uf=(rng.normal(size=(x.shape[0],f))*.01).astype('float32');vf=(rng.normal(size=(x.shape[1],f))*.01).astype('float32')\\ndef step(c,y,z):\\n reg=3.;g=y.T@y+np.eye(f,dtype=np.float32)*reg;rows=np.repeat(np.arange(c.shape[0]),np.diff(c.indptr));cols=c.indices\\n cp=c.copy();cp.data=cp.data+1;b=cp@y;del cp\\n temp=c.copy()\\n def mv(p):\\n for st in range(0,len(cols),150000):\\n en=st+150000;temp.data[st:en]=c.data[st:en]*np.einsum('ij,ij->i',p[rows[st:en]],y[cols[st:en]])\\n return p@g+temp@y\\n residual=b-mv(z);p=residual.copy();rs=np.einsum('ij,ij->i',residual,residual)\\n for cg in range(3):\\n ap=mv(p);a=rs/np.maximum(np.einsum('ij,ij->i',p,ap),1e-12);z+=a[:,None]*p;residual-=a[:,None]*ap;rsnew=np.einsum('ij,ij->i',residual,residual);p=residual+(rsnew/np.maximum(rs,1e-12))[:,None]*p;rs=rsnew\\n return z\\nfor it in range(16):\\n uf=step(x,vf,uf);vf=step(xt,uf,vf);print('iter',it,round(time.time()-t),flush=True)\\n if it in [7,15]:\\n name=f'als_{f}_{int(alpha)}_{it+1}';out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n s=uf[tr[b:b+256]]@vf.T;s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\\n out.flush();ix=np.concatenate(ixs);print('score',name,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_{name}.parquet');np.savez(O/f'{stage}_{name}_factors.npz',uf=uf,vf=vf)\\nPY\\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/als.py validation 96 10 > /app/solution/als.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:55061,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:27:46.592Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"fc02f1","wall_time_seconds":1.001169603,"session_id":68787,"original_token_count":0,"output":""} {"chunk_id":"955236","wall_time_seconds":0.0000026,"exit_code":0,"original_token_count":35,"output":"historical positives eligible 0.0\ncol_0 False\nstate \ndisliked 50689\nliked 418435\nrelevance 37769 likes 4417 users 4263\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36328d4dc87d2a07860b6977fb148", "usage": { "input_tokens": 35673, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 1223, "reasoning_output_tokens": 393, "total_tokens": 36896 }, "turn_token_usage": { "input_tokens": 681976, "cached_input_tokens": 654208, "cache_write_input_tokens": 0, "output_tokens": 11810, "reasoning_output_tokens": 3355, "total_tokens": 693786 }, "thread_token_usage": { "input_tokens": 681976, "cached_input_tokens": 654208, "cache_write_input_tokens": 0, "output_tokens": 11810, "reasoning_output_tokens": 3355, "total_tokens": 693786 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ccb9312a-66ee-408f-8956-07043002f2dc", "process_id": "54927", "command": [ "/bin/sh", "-lc", "cat > /app/solution/svd.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom sklearn.utils.extmath import randomized_svd\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(len(uids)/(df+1));idf=np.maximum(idf,0)\nlength=np.asarray(x.sum(axis=1)).ravel();row=np.repeat(np.arange(len(uids)),np.diff(x.indptr));x.data=x.data*2.2/(x.data+1.2*(.25+.75*length[row]/length.mean()));x=x@sp.diags(idf**.5)\na,s,vt=randomized_svd(x,n_components=256,n_iter=4,random_state=42);np.savez(O/f'{stage}_svd.npz',a=a,s=s,vt=vt);print('svd',time.time()-t,flush=True)\nfor rank in [64,128,256]:\n out=np.lib.format.open_memmap(O/f'{stage}_svd_{rank}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n score=(a[tr[b:b+256],:rank]*s[:rank])@vt[:rank];score[~mask[b:b+256]]=-np.inf;ixs.append(top(score));out[b:b+256]=score\n ix=np.concatenate(ixs);print(stage,rank,evaluate(ix) if stage=='validation' else '',time.time()-t,flush=True);save(ix,stage,f'{stage}_svd_{rank}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/svd.py validation > /app/solution/svd.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/svd.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport time,sys,numpy as np,scipy.sparse as sp\nfrom sklearn.utils.extmath import randomized_svd\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(len(uids)/(df+1));idf=np.maximum(idf,0)\nlength=np.asarray(x.sum(axis=1)).ravel();row=np.repeat(np.arange(len(uids)),np.diff(x.indptr));x.data=x.data*2.2/(x.data+1.2*(.25+.75*length[row]/length.mean()));x=x@sp.diags(idf**.5)\na,s,vt=randomized_svd(x,n_components=256,n_iter=4,random_state=42);np.savez(O/f'{stage}_svd.npz',a=a,s=s,vt=vt);print('svd',time.time()-t,flush=True)\nfor rank in [64,128,256]:\n out=np.lib.format.open_memmap(O/f'{stage}_svd_{rank}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n score=(a[tr[b:b+256],:rank]*s[:rank])@vt[:rank];score[~mask[b:b+256]]=-np.inf;ixs.append(top(score));out[b:b+256]=score\n ix=np.concatenate(ixs);print(stage,rank,evaluate(ix) if stage=='validation' else '',time.time()-t,flush=True);save(ix,stage,f'{stage}_svd_{rank}.parquet');out.flush()\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/svd.py validation > /app/solution/svd.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 43, "nanos": 273609676 }, "formatted_output": "" } } ## Step 27 · agent · 2026-09-23T05:28:04.056Z ### Thinking unavailable — no text recorded User-based collaborative filtering is now ahead of the popularity baseline, and popularity blends are improving the neighborhood model. I also confirmed that the masks exclude every observed positive listen and active explicit preference. I’m testing latent-factor models next, then I’ll assess a learned blend on held-out users. ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e969685d-f71b-46c1-96eb-640983ec1706", "process_id": "94033", "command": [ "/bin/sh", "-lc", "cat > /app/solution/tune.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,json,sys\nstage='validation';tu,tr,mask=context(stage);t=time.time()\nnames=sys.argv[1:] or ['knn_recent','knn_all','user_100','user_400','ease_base_100'];ixs=[]\nfor name in names:\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');ix=np.concatenate([top(np.array(s[b:b+256],dtype=np.float32),100) for b in range(0,len(tu),256)]);ixs.append(ix);print(name,evaluate(ix),flush=True)\npops={}\nfor k in ['count','organic','recent','recentorg','short','likes']:\n p=np.asarray(matrix(k).sum(axis=0)).ravel();pops[k]=p;s=np.broadcast_to(p,mask.shape).copy();s[~mask]=-np.inf;ix=top(s);ixs.append(ix);print('pop',k,evaluate(ix),flush=True);del s\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\nfor name in names:\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.array(a[np.arange(len(tu))[:,None],pool],np.float32);v[~np.isfinite(v)]=0;v=np.maximum(v,0);den=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v/den\nfor name,p in pops.items():\n v=p[pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8);features['pop_'+name]=v\nnp.savez_compressed(O/'tuning_pool.npz',pool=pool,valid=valid,**features)\ndef score(s):\n s=s.copy();s[~valid]=-np.inf;ix=top(s,10);return evaluate(np.take_along_axis(pool,ix,axis=1))\nbest=(0,None,None)\nfor name in names:\n for pn in ['pop_short','pop_recentorg','pop_likes']:\n for p in [.25,.5,1,2]:\n for w in [.1,.3,.6,1,2,4]:\n s=features[name]+w*features[pn]**p;v=score(s)\n if v>best[0]:best=(v,[name,pn,w,p],s);print('best',best[:2],flush=True)\nbase=best[2];config=best[1]\nfor name in names:\n for w in [.1,.3,.6,1,2]:\n v=score(base+w*features[name]);\n if v>best[0]:best=(v,config+[name,w],base+w*features[name]);print('best',best[:2],flush=True)\ns=best[2];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,'validation','validation_blend.parquet');json.dump({'score':best[0],'blend':best[1]},open(O/'blend.json','w'));print('done',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py > /app/solution/tune.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/tune.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,json,sys\nstage='validation';tu,tr,mask=context(stage);t=time.time()\nnames=sys.argv[1:] or ['knn_recent','knn_all','user_100','user_400','ease_base_100'];ixs=[]\nfor name in names:\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');ix=np.concatenate([top(np.array(s[b:b+256],dtype=np.float32),100) for b in range(0,len(tu),256)]);ixs.append(ix);print(name,evaluate(ix),flush=True)\npops={}\nfor k in ['count','organic','recent','recentorg','short','likes']:\n p=np.asarray(matrix(k).sum(axis=0)).ravel();pops[k]=p;s=np.broadcast_to(p,mask.shape).copy();s[~mask]=-np.inf;ix=top(s);ixs.append(ix);print('pop',k,evaluate(ix),flush=True);del s\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\nfor name in names:\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.array(a[np.arange(len(tu))[:,None],pool],np.float32);v[~np.isfinite(v)]=0;v=np.maximum(v,0);den=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v/den\nfor name,p in pops.items():\n v=p[pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8);features['pop_'+name]=v\nnp.savez_compressed(O/'tuning_pool.npz',pool=pool,valid=valid,**features)\ndef score(s):\n s=s.copy();s[~valid]=-np.inf;ix=top(s,10);return evaluate(np.take_along_axis(pool,ix,axis=1))\nbest=(0,None,None)\nfor name in names:\n for pn in ['pop_short','pop_recentorg','pop_likes']:\n for p in [.25,.5,1,2]:\n for w in [.1,.3,.6,1,2,4]:\n s=features[name]+w*features[pn]**p;v=score(s)\n if v>best[0]:best=(v,[name,pn,w,p],s);print('best',best[:2],flush=True)\nbase=best[2];config=best[1]\nfor name in names:\n for w in [.1,.3,.6,1,2]:\n v=score(base+w*features[name]);\n if v>best[0]:best=(v,config+[name,w],base+w*features[name]);print('best',best[:2],flush=True)\ns=best[2];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,'validation','validation_blend.parquet');json.dump({'score':best[0],'blend':best[1]},open(O/'blend.json','w'));print('done',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py > /app/solution/tune.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 110, "nanos": 19401028 }, "formatted_output": "" } } ### exec · 2026-09-23T05:28:08.129Z { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/tune.log; tail -5 /app/solution/als.log; tail -8 /app/solution/svd.log; du -sh /app/solution\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:28:08.213Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"fa1365","wall_time_seconds":0.00000518,"exit_code":0,"original_token_count":240,"output":"best (np.float64(0.029785704895926062), ['user_100', 'pop_short', 4, 0.25])\nbest (np.float64(0.029890036990034605), ['user_100', 'pop_short', 1, 0.5])\nbest (np.float64(0.03043310344688602), ['user_100', 'pop_short', 0.6, 1])\nbest (np.float64(0.03048817167121249), ['user_100', 'pop_short', 1, 1])\nbest (np.float64(0.031241175730171593), ['user_100', 'pop_short', 0.6, 2])\nbest (np.float64(0.03145537745400216), ['user_100', 'pop_short', 1, 2])\nbest (np.float64(0.031492950092363484), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.1])\nbest (np.float64(0.031702883155931914), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.6])\nbest (np.float64(0.03172392180341492), ['user_100', 'pop_short', 1, 2, 'ease_base_100', 0.6])\ndone 108.29443979263306\niter 0 13\nsvd 17.441051721572876\nvalidation 64 0.02238971755528694 22.125003576278687\nvalidation 128 0.024731956730051557 29.968602895736694\nvalidation 256 0.025254762964879104 38.57060146331787\n5.1G\t/app/solution\n"} {"current_time":"2026-09-23 05:28:08 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dcd80a21-1a8f-42e2-88a7-fb9e76b7daf7", "process_id": "83360", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/tune.log; tail -5 /app/solution/als.log; tail -8 /app/solution/svd.log; du -sh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/tune.log; tail -5 /app/solution/als.log; tail -8 /app/solution/svd.log; du -sh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "best (np.float64(0.029785704895926062), ['user_100', 'pop_short', 4, 0.25])\nbest (np.float64(0.029890036990034605), ['user_100', 'pop_short', 1, 0.5])\nbest (np.float64(0.03043310344688602), ['user_100', 'pop_short', 0.6, 1])\nbest (np.float64(0.03048817167121249), ['user_100', 'pop_short', 1, 1])\nbest (np.float64(0.031241175730171593), ['user_100', 'pop_short', 0.6, 2])\nbest (np.float64(0.03145537745400216), ['user_100', 'pop_short', 1, 2])\nbest (np.float64(0.031492950092363484), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.1])\nbest (np.float64(0.031702883155931914), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.6])\nbest (np.float64(0.03172392180341492), ['user_100', 'pop_short', 1, 2, 'ease_base_100', 0.6])\ndone 108.29443979263306\niter 0 13\nsvd 17.441051721572876\nvalidation 64 0.02238971755528694 22.125003576278687\nvalidation 128 0.024731956730051557 29.968602895736694\nvalidation 256 0.025254762964879104 38.57060146331787\n5.1G\t/app/solution\n", "stderr": "", "aggregated_output": "best (np.float64(0.029785704895926062), ['user_100', 'pop_short', 4, 0.25])\nbest (np.float64(0.029890036990034605), ['user_100', 'pop_short', 1, 0.5])\nbest (np.float64(0.03043310344688602), ['user_100', 'pop_short', 0.6, 1])\nbest (np.float64(0.03048817167121249), ['user_100', 'pop_short', 1, 1])\nbest (np.float64(0.031241175730171593), ['user_100', 'pop_short', 0.6, 2])\nbest (np.float64(0.03145537745400216), ['user_100', 'pop_short', 1, 2])\nbest (np.float64(0.031492950092363484), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.1])\nbest (np.float64(0.031702883155931914), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.6])\nbest (np.float64(0.03172392180341492), ['user_100', 'pop_short', 1, 2, 'ease_base_100', 0.6])\ndone 108.29443979263306\niter 0 13\nsvd 17.441051721572876\nvalidation 64 0.02238971755528694 22.125003576278687\nvalidation 128 0.024731956730051557 29.968602895736694\nvalidation 256 0.025254762964879104 38.57060146331787\n5.1G\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5180 }, "formatted_output": "best (np.float64(0.029785704895926062), ['user_100', 'pop_short', 4, 0.25])\nbest (np.float64(0.029890036990034605), ['user_100', 'pop_short', 1, 0.5])\nbest (np.float64(0.03043310344688602), ['user_100', 'pop_short', 0.6, 1])\nbest (np.float64(0.03048817167121249), ['user_100', 'pop_short', 1, 1])\nbest (np.float64(0.031241175730171593), ['user_100', 'pop_short', 0.6, 2])\nbest (np.float64(0.03145537745400216), ['user_100', 'pop_short', 1, 2])\nbest (np.float64(0.031492950092363484), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.1])\nbest (np.float64(0.031702883155931914), ['user_100', 'pop_short', 1, 2, 'knn_recent', 0.6])\nbest (np.float64(0.03172392180341492), ['user_100', 'pop_short', 1, 2, 'ease_base_100', 0.6])\ndone 108.29443979263306\niter 0 13\nsvd 17.441051721572876\nvalidation 64 0.02238971755528694 22.125003576278687\nvalidation 128 0.024731956730051557 29.968602895736694\nvalidation 256 0.025254762964879104 38.57060146331787\n5.1G\t/app/solution\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36352d49887d2ac628b640d3ff89b", "usage": { "input_tokens": 37031, "cached_input_tokens": 35456, "cache_write_input_tokens": 0, "output_tokens": 657, "reasoning_output_tokens": 516, "total_tokens": 37688 }, "turn_token_usage": { "input_tokens": 719007, "cached_input_tokens": 689664, "cache_write_input_tokens": 0, "output_tokens": 12467, "reasoning_output_tokens": 3871, "total_tokens": 731474 }, "thread_token_usage": { "input_tokens": 719007, "cached_input_tokens": 689664, "cache_write_input_tokens": 0, "output_tokens": 12467, "reasoning_output_tokens": 3871, "total_tokens": 731474 } } ## Step 28 · agent · 2026-09-23T05:28:19.892Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:28:37.427Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/user_variants.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport time,sys\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\\nrs=[];cs=[];vs=[]\\nfor b in range(0,len(tu),256):\\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);v=np.take_along_axis(s,ix,axis=1);cs.append(ix);vs.append(v)\\nix=np.concatenate(cs);v=np.concatenate(vs);np.savez(O/f'{stage}_usersim.npz',ix=ix,v=v)\\nfor k,power,kind in [(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]:\\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**power).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'uv_{k}_{power}_{kind}'\\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/user_variants.py validation > /app/solution/user_variants.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:28:38.608Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ac6a70","wall_time_seconds":1.000165091,"session_id":58199,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36368796087d2afd7f8553e8d4cb9", "usage": { "input_tokens": 38187, "cached_input_tokens": 36864, "cache_write_input_tokens": 0, "output_tokens": 920, "reasoning_output_tokens": 277, "total_tokens": 39107 }, "turn_token_usage": { "input_tokens": 757194, "cached_input_tokens": 726528, "cache_write_input_tokens": 0, "output_tokens": 13387, "reasoning_output_tokens": 4148, "total_tokens": 770581 }, "thread_token_usage": { "input_tokens": 757194, "cached_input_tokens": 726528, "cache_write_input_tokens": 0, "output_tokens": 13387, "reasoning_output_tokens": 4148, "total_tokens": 770581 } } ## Step 29 · agent · 2026-09-23T05:29:07.471Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:29:21.532Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/full_history.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport pyarrow.parquet as pq,time,sys\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();cutoff=24786800+(606600 if stage=='test' else 0);shape=(len(uids),int(2e7));x=sp.csr_matrix(shape,dtype=np.float32)\\nfor part in (['train','val'] if stage=='test' else ['train']):\\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\\nx.data=np.log1p(x.data)\\nfor part in (['train','val'] if stage=='test' else ['train']):\\n d=pd.read_parquet(D/f'{part}_likes.parquet');r=U.get_indexer(d.uid);m=r>=0;x+=sp.coo_matrix((np.full(m.sum(),3,np.float32),(r[m],d.item_id.values[m])),shape=shape).tocsr()\\nsp.save_npz(O/f'{stage}_fullhistory.npz',x);print('prepared',x.nnz,time.time()-t,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/als.log; tail -5 /app/solution/user_variants.log; cat /app/solution/blend.json\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:29:22.922Z Script completed Wall time 1.4 seconds Output: {"chunk_id":"a1960d","wall_time_seconds":1.000450879,"session_id":48521,"original_token_count":0,"output":""} {"chunk_id":"7f97a1","wall_time_seconds":0.00000676,"exit_code":0,"original_token_count":65,"output":"iter 5 56\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\n{\"score\": 0.03172392180341492, \"blend\": [\"user_100\", \"pop_short\", 1, 2, \"ease_base_100\", 0.6]}"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36394390c87d2972fb3342e6c8cca", "usage": { "input_tokens": 39165, "cached_input_tokens": 38016, "cache_write_input_tokens": 0, "output_tokens": 943, "reasoning_output_tokens": 456, "total_tokens": 40108 }, "turn_token_usage": { "input_tokens": 796359, "cached_input_tokens": 764544, "cache_write_input_tokens": 0, "output_tokens": 14330, "reasoning_output_tokens": 4604, "total_tokens": 810689 }, "thread_token_usage": { "input_tokens": 796359, "cached_input_tokens": 764544, "cache_write_input_tokens": 0, "output_tokens": 14330, "reasoning_output_tokens": 4604, "total_tokens": 810689 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7ce6a3ce-ac02-4510-873c-effb54c6ea5d", "process_id": "65387", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/als.log; tail -5 /app/solution/user_variants.log; cat /app/solution/blend.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/als.log; tail -5 /app/solution/user_variants.log; cat /app/solution/blend.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "iter 5 56\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\n{\"score\": 0.03172392180341492, \"blend\": [\"user_100\", \"pop_short\", 1, 2, \"ease_base_100\", 0.6]}", "stderr": "", "aggregated_output": "iter 5 56\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\n{\"score\": 0.03172392180341492, \"blend\": [\"user_100\", \"pop_short\", 1, 2, \"ease_base_100\", 0.6]}", "exit_code": 0, "duration": { "secs": 0, "nanos": 6760 }, "formatted_output": "iter 5 56\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\n{\"score\": 0.03172392180341492, \"blend\": [\"user_100\", \"pop_short\", 1, 2, \"ease_base_100\", 0.6]}" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-449524cd-b768-4f31-aa5e-46883b7459ee", "process_id": "48521", "command": [ "/bin/sh", "-lc", "cat > /app/solution/full_history.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();cutoff=24786800+(606600 if stage=='test' else 0);shape=(len(uids),int(2e7));x=sp.csr_matrix(shape,dtype=np.float32)\nfor part in (['train','val'] if stage=='test' else ['train']):\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nx.data=np.log1p(x.data)\nfor part in (['train','val'] if stage=='test' else ['train']):\n d=pd.read_parquet(D/f'{part}_likes.parquet');r=U.get_indexer(d.uid);m=r>=0;x+=sp.coo_matrix((np.full(m.sum(),3,np.float32),(r[m],d.item_id.values[m])),shape=shape).tocsr()\nsp.save_npz(O/f'{stage}_fullhistory.npz',x);print('prepared',x.nnz,time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/full_history.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();cutoff=24786800+(606600 if stage=='test' else 0);shape=(len(uids),int(2e7));x=sp.csr_matrix(shape,dtype=np.float32)\nfor part in (['train','val'] if stage=='test' else ['train']):\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nx.data=np.log1p(x.data)\nfor part in (['train','val'] if stage=='test' else ['train']):\n d=pd.read_parquet(D/f'{part}_likes.parquet');r=U.get_indexer(d.uid);m=r>=0;x+=sp.coo_matrix((np.full(m.sum(),3,np.float32),(r[m],d.item_id.values[m])),shape=shape).tocsr()\nsp.save_npz(O/f'{stage}_fullhistory.npz',x);print('prepared',x.nnz,time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 6, "nanos": 209224454 }, "formatted_output": "" } } ## Step 30 · agent · 2026-09-23T05:29:42.565Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:29:43.776Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/full_history.log; tail -10 /app/solution/user_variants.log; tail -6 /app/solution/als.log\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:29:43.864Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"50ef4f","wall_time_seconds":0.000010031,"exit_code":0,"original_token_count":154,"output":"/app/solution/full_history.py:8: RuntimeWarning: overflow encountered in cast\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nprepared 8566997 4.998405933380127\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\n"} {"current_time":"2026-09-23 05:29:43 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-91a17617-82de-40b8-83a9-ec94e6c82a65", "process_id": "99853", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/full_history.log; tail -10 /app/solution/user_variants.log; tail -6 /app/solution/als.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/full_history.log; tail -10 /app/solution/user_variants.log; tail -6 /app/solution/als.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/full_history.py:8: RuntimeWarning: overflow encountered in cast\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nprepared 8566997 4.998405933380127\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\n", "stderr": "", "aggregated_output": "/app/solution/full_history.py:8: RuntimeWarning: overflow encountered in cast\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nprepared 8566997 4.998405933380127\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 10031 }, "formatted_output": "/app/solution/full_history.py:8: RuntimeWarning: overflow encountered in cast\n d=batch.to_pandas();d=d[d.played_ratio_pct>=50];r=U.get_indexer(d.uid);c=d.item_id.values;w=(.4+.6*d.is_organic.values)*np.exp2(-(cutoff-d.timestamp.values)/86400/180);x+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nprepared 8566997 4.998405933380127\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab363b31d9c87d29bf8209e27ec32a0", "usage": { "input_tokens": 40325, "cached_input_tokens": 39040, "cache_write_input_tokens": 0, "output_tokens": 586, "reasoning_output_tokens": 516, "total_tokens": 40911 }, "turn_token_usage": { "input_tokens": 836684, "cached_input_tokens": 803584, "cache_write_input_tokens": 0, "output_tokens": 14916, "reasoning_output_tokens": 5120, "total_tokens": 851600 }, "thread_token_usage": { "input_tokens": 836684, "cached_input_tokens": 803584, "cache_write_input_tokens": 0, "output_tokens": 14916, "reasoning_output_tokens": 5120, "total_tokens": 851600 } } ## Step 31 · agent · 2026-09-23T05:29:48.041Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:29:59.619Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/full_history.py';s=open(p).read().replace('cutoff-d.timestamp.values','cutoff-d.timestamp.values.astype(np.float64)');open(p,'w').write(s)\\np='/app/solution/user_variants.py';s=open(p).read().replace(\\\"x=profile(stage);\\\", \\\"x=sp.load_npz(O/f'{stage}_fullhistory.npz');\\\").replace(\\\"z=x@sp.diags(idf**.7)\\\",\\\"z=x.copy();z.data*=idf[z.indices]**.7\\\").replace(\\\"f'uv_{k}_{power}_{kind}'\\\",\\\"f'full_{k}_{power}_{kind}'\\\");s=s.replace(\\\"[(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]\\\", \\\"[(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'likes')]\\\")\\nopen('/app/solution/full_user.py','w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_user.py validation > /app/solution/full_user.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:30:00.803Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"3d3aa3","wall_time_seconds":1.001978522,"session_id":60830,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab363c81afc87d2a8cbac9e48e629e3", "usage": { "input_tokens": 41227, "cached_input_tokens": 40192, "cache_write_input_tokens": 0, "output_tokens": 454, "reasoning_output_tokens": 59, "total_tokens": 41681 }, "turn_token_usage": { "input_tokens": 877911, "cached_input_tokens": 843776, "cache_write_input_tokens": 0, "output_tokens": 15370, "reasoning_output_tokens": 5179, "total_tokens": 893281 }, "thread_token_usage": { "input_tokens": 877911, "cached_input_tokens": 843776, "cache_write_input_tokens": 0, "output_tokens": 15370, "reasoning_output_tokens": 5179, "total_tokens": 893281 } } ## Step 32 · agent · 2026-09-23T05:30:05.313Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:30:07.197Z { "code": "text(await tools.exec_command({cmd:\"head -12 /app/solution/tune.log; tail -10 /app/solution/user_variants.log; tail -8 /app/solution/als.log; tail -5 /app/solution/full_user.log\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:30:07.289Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"12a09f","wall_time_seconds":0.0000065,"exit_code":1,"original_token_count":210,"output":"knn_recent 0.017887546037918915\nknn_all 0.013689069003928028\nuser_100 0.02804272143383684\nuser_400 0.026168095355456066\nease_base_100 0.020794328289637377\npop count 0.010713530758619576\npop organic 0.010531273184921974\npop recent 0.016778295514801304\npop recentorg 0.016199557307546377\npop short 0.025879297170205907\npop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\ntail: cannot open '/app/solution/full_user.log' for reading: No such file or directory\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-118cc4dc-eb71-4028-aab6-1a99ce7901e6", "process_id": "23626", "command": [ "/bin/sh", "-lc", "head -12 /app/solution/tune.log; tail -10 /app/solution/user_variants.log; tail -8 /app/solution/als.log; tail -5 /app/solution/full_user.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "head -12 /app/solution/tune.log; tail -10 /app/solution/user_variants.log; tail -8 /app/solution/als.log; tail -5 /app/solution/full_user.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "knn_recent 0.017887546037918915\nknn_all 0.013689069003928028\nuser_100 0.02804272143383684\nuser_400 0.026168095355456066\nease_base_100 0.020794328289637377\npop count 0.010713530758619576\npop organic 0.010531273184921974\npop recent 0.016778295514801304\npop recentorg 0.016199557307546377\npop short 0.025879297170205907\npop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\ntail: cannot open '/app/solution/full_user.log' for reading: No such file or directory\n", "stderr": "", "aggregated_output": "knn_recent 0.017887546037918915\nknn_all 0.013689069003928028\nuser_100 0.02804272143383684\nuser_400 0.026168095355456066\nease_base_100 0.020794328289637377\npop count 0.010713530758619576\npop organic 0.010531273184921974\npop recent 0.016778295514801304\npop recentorg 0.016199557307546377\npop short 0.025879297170205907\npop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\ntail: cannot open '/app/solution/full_user.log' for reading: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 6500 }, "formatted_output": "knn_recent 0.017887546037918915\nknn_all 0.013689069003928028\nuser_100 0.02804272143383684\nuser_400 0.026168095355456066\nease_base_100 0.020794328289637377\npop count 0.010713530758619576\npop organic 0.010531273184921974\npop recent 0.016778295514801304\npop recentorg 0.016199557307546377\npop short 0.025879297170205907\npop likes 0.01206964587394165\nbest (np.float64(0.018311317786419295), ['knn_recent', 'pop_short', 0.1, 0.25])\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\niter 6 64\niter 7 72\nscore als_96_10_8 0.018972463534786023 78\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\ntail: cannot open '/app/solution/full_user.log' for reading: No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab363d9095887d2a4ee42a7eaa78064", "usage": { "input_tokens": 41740, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 168, "reasoning_output_tokens": 96, "total_tokens": 41908 }, "turn_token_usage": { "input_tokens": 919651, "cached_input_tokens": 884864, "cache_write_input_tokens": 0, "output_tokens": 15538, "reasoning_output_tokens": 5275, "total_tokens": 935189 }, "thread_token_usage": { "input_tokens": 919651, "cached_input_tokens": 884864, "cache_write_input_tokens": 0, "output_tokens": 15538, "reasoning_output_tokens": 5275, "total_tokens": 935189 } } ## Step 33 · agent · 2026-09-23T05:30:18.660Z ### Thinking unavailable — no text recorded Recency has made the biggest difference so far: using neighbors’ listening from the last week raised NDCG@10 to 0.041615. I’m testing shorter time scales and histories outside the retrieval catalog, while keeping all recommended items inside the supplied masks. ### exec · 2026-09-23T05:30:39.305Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/temporal.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport pyarrow.parquet as pq,time\\nfor part in ['train','val']:\\n cutoff=24786800+(606600 if part=='val' else 0);shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['d1','d3','d14','org7','like7']}\\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;strength=np.where(d.played_ratio_pct.values>=80,1,.5);org=d.is_organic.values\\n for k,h in [('d1',1),('d3',3),('d14',14),('org7',7)]:\\n w=np.exp2(-age/h)*strength*(org if k=='org7' else (.4+.6*org));mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\\n d=pd.read_parquet(D/f'{part}_likes.parquet');c=I.get_indexer(d.item_id);r=U.get_indexer(d.uid);m=(r>=0)&(c>=0);age=(cutoff-d.timestamp.values.astype(np.float64)[m])/86400;mats['like7']=sp.coo_matrix((np.exp2(-age/7).astype(np.float32),(r[m],c[m])),shape=shape).tocsr()\\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{part}_{k}.npz',v)\\n print(part,flush=True)\\nPY\\npython - <<'PY'\\np='/app/solution/common.py';s=open(p).read().replace(\\\"{'recent':30,'recentorg':30,'short':7}\\\",\\\"{'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7,'like7':7}\\\");open(p,'w').write(s)\\np='/app/solution/full_user.py';s=open(p).read().replace(\\\"f'{stage}_usersim.npz'\\\",\\\"f'{stage}_full_usersim.npz'\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/temporal.py > /app/solution/temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:30:40.490Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"933485","wall_time_seconds":1.001124895,"session_id":43203,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab363df884887d2b6dedad3f58d47d7", "usage": { "input_tokens": 42318, "cached_input_tokens": 41600, "cache_write_input_tokens": 0, "output_tokens": 1021, "reasoning_output_tokens": 296, "total_tokens": 43339 }, "turn_token_usage": { "input_tokens": 961969, "cached_input_tokens": 926464, "cache_write_input_tokens": 0, "output_tokens": 16559, "reasoning_output_tokens": 5571, "total_tokens": 978528 }, "thread_token_usage": { "input_tokens": 961969, "cached_input_tokens": 926464, "cache_write_input_tokens": 0, "output_tokens": 16559, "reasoning_output_tokens": 5571, "total_tokens": 978528 } } ## Step 34 · agent · 2026-09-23T05:30:46.148Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:30:48.301Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/full_history.log; tail -8 /app/solution/als.log; tail -8 /app/solution/user_variants.log; tail -4 /app/solution/full_user.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:30:48.692Z Script completed Wall time 0.4 seconds Output: {"chunk_id":"83e0f9","wall_time_seconds":0.00000393,"exit_code":0,"original_token_count":118,"output":"prepared 8566997 6.905769348144531\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\niter 13 148\niter 14 162\niter 15 175\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\nuv_100_2_recent 0.030366108227652483 94\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\n8526221312\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36400a93487d2b07c98ce90ed53f5", "usage": { "input_tokens": 43395, "cached_input_tokens": 42112, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 21, "total_tokens": 43498 }, "turn_token_usage": { "input_tokens": 1005364, "cached_input_tokens": 968576, "cache_write_input_tokens": 0, "output_tokens": 16662, "reasoning_output_tokens": 5592, "total_tokens": 1022026 }, "thread_token_usage": { "input_tokens": 1005364, "cached_input_tokens": 968576, "cache_write_input_tokens": 0, "output_tokens": 16662, "reasoning_output_tokens": 5592, "total_tokens": 1022026 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-94736b36-40ad-4693-b485-19d0751b5d31", "process_id": "48652", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/full_history.log; tail -8 /app/solution/als.log; tail -8 /app/solution/user_variants.log; tail -4 /app/solution/full_user.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/full_history.log; tail -8 /app/solution/als.log; tail -8 /app/solution/user_variants.log; tail -4 /app/solution/full_user.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 8566997 6.905769348144531\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\niter 13 148\niter 14 162\niter 15 175\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\nuv_100_2_recent 0.030366108227652483 94\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\n8526221312\n", "stderr": "", "aggregated_output": "prepared 8566997 6.905769348144531\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\niter 13 148\niter 14 162\niter 15 175\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\nuv_100_2_recent 0.030366108227652483 94\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\n8526221312\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3930 }, "formatted_output": "prepared 8566997 6.905769348144531\niter 8 91\niter 9 103\niter 10 115\niter 11 124\niter 12 133\niter 13 148\niter 14 162\niter 15 175\nuv_20_2_recentorg 0.026531150621567082 23\nuv_50_2_recentorg 0.030317178420927078 34\nuv_100_2_recentorg 0.031099475381133487 46\nuv_400_2_recentorg 0.02998144830943394 61\nuv_100_2_short 0.0416147616958469 78\nuv_100_2_recent 0.030366108227652483 94\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\n8526221312\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c0e3ac1f-5417-4f69-93d7-e0188f509000", "process_id": "68787", "command": [ "/bin/sh", "-lc", "cat > /app/solution/als.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport numpy as np,scipy.sparse as sp,time,sys\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';f=int(sys.argv[2]) if len(sys.argv)>2 else 96;alpha=float(sys.argv[3]) if len(sys.argv)>3 else 10.;t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);x=x+matrix('recentorg',stage)*.5;x.data=np.minimum(x.data,8)*alpha;x=x.tocsr();xt=x.T.tocsr()\nrng=np.random.RandomState(42);uf=(rng.normal(size=(x.shape[0],f))*.01).astype('float32');vf=(rng.normal(size=(x.shape[1],f))*.01).astype('float32')\ndef step(c,y,z):\n reg=3.;g=y.T@y+np.eye(f,dtype=np.float32)*reg;rows=np.repeat(np.arange(c.shape[0]),np.diff(c.indptr));cols=c.indices\n cp=c.copy();cp.data=cp.data+1;b=cp@y;del cp\n temp=c.copy()\n def mv(p):\n for st in range(0,len(cols),150000):\n en=st+150000;temp.data[st:en]=c.data[st:en]*np.einsum('ij,ij->i',p[rows[st:en]],y[cols[st:en]])\n return p@g+temp@y\n residual=b-mv(z);p=residual.copy();rs=np.einsum('ij,ij->i',residual,residual)\n for cg in range(3):\n ap=mv(p);a=rs/np.maximum(np.einsum('ij,ij->i',p,ap),1e-12);z+=a[:,None]*p;residual-=a[:,None]*ap;rsnew=np.einsum('ij,ij->i',residual,residual);p=residual+(rsnew/np.maximum(rs,1e-12))[:,None]*p;rs=rsnew\n return z\nfor it in range(16):\n uf=step(x,vf,uf);vf=step(xt,uf,vf);print('iter',it,round(time.time()-t),flush=True)\n if it in [7,15]:\n name=f'als_{f}_{int(alpha)}_{it+1}';out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=uf[tr[b:b+256]]@vf.T;s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\n out.flush();ix=np.concatenate(ixs);print('score',name,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_{name}.parquet');np.savez(O/f'{stage}_{name}_factors.npz',uf=uf,vf=vf)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/als.py validation 96 10 > /app/solution/als.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/als.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='4'\nimport numpy as np,scipy.sparse as sp,time,sys\nfrom common import *\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';f=int(sys.argv[2]) if len(sys.argv)>2 else 96;alpha=float(sys.argv[3]) if len(sys.argv)>3 else 10.;t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);x=x+matrix('recentorg',stage)*.5;x.data=np.minimum(x.data,8)*alpha;x=x.tocsr();xt=x.T.tocsr()\nrng=np.random.RandomState(42);uf=(rng.normal(size=(x.shape[0],f))*.01).astype('float32');vf=(rng.normal(size=(x.shape[1],f))*.01).astype('float32')\ndef step(c,y,z):\n reg=3.;g=y.T@y+np.eye(f,dtype=np.float32)*reg;rows=np.repeat(np.arange(c.shape[0]),np.diff(c.indptr));cols=c.indices\n cp=c.copy();cp.data=cp.data+1;b=cp@y;del cp\n temp=c.copy()\n def mv(p):\n for st in range(0,len(cols),150000):\n en=st+150000;temp.data[st:en]=c.data[st:en]*np.einsum('ij,ij->i',p[rows[st:en]],y[cols[st:en]])\n return p@g+temp@y\n residual=b-mv(z);p=residual.copy();rs=np.einsum('ij,ij->i',residual,residual)\n for cg in range(3):\n ap=mv(p);a=rs/np.maximum(np.einsum('ij,ij->i',p,ap),1e-12);z+=a[:,None]*p;residual-=a[:,None]*ap;rsnew=np.einsum('ij,ij->i',residual,residual);p=residual+(rsnew/np.maximum(rs,1e-12))[:,None]*p;rs=rsnew\n return z\nfor it in range(16):\n uf=step(x,vf,uf);vf=step(xt,uf,vf);print('iter',it,round(time.time()-t),flush=True)\n if it in [7,15]:\n name=f'als_{f}_{int(alpha)}_{it+1}';out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n s=uf[tr[b:b+256]]@vf.T;s[~mask[b:b+256]]=-np.inf;ixs.append(top(s));out[b:b+256]=s\n out.flush();ix=np.concatenate(ixs);print('score',name,evaluate(ix) if stage=='validation' else '',round(time.time()-t),flush=True);save(ix,stage,f'{stage}_{name}.parquet');np.savez(O/f'{stage}_{name}_factors.npz',uf=uf,vf=vf)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/als.py validation 96 10 > /app/solution/als.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 185, "nanos": 923475087 }, "formatted_output": "" } } ## Step 35 · agent · 2026-09-23T05:30:54.745Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:30:56.007Z { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.events; tail -4 /app/solution/als.log; tail -4 /app/solution/full_user.log; df -h /app/solution\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:30:56.092Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3d32d1","wall_time_seconds":0.00000463,"exit_code":0,"original_token_count":61,"output":"low 0\nhigh 0\nmax 2049\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\niter 13 148\niter 14 162\niter 15 175\nscore als_96_10_16 0.019441565821781404 185\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 22G 75G 23% /\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd2a04ad-6f04-4a31-955a-b67143bdc344", "process_id": "11435", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.events; tail -4 /app/solution/als.log; tail -4 /app/solution/full_user.log; df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.events; tail -4 /app/solution/als.log; tail -4 /app/solution/full_user.log; df -h /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "low 0\nhigh 0\nmax 2049\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\niter 13 148\niter 14 162\niter 15 175\nscore als_96_10_16 0.019441565821781404 185\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 22G 75G 23% /\n", "stderr": "", "aggregated_output": "low 0\nhigh 0\nmax 2049\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\niter 13 148\niter 14 162\niter 15 175\nscore als_96_10_16 0.019441565821781404 185\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 22G 75G 23% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4630 }, "formatted_output": "low 0\nhigh 0\nmax 2049\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\niter 13 148\niter 14 162\niter 15 175\nscore als_96_10_16 0.019441565821781404 185\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 22G 75G 23% /\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3640907ec87d2b6cfa4e2906dee23", "usage": { "input_tokens": 43772, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 76, "total_tokens": 43915 }, "turn_token_usage": { "input_tokens": 1049136, "cached_input_tokens": 1011840, "cache_write_input_tokens": 0, "output_tokens": 16805, "reasoning_output_tokens": 5668, "total_tokens": 1065941 }, "thread_token_usage": { "input_tokens": 1049136, "cached_input_tokens": 1011840, "cache_write_input_tokens": 0, "output_tokens": 16805, "reasoning_output_tokens": 5668, "total_tokens": 1065941 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d4d8076a-8b8a-4fb5-a687-c39a32b6d1d2", "process_id": "43203", "command": [ "/bin/sh", "-lc", "cat > /app/solution/temporal.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time\nfor part in ['train','val']:\n cutoff=24786800+(606600 if part=='val' else 0);shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['d1','d3','d14','org7','like7']}\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;strength=np.where(d.played_ratio_pct.values>=80,1,.5);org=d.is_organic.values\n for k,h in [('d1',1),('d3',3),('d14',14),('org7',7)]:\n w=np.exp2(-age/h)*strength*(org if k=='org7' else (.4+.6*org));mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\n d=pd.read_parquet(D/f'{part}_likes.parquet');c=I.get_indexer(d.item_id);r=U.get_indexer(d.uid);m=(r>=0)&(c>=0);age=(cutoff-d.timestamp.values.astype(np.float64)[m])/86400;mats['like7']=sp.coo_matrix((np.exp2(-age/7).astype(np.float32),(r[m],c[m])),shape=shape).tocsr()\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{part}_{k}.npz',v)\n print(part,flush=True)\nPY\npython - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"{'recent':30,'recentorg':30,'short':7}\",\"{'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7,'like7':7}\");open(p,'w').write(s)\np='/app/solution/full_user.py';s=open(p).read().replace(\"f'{stage}_usersim.npz'\",\"f'{stage}_full_usersim.npz'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/temporal.py > /app/solution/temporal.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/temporal.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time\nfor part in ['train','val']:\n cutoff=24786800+(606600 if part=='val' else 0);shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['d1','d3','d14','org7','like7']}\n for batch in pq.ParquetFile(D/f'{part}_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;strength=np.where(d.played_ratio_pct.values>=80,1,.5);org=d.is_organic.values\n for k,h in [('d1',1),('d3',3),('d14',14),('org7',7)]:\n w=np.exp2(-age/h)*strength*(org if k=='org7' else (.4+.6*org));mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\n d=pd.read_parquet(D/f'{part}_likes.parquet');c=I.get_indexer(d.item_id);r=U.get_indexer(d.uid);m=(r>=0)&(c>=0);age=(cutoff-d.timestamp.values.astype(np.float64)[m])/86400;mats['like7']=sp.coo_matrix((np.exp2(-age/7).astype(np.float32),(r[m],c[m])),shape=shape).tocsr()\n for k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'{part}_{k}.npz',v)\n print(part,flush=True)\nPY\npython - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"{'recent':30,'recentorg':30,'short':7}\",\"{'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7,'like7':7}\");open(p,'w').write(s)\np='/app/solution/full_user.py';s=open(p).read().replace(\"f'{stage}_usersim.npz'\",\"f'{stage}_full_usersim.npz'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/temporal.py > /app/solution/temporal.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 20, "nanos": 614474396 }, "formatted_output": "" } } ## Step 36 · agent · 2026-09-23T05:31:07.837Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4a3dd283-5061-46a0-a6d1-4648e527735d", "process_id": "58199", "command": [ "/bin/sh", "-lc", "cat > /app/solution/user_variants.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\nrs=[];cs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);v=np.take_along_axis(s,ix,axis=1);cs.append(ix);vs.append(v)\nix=np.concatenate(cs);v=np.concatenate(vs);np.savez(O/f'{stage}_usersim.npz',ix=ix,v=v)\nfor k,power,kind in [(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]:\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**power).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'uv_{k}_{power}_{kind}'\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/user_variants.py validation > /app/solution/user_variants.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/user_variants.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';t=time.time();tu,tr,mask=context(stage)\nx=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\nrs=[];cs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);v=np.take_along_axis(s,ix,axis=1);cs.append(ix);vs.append(v)\nix=np.concatenate(cs);v=np.concatenate(vs);np.savez(O/f'{stage}_usersim.npz',ix=ix,v=v)\nfor k,power,kind in [(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]:\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**power).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'uv_{k}_{power}_{kind}'\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/user_variants.py validation > /app/solution/user_variants.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 151, "nanos": 309006291 }, "formatted_output": "" } } ### exec · 2026-09-23T05:31:22.829Z { "code": "text(await tools.exec_command({cmd:\"cp /app/solution/validation_usersim.npz /app/solution/validation_cat_usersim.npz\\ncat > /app/solution/time_user.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport time,sys\\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';mode=sys.argv[2] if len(sys.argv)>2 else 'cat';t=time.time();tu,tr,mask=context(stage);a=np.load(O/f'{stage}_{mode}_usersim.npz');ix=a['ix'];v=a['v']\\nvariants=[(100,'d1'),(100,'d3'),(100,'d14'),(100,'org7'),(100,'like7'),(30,'short'),(300,'short')]\\nif len(sys.argv)>3:variants=[(int(sys.argv[3]),sys.argv[4])]\\nfor k,kind in variants:\\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**2).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'{mode}time_{k}_{kind}'\\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\\n for b in range(0,len(tu),256):\\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:31:24.195Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f5f718","wall_time_seconds":1.001231725,"session_id":25013,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab364105a9c87d281dc17f4cdfd4dc1", "usage": { "input_tokens": 44082, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 809, "reasoning_output_tokens": 309, "total_tokens": 44891 }, "turn_token_usage": { "input_tokens": 1093218, "cached_input_tokens": 1055488, "cache_write_input_tokens": 0, "output_tokens": 17614, "reasoning_output_tokens": 5977, "total_tokens": 1110832 }, "thread_token_usage": { "input_tokens": 1093218, "cached_input_tokens": 1055488, "cache_write_input_tokens": 0, "output_tokens": 17614, "reasoning_output_tokens": 5977, "total_tokens": 1110832 } } ## Step 37 · agent · 2026-09-23T05:31:41.617Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-67d5c59c-778a-4d06-a870-c8eabcd2caff", "process_id": "60830", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/full_history.py';s=open(p).read().replace('cutoff-d.timestamp.values','cutoff-d.timestamp.values.astype(np.float64)');open(p,'w').write(s)\np='/app/solution/user_variants.py';s=open(p).read().replace(\"x=profile(stage);\", \"x=sp.load_npz(O/f'{stage}_fullhistory.npz');\").replace(\"z=x@sp.diags(idf**.7)\",\"z=x.copy();z.data*=idf[z.indices]**.7\").replace(\"f'uv_{k}_{power}_{kind}'\",\"f'full_{k}_{power}_{kind}'\");s=s.replace(\"[(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]\", \"[(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'likes')]\")\nopen('/app/solution/full_user.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_user.py validation > /app/solution/full_user.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/full_history.py';s=open(p).read().replace('cutoff-d.timestamp.values','cutoff-d.timestamp.values.astype(np.float64)');open(p,'w').write(s)\np='/app/solution/user_variants.py';s=open(p).read().replace(\"x=profile(stage);\", \"x=sp.load_npz(O/f'{stage}_fullhistory.npz');\").replace(\"z=x@sp.diags(idf**.7)\",\"z=x.copy();z.data*=idf[z.indices]**.7\").replace(\"f'uv_{k}_{power}_{kind}'\",\"f'full_{k}_{power}_{kind}'\");s=s.replace(\"[(20,2,'recentorg'),(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'recent'),(100,2,'organic'),(100,3,'recentorg'),(100,1,'recentorg'),(50,2,'likes')]\", \"[(50,2,'recentorg'),(100,2,'recentorg'),(400,2,'recentorg'),(100,2,'short'),(100,2,'likes')]\")\nopen('/app/solution/full_user.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_history.py validation > /app/solution/full_history.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/full_user.py validation > /app/solution/full_user.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 109, "nanos": 713512073 }, "formatted_output": "" } } ## Step 38 · agent · 2026-09-23T05:31:52.537Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:31:54.670Z { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/time_user.log; tail -8 /app/solution/full_user.log; tail -4 /app/solution/user_variants.log; cat /app/solution/temporal.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:31:54.842Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"52ec50","wall_time_seconds":0.000005121,"exit_code":0,"original_token_count":108,"output":"cattime_100_d1 0.03741256215545792 17\nfull_50_2_recentorg 0.027785263189234643 47\nfull_100_2_recentorg 0.029041334684059998 61\nfull_400_2_recentorg 0.027957159028024144 74\nfull_100_2_short 0.04002055244040366 89\nfull_100_2_likes 0.01649774463549619 100\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\nuv_100_1_recentorg 0.030889164852883698 136\nuv_50_2_likes 0.015070099955942063 150\ntrain\nval\n"} {"current_time":"2026-09-23 05:31:54 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3642c7b2487d2a1c31cff5f69990e", "usage": { "input_tokens": 44949, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 965, "reasoning_output_tokens": 885, "total_tokens": 45914 }, "turn_token_usage": { "input_tokens": 1138167, "cached_input_tokens": 1099392, "cache_write_input_tokens": 0, "output_tokens": 18579, "reasoning_output_tokens": 6862, "total_tokens": 1156746 }, "thread_token_usage": { "input_tokens": 1138167, "cached_input_tokens": 1099392, "cache_write_input_tokens": 0, "output_tokens": 18579, "reasoning_output_tokens": 6862, "total_tokens": 1156746 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c0a8e082-08f8-4784-bde6-16603476379c", "process_id": "93555", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/time_user.log; tail -8 /app/solution/full_user.log; tail -4 /app/solution/user_variants.log; cat /app/solution/temporal.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/time_user.log; tail -8 /app/solution/full_user.log; tail -4 /app/solution/user_variants.log; cat /app/solution/temporal.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cattime_100_d1 0.03741256215545792 17\nfull_50_2_recentorg 0.027785263189234643 47\nfull_100_2_recentorg 0.029041334684059998 61\nfull_400_2_recentorg 0.027957159028024144 74\nfull_100_2_short 0.04002055244040366 89\nfull_100_2_likes 0.01649774463549619 100\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\nuv_100_1_recentorg 0.030889164852883698 136\nuv_50_2_likes 0.015070099955942063 150\ntrain\nval\n", "stderr": "", "aggregated_output": "cattime_100_d1 0.03741256215545792 17\nfull_50_2_recentorg 0.027785263189234643 47\nfull_100_2_recentorg 0.029041334684059998 61\nfull_400_2_recentorg 0.027957159028024144 74\nfull_100_2_short 0.04002055244040366 89\nfull_100_2_likes 0.01649774463549619 100\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\nuv_100_1_recentorg 0.030889164852883698 136\nuv_50_2_likes 0.015070099955942063 150\ntrain\nval\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5121 }, "formatted_output": "cattime_100_d1 0.03741256215545792 17\nfull_50_2_recentorg 0.027785263189234643 47\nfull_100_2_recentorg 0.029041334684059998 61\nfull_400_2_recentorg 0.027957159028024144 74\nfull_100_2_short 0.04002055244040366 89\nfull_100_2_likes 0.01649774463549619 100\nuv_100_2_organic 0.020089911528997646 106\nuv_100_3_recentorg 0.030670136356580155 119\nuv_100_1_recentorg 0.030889164852883698 136\nuv_50_2_likes 0.015070099955942063 150\ntrain\nval\n" } } ## Step 39 · agent · 2026-09-23T05:32:15.258Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c85423ce-e508-4b01-a82c-fd0f91f1a981", "process_id": "25013", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_usersim.npz /app/solution/validation_cat_usersim.npz\ncat > /app/solution/time_user.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';mode=sys.argv[2] if len(sys.argv)>2 else 'cat';t=time.time();tu,tr,mask=context(stage);a=np.load(O/f'{stage}_{mode}_usersim.npz');ix=a['ix'];v=a['v']\nvariants=[(100,'d1'),(100,'d3'),(100,'d14'),(100,'org7'),(100,'like7'),(30,'short'),(300,'short')]\nif len(sys.argv)>3:variants=[(int(sys.argv[3]),sys.argv[4])]\nfor k,kind in variants:\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**2).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'{mode}time_{k}_{kind}'\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_usersim.npz /app/solution/validation_cat_usersim.npz\ncat > /app/solution/time_user.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport time,sys\nstage=sys.argv[1] if len(sys.argv)>1 else 'validation';mode=sys.argv[2] if len(sys.argv)>2 else 'cat';t=time.time();tu,tr,mask=context(stage);a=np.load(O/f'{stage}_{mode}_usersim.npz');ix=a['ix'];v=a['v']\nvariants=[(100,'d1'),(100,'d3'),(100,'d14'),(100,'org7'),(100,'like7'),(30,'short'),(300,'short')]\nif len(sys.argv)>3:variants=[(int(sys.argv[3]),sys.argv[4])]\nfor k,kind in variants:\n p=matrix(kind,stage);p.data=np.log1p(p.data);sim=sp.coo_matrix(((v[:,:k]**2).ravel(),(np.repeat(np.arange(len(tu)),k),ix[:,:k].ravel())),shape=(len(tu),len(uids))).tocsr();name=f'{mode}time_{k}_{kind}'\n out=np.lib.format.open_memmap(O/f'{stage}_{name}.npy',mode='w+',dtype='float16',shape=mask.shape);ixs=[]\n for b in range(0,len(tu),256):\n scores=(sim[b:b+256]@p).toarray();scores[~mask[b:b+256]]=-np.inf;ixs.append(top(scores));out[b:b+256]=scores\n out.flush();indices=np.concatenate(ixs);print(name,evaluate(indices) if stage=='validation' else '',round(time.time()-t),flush=True);save(indices,stage,f'{stage}_{name}.parquet')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 106, "nanos": 127189603 }, "formatted_output": "" } } ### exec · 2026-09-23T05:33:16.801Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/build_past.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport pyarrow.parquet as pq,time\\ncutoff=24786800-606600;shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes','d1','d3','d14','org7','like7']};yr=[];yc=[]\\nfor batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=2000000):\\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;org=d.is_organic.values;strength=np.where(d.played_ratio_pct.values>=80,1,.5);future=(age<-1800/86400)&(org==1)&(strength==1);yr.append(r[future]);yc.append(c[future]);old=age>=0;r=r[old];c=c[old];age=age[old];org=org[old];strength=strength[old]\\n for k in mats:\\n if k in ['likes','like7']:continue\\n if k=='count':w=np.ones(len(r))\\n elif k=='organic':w=org*strength\\n else:\\n h={'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7}[k];w=np.exp2(-age/h)*strength*(org if k in ['recentorg','org7'] else (.4+.6*org))\\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\\nx=pd.read_parquet(D/'train_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);valid=(c>=0)&(r>=0);x=x[valid];c=c[valid];r=r[valid];age=(cutoff-x.timestamp.values.astype(np.float64))/86400;old=age>=0\\nmats['likes']=sp.coo_matrix((np.ones(old.sum(),np.float32),(r[old],c[old])),shape=shape).tocsr();mats['like7']=sp.coo_matrix((np.exp2(-age[old]/7).astype(np.float32),(r[old],c[old])),shape=shape).tocsr()\\nfor k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'past_{k}.npz',v)\\na=np.load(D/'validation_eligible_candidates.npz');tu=a['uids'];tr=U.get_indexer(tu);mask=np.ones((len(tu),len(items)),bool);known=(mats['count']+mats['likes'])[tr].tocoo();mask[known.row,known.col]=False\\n# Prior dislikes are withheld for this auxiliary training window.\\nd=pd.read_parquet(D/'train_dislikes.parquet');d=d[d.timestamp<=cutoff];rr=pd.Index(tu).get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0);mask[rr[m],cc[m]]=False\\nnp.savez_compressed(O/'past_eligible_candidates.npz',uids=tu,item_ids=items,packed_eligible=np.packbits(mask,axis=1,bitorder='little'))\\ny=sp.coo_matrix((np.ones(sum(map(len,yr)),np.float32),(np.concatenate(yr),np.concatenate(yc))),shape=shape).tocsr();y.data[:]=1;f=(age<-1800/86400)&(x.is_organic.values==1);l=sp.coo_matrix((np.full(f.sum(),7,np.float32),(r[f],c[f])),shape=shape).tocsr();l.data[:]=7;y=y.maximum(l)[tr].multiply(mask).tocsr();y.eliminate_zeros();sp.save_npz(O/'past_truth.npz',y);print('past complete',y.nnz,flush=True)\\nPY\\npython - <<'PY'\\np='/app/solution/common.py';s=open(p).read().replace(\\\"def matrix(k,stage='validation'):\\\\n\\\", \\\"def matrix(k,stage='validation'):\\\\n if stage=='past':return sp.load_npz(O/f'past_{k}.npz')\\\\n\\\").replace(\\\"a=np.load(D/f'{stage}_eligible_candidates.npz');\\\", \\\"a=np.load((O if stage=='past' else D)/f'{stage}_eligible_candidates.npz');\\\");open(p,'w').write(s)\\np='/app/solution/time_user.py';s=open(p).read().replace(\\\"variants=[(100,'d1')\\\", \\\"variants=[(100,'short'),(100,'d1')\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/build_past.py > /app/solution/past.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:33:17.971Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"cbf4c2","wall_time_seconds":1.000730914,"session_id":11109,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36477474087d2b5b61b8eec28fa92", "usage": { "input_tokens": 46681, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 1195, "reasoning_output_tokens": 0, "total_tokens": 47876 }, "turn_token_usage": { "input_tokens": 1184848, "cached_input_tokens": 1145344, "cache_write_input_tokens": 0, "output_tokens": 19774, "reasoning_output_tokens": 6862, "total_tokens": 1204622 }, "thread_token_usage": { "input_tokens": 1184848, "cached_input_tokens": 1145344, "cache_write_input_tokens": 0, "output_tokens": 19774, "reasoning_output_tokens": 6862, "total_tokens": 1204622 } } ## Step 40 · agent · 2026-09-23T05:33:37.996Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f00d6b3-dc54-429e-8385-b13b8d8fd8e6", "process_id": "11109", "command": [ "/bin/sh", "-lc", "cat > /app/solution/build_past.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time\ncutoff=24786800-606600;shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes','d1','d3','d14','org7','like7']};yr=[];yc=[]\nfor batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;org=d.is_organic.values;strength=np.where(d.played_ratio_pct.values>=80,1,.5);future=(age<-1800/86400)&(org==1)&(strength==1);yr.append(r[future]);yc.append(c[future]);old=age>=0;r=r[old];c=c[old];age=age[old];org=org[old];strength=strength[old]\n for k in mats:\n if k in ['likes','like7']:continue\n if k=='count':w=np.ones(len(r))\n elif k=='organic':w=org*strength\n else:\n h={'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7}[k];w=np.exp2(-age/h)*strength*(org if k in ['recentorg','org7'] else (.4+.6*org))\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nx=pd.read_parquet(D/'train_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);valid=(c>=0)&(r>=0);x=x[valid];c=c[valid];r=r[valid];age=(cutoff-x.timestamp.values.astype(np.float64))/86400;old=age>=0\nmats['likes']=sp.coo_matrix((np.ones(old.sum(),np.float32),(r[old],c[old])),shape=shape).tocsr();mats['like7']=sp.coo_matrix((np.exp2(-age[old]/7).astype(np.float32),(r[old],c[old])),shape=shape).tocsr()\nfor k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'past_{k}.npz',v)\na=np.load(D/'validation_eligible_candidates.npz');tu=a['uids'];tr=U.get_indexer(tu);mask=np.ones((len(tu),len(items)),bool);known=(mats['count']+mats['likes'])[tr].tocoo();mask[known.row,known.col]=False\n# Prior dislikes are withheld for this auxiliary training window.\nd=pd.read_parquet(D/'train_dislikes.parquet');d=d[d.timestamp<=cutoff];rr=pd.Index(tu).get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0);mask[rr[m],cc[m]]=False\nnp.savez_compressed(O/'past_eligible_candidates.npz',uids=tu,item_ids=items,packed_eligible=np.packbits(mask,axis=1,bitorder='little'))\ny=sp.coo_matrix((np.ones(sum(map(len,yr)),np.float32),(np.concatenate(yr),np.concatenate(yc))),shape=shape).tocsr();y.data[:]=1;f=(age<-1800/86400)&(x.is_organic.values==1);l=sp.coo_matrix((np.full(f.sum(),7,np.float32),(r[f],c[f])),shape=shape).tocsr();l.data[:]=7;y=y.maximum(l)[tr].multiply(mask).tocsr();y.eliminate_zeros();sp.save_npz(O/'past_truth.npz',y);print('past complete',y.nnz,flush=True)\nPY\npython - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"def matrix(k,stage='validation'):\\n\", \"def matrix(k,stage='validation'):\\n if stage=='past':return sp.load_npz(O/f'past_{k}.npz')\\n\").replace(\"a=np.load(D/f'{stage}_eligible_candidates.npz');\", \"a=np.load((O if stage=='past' else D)/f'{stage}_eligible_candidates.npz');\");open(p,'w').write(s)\np='/app/solution/time_user.py';s=open(p).read().replace(\"variants=[(100,'d1')\", \"variants=[(100,'short'),(100,'d1')\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/build_past.py > /app/solution/past.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/build_past.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport pyarrow.parquet as pq,time\ncutoff=24786800-606600;shape=(len(uids),len(items));mats={k:sp.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','recentorg','short','likes','d1','d3','d14','org7','like7']};yr=[];yc=[]\nfor batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=2000000):\n d=batch.to_pandas();c=I.get_indexer(d.item_id);m=(c>=0)&(d.played_ratio_pct.values>=50);d=d[m];c=c[m];r=U.get_indexer(d.uid);age=(cutoff-d.timestamp.values.astype(np.float64))/86400;org=d.is_organic.values;strength=np.where(d.played_ratio_pct.values>=80,1,.5);future=(age<-1800/86400)&(org==1)&(strength==1);yr.append(r[future]);yc.append(c[future]);old=age>=0;r=r[old];c=c[old];age=age[old];org=org[old];strength=strength[old]\n for k in mats:\n if k in ['likes','like7']:continue\n if k=='count':w=np.ones(len(r))\n elif k=='organic':w=org*strength\n else:\n h={'recent':30,'recentorg':30,'short':7,'d1':1,'d3':3,'d14':14,'org7':7}[k];w=np.exp2(-age/h)*strength*(org if k in ['recentorg','org7'] else (.4+.6*org))\n mats[k]+=sp.coo_matrix((w.astype(np.float32),(r,c)),shape=shape).tocsr()\nx=pd.read_parquet(D/'train_likes.parquet');c=I.get_indexer(x.item_id);r=U.get_indexer(x.uid);valid=(c>=0)&(r>=0);x=x[valid];c=c[valid];r=r[valid];age=(cutoff-x.timestamp.values.astype(np.float64))/86400;old=age>=0\nmats['likes']=sp.coo_matrix((np.ones(old.sum(),np.float32),(r[old],c[old])),shape=shape).tocsr();mats['like7']=sp.coo_matrix((np.exp2(-age[old]/7).astype(np.float32),(r[old],c[old])),shape=shape).tocsr()\nfor k,v in mats.items():v.eliminate_zeros();sp.save_npz(O/f'past_{k}.npz',v)\na=np.load(D/'validation_eligible_candidates.npz');tu=a['uids'];tr=U.get_indexer(tu);mask=np.ones((len(tu),len(items)),bool);known=(mats['count']+mats['likes'])[tr].tocoo();mask[known.row,known.col]=False\n# Prior dislikes are withheld for this auxiliary training window.\nd=pd.read_parquet(D/'train_dislikes.parquet');d=d[d.timestamp<=cutoff];rr=pd.Index(tu).get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0);mask[rr[m],cc[m]]=False\nnp.savez_compressed(O/'past_eligible_candidates.npz',uids=tu,item_ids=items,packed_eligible=np.packbits(mask,axis=1,bitorder='little'))\ny=sp.coo_matrix((np.ones(sum(map(len,yr)),np.float32),(np.concatenate(yr),np.concatenate(yc))),shape=shape).tocsr();y.data[:]=1;f=(age<-1800/86400)&(x.is_organic.values==1);l=sp.coo_matrix((np.full(f.sum(),7,np.float32),(r[f],c[f])),shape=shape).tocsr();l.data[:]=7;y=y.maximum(l)[tr].multiply(mask).tocsr();y.eliminate_zeros();sp.save_npz(O/'past_truth.npz',y);print('past complete',y.nnz,flush=True)\nPY\npython - <<'PY'\np='/app/solution/common.py';s=open(p).read().replace(\"def matrix(k,stage='validation'):\\n\", \"def matrix(k,stage='validation'):\\n if stage=='past':return sp.load_npz(O/f'past_{k}.npz')\\n\").replace(\"a=np.load(D/f'{stage}_eligible_candidates.npz');\", \"a=np.load((O if stage=='past' else D)/f'{stage}_eligible_candidates.npz');\");open(p,'w').write(s)\np='/app/solution/time_user.py';s=open(p).read().replace(\"variants=[(100,'d1')\", \"variants=[(100,'short'),(100,'d1')\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/build_past.py > /app/solution/past.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 33, "nanos": 326975142 }, "formatted_output": "" } } ### exec · 2026-09-23T05:35:37.251Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/make_neighbors.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport sys\\nstage=sys.argv[1];tu,tr,mask=context(stage);x=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\\ncs=[];vs=[]\\nfor b in range(0,len(tu),256):\\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\\nnp.savez(O/f'{stage}_cat_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,'neighbors done',flush=True)\\nPY\\ncat > /app/solution/meta.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nos.environ['OMP_NUM_THREADS']='4'\\nfrom common import *\\nimport sys,pickle,time,json\\nfrom sklearn.ensemble import HistGradientBoostingClassifier\\nnames=['cattime_100_'+k for k in ['short','d1','d3','d14','org7','like7']]+['cattime_300_short']\\nclass Data:\\n def __init__(self,stage):\\n self.stage=stage;self.tu,self.tr,self.mask=context(stage);self.scores=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in names];self.pops={};self.uc=[]\\n for k in ['count','organic','likes','recent','recentorg','short','d1','d3','d14','org7','like7']:\\n x=matrix(k,stage);self.pops[k]=np.asarray(x.sum(axis=0)).ravel();self.uc.append(np.log1p(np.asarray(x.sum(axis=1)).ravel()[self.tr]))\\n x=matrix('count',stage);self.pops['degree']=np.asarray((x>0).sum(axis=0)).ravel();self.uc=np.array(self.uc).T\\n pool=[]\\n for s in self.scores:pool.append(np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(self.tu),256)]))\\n for k in ['short','d1','d3','like7']:\\n p=self.pops[k];pool.append(np.concatenate([top(np.where(self.mask[b:b+256],p,-np.inf),150) for b in range(0,len(self.tu),256)]))\\n self.pool=np.sort(np.concatenate(pool,axis=1),axis=1);self.valid=np.ones(self.pool.shape,bool);self.valid[:,1:]=self.pool[:,1:]!=self.pool[:,:-1]\\n self.maxs=np.stack([np.maximum(np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8) for s in self.scores],axis=1)\\n def features(self,r,c):\\n f=[]\\n for j,s in enumerate(self.scores):\\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\\n for p in self.pops.values():f.append(np.log1p(p[c]))\\n for a,b in [('d1','d3'),('d3','short'),('short','d14'),('short','recent'),('recent','count'),('organic','count'),('org7','short'),('like7','short'),('likes','organic')]:f.append(np.log((self.pops[a][c]+.01)/(self.pops[b][c]+.1)))\\n for j in range(self.uc.shape[1]):f.append(self.uc[r,j])\\n return np.stack(f,axis=1).astype(np.float32)\\n def sample(self):\\n y=sp.load_npz(O/'past_truth.npz') if self.stage=='past' else truth()[0];discount=1/np.log2(np.arange(2,12));idcg=np.zeros(len(self.tu))\\n for r in range(len(self.tu)):\\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\\n rng=np.random.RandomState(42);rr=[];cc=[];ww=[];yy=[]\\n for r in np.flatnonzero(idcg):\\n c=self.pool[r,self.valid[r]];g=y[r,c].toarray().ravel();pos=g>0;neg=np.flatnonzero(~pos);sel=np.concatenate([np.flatnonzero(pos),rng.choice(neg,min(120,len(neg)),replace=False)]);rr.extend([r]*len(sel));cc.extend(c[sel]);yy.extend(g[sel]>0);w=np.where(g[sel]>0,g[sel],len(neg)/min(120,len(neg)));ww.extend(w/np.sqrt(idcg[r]))\\n r=np.array(rr);c=np.array(cc);print('samples',self.stage,len(r),sum(yy),flush=True);return self.features(r,c),np.array(yy),np.array(ww,dtype=np.float32)\\n def predict(self,model):\\n out=np.empty(self.pool.shape,np.float32)\\n for b in range(0,len(self.tu),64):\\n p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\\n out[~self.valid]=-np.inf;return out\\nif __name__=='__main__':\\n mode=sys.argv[1];t=time.time()\\n if mode=='train':\\n d=Data('past');x,y,w=d.sample();np.savez(O/'meta_past_samples.npz',x=x,y=y,w=w);del d\\n val=Data('validation');best=0\\n for leaves in [15,31]:\\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(x,y,sample_weight=w);s=val.predict(model);ix=np.take_along_axis(val.pool,top(s),axis=1);v=evaluate(ix);print('meta',leaves,v,round(time.time()-t),flush=True);save(ix,'validation',f'validation_meta_{leaves}.parquet');np.save(O/f'validation_meta_{leaves}_pool_scores.npy',s)\\n if v>best:best=v;pickle.dump(model,open(O/'meta_model.pkl','wb'));json.dump({'leaves':leaves,'score':v},open(O/'meta_config.json','w'));save(ix,'validation','validation_meta.parquet')\\n np.savez_compressed(O/'meta_validation_pool.npz',pool=val.pool,valid=val.valid);vx,vy,vw=val.sample();np.savez(O/'meta_val_samples.npz',x=vx,y=vy,w=vw)\\n elif mode=='final':\\n a=np.load(O/'meta_past_samples.npz');b=np.load(O/'meta_val_samples.npz');config=json.load(open(O/'meta_config.json'));model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=config['leaves'],min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'meta_final_model.pkl','wb'));d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_meta.parquet');print('final meta done',round(time.time()-t),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py past > /app/solution/past_neighbors.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past cat > /app/solution/past_predict.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:35:38.432Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fc012f","wall_time_seconds":1.001419496,"session_id":40333,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab364eab50c87d288b32fdc5e124d47", "usage": { "input_tokens": 48395, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 2000, "reasoning_output_tokens": 0, "total_tokens": 50395 }, "turn_token_usage": { "input_tokens": 1233243, "cached_input_tokens": 1193088, "cache_write_input_tokens": 0, "output_tokens": 21774, "reasoning_output_tokens": 6862, "total_tokens": 1255017 }, "thread_token_usage": { "input_tokens": 1233243, "cached_input_tokens": 1193088, "cache_write_input_tokens": 0, "output_tokens": 21774, "reasoning_output_tokens": 6862, "total_tokens": 1255017 } } ## Step 41 · agent · 2026-09-23T05:35:43.192Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:35:46.302Z { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/time_user.log; cat /app/solution/past.log; tail -3 /app/solution/past_neighbors.log; tail -4 /app/solution/past_predict.log; ls -lh /app/solution/validation_cat_usersim.npz /app/solution/validation_usersim.npz /app/solution/validation_full_usersim.npz\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:35:46.380Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3072c2","wall_time_seconds":0.000004051,"exit_code":2,"original_token_count":161,"output":"cattime_100_d1 0.03741256215545792 17\ncattime_100_d3 0.04312271169223471 33\ncattime_100_d14 0.034427320688311176 47\ncattime_100_org7 0.03877375096614469 60\ncattime_100_like7 0.020956569882531015 72\ncattime_30_short 0.03254667673454012 85\ncattime_300_short 0.041673902546757416 105\npast complete 46402\ntail: cannot open '/app/solution/past_predict.log' for reading: No such file or directory\nls: cannot access '/app/solution/validation_full_usersim.npz': No such file or directory\n-rw-r--r-- 1 agent agent 39M Sep 23 05:31 /app/solution/validation_cat_usersim.npz\n-rw-r--r-- 1 agent agent 39M Sep 23 05:30 /app/solution/validation_usersim.npz\n"} {"current_time":"2026-09-23 05:35:46 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5a84bc31-2263-4ed8-bdac-f8017bc3511a", "process_id": "74448", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/time_user.log; cat /app/solution/past.log; tail -3 /app/solution/past_neighbors.log; tail -4 /app/solution/past_predict.log; ls -lh /app/solution/validation_cat_usersim.npz /app/solution/validation_usersim.npz /app/solution/validation_full_usersim.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/time_user.log; cat /app/solution/past.log; tail -3 /app/solution/past_neighbors.log; tail -4 /app/solution/past_predict.log; ls -lh /app/solution/validation_cat_usersim.npz /app/solution/validation_usersim.npz /app/solution/validation_full_usersim.npz" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cattime_100_d1 0.03741256215545792 17\ncattime_100_d3 0.04312271169223471 33\ncattime_100_d14 0.034427320688311176 47\ncattime_100_org7 0.03877375096614469 60\ncattime_100_like7 0.020956569882531015 72\ncattime_30_short 0.03254667673454012 85\ncattime_300_short 0.041673902546757416 105\npast complete 46402\ntail: cannot open '/app/solution/past_predict.log' for reading: No such file or directory\nls: cannot access '/app/solution/validation_full_usersim.npz': No such file or directory\n-rw-r--r-- 1 agent agent 39M Sep 23 05:31 /app/solution/validation_cat_usersim.npz\n-rw-r--r-- 1 agent agent 39M Sep 23 05:30 /app/solution/validation_usersim.npz\n", "stderr": "", "aggregated_output": "cattime_100_d1 0.03741256215545792 17\ncattime_100_d3 0.04312271169223471 33\ncattime_100_d14 0.034427320688311176 47\ncattime_100_org7 0.03877375096614469 60\ncattime_100_like7 0.020956569882531015 72\ncattime_30_short 0.03254667673454012 85\ncattime_300_short 0.041673902546757416 105\npast complete 46402\ntail: cannot open '/app/solution/past_predict.log' for reading: No such file or directory\nls: cannot access '/app/solution/validation_full_usersim.npz': No such file or directory\n-rw-r--r-- 1 agent agent 39M Sep 23 05:31 /app/solution/validation_cat_usersim.npz\n-rw-r--r-- 1 agent agent 39M Sep 23 05:30 /app/solution/validation_usersim.npz\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4051 }, "formatted_output": "cattime_100_d1 0.03741256215545792 17\ncattime_100_d3 0.04312271169223471 33\ncattime_100_d14 0.034427320688311176 47\ncattime_100_org7 0.03877375096614469 60\ncattime_100_like7 0.020956569882531015 72\ncattime_30_short 0.03254667673454012 85\ncattime_300_short 0.041673902546757416 105\npast complete 46402\ntail: cannot open '/app/solution/past_predict.log' for reading: No such file or directory\nls: cannot access '/app/solution/validation_full_usersim.npz': No such file or directory\n-rw-r--r-- 1 agent agent 39M Sep 23 05:31 /app/solution/validation_cat_usersim.npz\n-rw-r--r-- 1 agent agent 39M Sep 23 05:30 /app/solution/validation_usersim.npz\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3652aad1887d2a840b61b7b892e96", "usage": { "input_tokens": 50452, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 130, "reasoning_output_tokens": 14, "total_tokens": 50582 }, "turn_token_usage": { "input_tokens": 1283695, "cached_input_tokens": 1241344, "cache_write_input_tokens": 0, "output_tokens": 21904, "reasoning_output_tokens": 6876, "total_tokens": 1305599 }, "thread_token_usage": { "input_tokens": 1283695, "cached_input_tokens": 1241344, "cache_write_input_tokens": 0, "output_tokens": 21904, "reasoning_output_tokens": 6876, "total_tokens": 1305599 } } ## Step 42 · agent · 2026-09-23T05:35:56.678Z ### Thinking unavailable — no text recorded The best score is now 0.043123, with three-day recency helping most. I’m also building an earlier holdout from the training period to train a reranker, so its public-validation features will remain strictly based on training history. Final-history refitting is starting alongside that comparison. ### exec · 2026-09-23T05:36:05.654Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py validation > /app/solution/validation_neighbors.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user2.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta.py train > /app/solution/meta.log 2>&1\",\"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/make_neighbors.py test > /app/solution/test_neighbors.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test cat > /app/solution/test_predict.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:36:08.006Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"b92cf7","wall_time_seconds":1.000423888,"session_id":40689,"original_token_count":0,"output":""} {"chunk_id":"0f4047","wall_time_seconds":1.001261661,"session_id":4900,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365329dc087d281a2e17ba736e612", "usage": { "input_tokens": 50901, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 512, "reasoning_output_tokens": 205, "total_tokens": 51413 }, "turn_token_usage": { "input_tokens": 1334596, "cached_input_tokens": 1291648, "cache_write_input_tokens": 0, "output_tokens": 22416, "reasoning_output_tokens": 7081, "total_tokens": 1357012 }, "thread_token_usage": { "input_tokens": 1334596, "cached_input_tokens": 1291648, "cache_write_input_tokens": 0, "output_tokens": 22416, "reasoning_output_tokens": 7081, "total_tokens": 1357012 } } ## Step 43 · agent · 2026-09-23T05:36:27.042Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:36:37.355Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\\\"p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\\\",\\\"p=self.pool[b:b+64];v=self.valid[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r[v.ravel()],p[v]);out[b:b+64]=-np.inf;out[b:b+64][v]=model.predict_proba(f)[:,1]\\\")\\nopen(p,'w').write(s)\\np='/app/solution/tune.py';s=open(p).read().replace(\\\"['count','organic','recent','recentorg','short','likes']\\\",\\\"['short','d1','d3','like7']\\\").replace(\\\"['pop_short','pop_recentorg','pop_likes']\\\", \\\"['pop_short','pop_d1','pop_d3']\\\").replace(\\\"for p in [.25,.5,1,2]:\\\", \\\"for p in [.5,1,2]:\\\").replace(\\\"for w in [.1,.3,.6,1,2,4]:\\\", \\\"for w in [.3,.6,1,2]:\\\");open('/app/solution/tune_temporal.py','w').write(s)\\nPY\\ntail -6 /app/solution/past_predict.log; tail -3 /app/solution/time_user2.log; tail -3 /app/solution/test_predict.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1200}));\n" } ### Tool output · 2026-09-23T05:36:38.176Z Script completed Wall time 0.6 seconds Output: {"chunk_id":"5f628f","wall_time_seconds":0.00000325,"exit_code":0,"original_token_count":29,"output":"cattime_100_short 15\ncattime_100_d1 32\nlow 0\nhigh 0\nmax 3766\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36548353887d28b2ff7aae8d481f2", "usage": { "input_tokens": 51506, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 903, "reasoning_output_tokens": 516, "total_tokens": 52409 }, "turn_token_usage": { "input_tokens": 1386102, "cached_input_tokens": 1342336, "cache_write_input_tokens": 0, "output_tokens": 23319, "reasoning_output_tokens": 7597, "total_tokens": 1409421 }, "thread_token_usage": { "input_tokens": 1386102, "cached_input_tokens": 1342336, "cache_write_input_tokens": 0, "output_tokens": 23319, "reasoning_output_tokens": 7597, "total_tokens": 1409421 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d1abedde-4c14-4f36-8524-dde9de63683f", "process_id": "27428", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\"p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\",\"p=self.pool[b:b+64];v=self.valid[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r[v.ravel()],p[v]);out[b:b+64]=-np.inf;out[b:b+64][v]=model.predict_proba(f)[:,1]\")\nopen(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"['count','organic','recent','recentorg','short','likes']\",\"['short','d1','d3','like7']\").replace(\"['pop_short','pop_recentorg','pop_likes']\", \"['pop_short','pop_d1','pop_d3']\").replace(\"for p in [.25,.5,1,2]:\", \"for p in [.5,1,2]:\").replace(\"for w in [.1,.3,.6,1,2,4]:\", \"for w in [.3,.6,1,2]:\");open('/app/solution/tune_temporal.py','w').write(s)\nPY\ntail -6 /app/solution/past_predict.log; tail -3 /app/solution/time_user2.log; tail -3 /app/solution/test_predict.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\"p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\",\"p=self.pool[b:b+64];v=self.valid[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r[v.ravel()],p[v]);out[b:b+64]=-np.inf;out[b:b+64][v]=model.predict_proba(f)[:,1]\")\nopen(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"['count','organic','recent','recentorg','short','likes']\",\"['short','d1','d3','like7']\").replace(\"['pop_short','pop_recentorg','pop_likes']\", \"['pop_short','pop_d1','pop_d3']\").replace(\"for p in [.25,.5,1,2]:\", \"for p in [.5,1,2]:\").replace(\"for w in [.1,.3,.6,1,2,4]:\", \"for w in [.3,.6,1,2]:\");open('/app/solution/tune_temporal.py','w').write(s)\nPY\ntail -6 /app/solution/past_predict.log; tail -3 /app/solution/time_user2.log; tail -3 /app/solution/test_predict.log; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cattime_100_short 15\ncattime_100_d1 32\nlow 0\nhigh 0\nmax 3766\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "cattime_100_short 15\ncattime_100_d1 32\nlow 0\nhigh 0\nmax 3766\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3250 }, "formatted_output": "cattime_100_short 15\ncattime_100_d1 32\nlow 0\nhigh 0\nmax 3766\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } ## Step 44 · agent · 2026-09-23T05:36:59.634Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:37:16.234Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/neighbors_extra.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport sys,time\\nstage=sys.argv[1];mode=sys.argv[2];t=time.time();tu,tr,mask=context(stage);x=profile(stage)\\nif mode=='binary':x=profile(stage,'binary')\\nif mode=='recent':\\n r=matrix('recent',stage);r.data=np.log1p(r.data);x=x*.25+r\\nif mode=='organic':x=profile(stage,'organic')\\ndf=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\\ncs=[];vs=[]\\nfor b in range(0,len(tu),256):\\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,1000);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\\nnp.savez(O/f'{stage}_{mode}_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,mode,'neighbors done',time.time()-t,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation recent > /app/solution/extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 100 d3 >> /app/solution/extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 d3 >> /app/solution/extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation binary >> /app/solution/extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 100 d3 >> /app/solution/extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 500 d3 >> /app/solution/extra.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/past_predict.log; tail -6 /app/solution/time_user2.log; tail -6 /app/solution/test_predict.log; cat /app/solution/meta.log\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:37:17.478Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b59420","wall_time_seconds":1.001048308,"session_id":98480,"original_token_count":0,"output":""} {"chunk_id":"d8db98","wall_time_seconds":0.00000445,"exit_code":1,"original_token_count":69,"output":"cattime_100_short 15\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_short 21\ncattime_100_d1 40\ncat: /app/solution/meta.log: No such file or directory\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3656673dc87d2a547e6f464cdaadb", "usage": { "input_tokens": 52526, "cached_input_tokens": 51328, "cache_write_input_tokens": 0, "output_tokens": 1176, "reasoning_output_tokens": 516, "total_tokens": 53702 }, "turn_token_usage": { "input_tokens": 1438628, "cached_input_tokens": 1393664, "cache_write_input_tokens": 0, "output_tokens": 24495, "reasoning_output_tokens": 8113, "total_tokens": 1463123 }, "thread_token_usage": { "input_tokens": 1438628, "cached_input_tokens": 1393664, "cache_write_input_tokens": 0, "output_tokens": 24495, "reasoning_output_tokens": 8113, "total_tokens": 1463123 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf4489dd-b579-49f6-9858-fa8a8f01dfe2", "process_id": "51248", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/past_predict.log; tail -6 /app/solution/time_user2.log; tail -6 /app/solution/test_predict.log; cat /app/solution/meta.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/past_predict.log; tail -6 /app/solution/time_user2.log; tail -6 /app/solution/test_predict.log; cat /app/solution/meta.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "cattime_100_short 15\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_short 21\ncattime_100_d1 40\ncat: /app/solution/meta.log: No such file or directory\n", "stderr": "", "aggregated_output": "cattime_100_short 15\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_short 21\ncattime_100_d1 40\ncat: /app/solution/meta.log: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4450 }, "formatted_output": "cattime_100_short 15\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_short 21\ncattime_100_d1 40\ncat: /app/solution/meta.log: No such file or directory\n" } } ## Step 45 · agent · 2026-09-23T05:37:21.047Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:37:25.638Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,shutil\\np=Path('/app/solution');src=p/'test_cattime_100_short.parquet'\\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Updated submission to refitted one-week collaborative ranker')\\nPY\\ntail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -4 /app/solution/extra.log\",\"max_output_tokens\":1200}));\n" } ### Tool output · 2026-09-23T05:37:26.141Z Script completed Wall time 0.4 seconds Output: {"chunk_id":"43f73c","wall_time_seconds":0.07430893,"exit_code":0,"original_token_count":80,"output":"Updated submission to refitted one-week collaborative ranker\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3658daab487d2aa4aba051a1cad25", "usage": { "input_tokens": 53919, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 223, "reasoning_output_tokens": 54, "total_tokens": 54142 }, "turn_token_usage": { "input_tokens": 1492547, "cached_input_tokens": 1446016, "cache_write_input_tokens": 0, "output_tokens": 24718, "reasoning_output_tokens": 8167, "total_tokens": 1517265 }, "thread_token_usage": { "input_tokens": 1492547, "cached_input_tokens": 1446016, "cache_write_input_tokens": 0, "output_tokens": 24718, "reasoning_output_tokens": 8167, "total_tokens": 1517265 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0144750f-2bf2-466d-8468-a71dea5013ff", "process_id": "61484", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,shutil\np=Path('/app/solution');src=p/'test_cattime_100_short.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Updated submission to refitted one-week collaborative ranker')\nPY\ntail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -4 /app/solution/extra.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,shutil\np=Path('/app/solution');src=p/'test_cattime_100_short.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Updated submission to refitted one-week collaborative ranker')\nPY\ntail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -4 /app/solution/extra.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Updated submission to refitted one-week collaborative ranker\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\n", "stderr": "", "aggregated_output": "Updated submission to refitted one-week collaborative ranker\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 74292619 }, "formatted_output": "Updated submission to refitted one-week collaborative ranker\ncattime_100_d1 32\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\n" } } ## Step 46 · agent · 2026-09-23T05:37:46.457Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:37:51.450Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution')\\nfor pattern in ['*_ease_*.npy','*_svd_*.npy','*_als_*.npy','*_knn_*.npy','*_user_100.npy','*_user_400.npy','validation_full_*.npy']:\\n for f in p.glob(pattern):f.unlink()\\nprint('Removed discarded model score caches')\\nPY\\ntail -5 /app/solution/past_predict.log; tail -5 /app/solution/time_user2.log; tail -5 /app/solution/test_predict.log; tail -5 /app/solution/extra.log\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:37:51.852Z Script completed Wall time 0.4 seconds Output: {"chunk_id":"cce34f","wall_time_seconds":0.220832385,"exit_code":0,"original_token_count":118,"output":"Removed discarded model score caches\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\ncattime_100_d14 84\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\n"} {"current_time":"2026-09-23 05:37:51 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365967de487d2be9b6752390767e1", "usage": { "input_tokens": 54341, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 699, "reasoning_output_tokens": 516, "total_tokens": 55040 }, "turn_token_usage": { "input_tokens": 1546888, "cached_input_tokens": 1499776, "cache_write_input_tokens": 0, "output_tokens": 25417, "reasoning_output_tokens": 8683, "total_tokens": 1572305 }, "thread_token_usage": { "input_tokens": 1546888, "cached_input_tokens": 1499776, "cache_write_input_tokens": 0, "output_tokens": 25417, "reasoning_output_tokens": 8683, "total_tokens": 1572305 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60ab29c8-76f8-4b25-b65c-f8b8ebd29a40", "process_id": "64689", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution')\nfor pattern in ['*_ease_*.npy','*_svd_*.npy','*_als_*.npy','*_knn_*.npy','*_user_100.npy','*_user_400.npy','validation_full_*.npy']:\n for f in p.glob(pattern):f.unlink()\nprint('Removed discarded model score caches')\nPY\ntail -5 /app/solution/past_predict.log; tail -5 /app/solution/time_user2.log; tail -5 /app/solution/test_predict.log; tail -5 /app/solution/extra.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution')\nfor pattern in ['*_ease_*.npy','*_svd_*.npy','*_als_*.npy','*_knn_*.npy','*_user_100.npy','*_user_400.npy','validation_full_*.npy']:\n for f in p.glob(pattern):f.unlink()\nprint('Removed discarded model score caches')\nPY\ntail -5 /app/solution/past_predict.log; tail -5 /app/solution/time_user2.log; tail -5 /app/solution/test_predict.log; tail -5 /app/solution/extra.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Removed discarded model score caches\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\ncattime_100_d14 84\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\n", "stderr": "", "aggregated_output": "Removed discarded model score caches\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\ncattime_100_d14 84\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 220818024 }, "formatted_output": "Removed discarded model score caches\ncattime_100_d3 54\ncattime_100_d14 69\ncattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_100_short 0.0416147616958469 20\ncattime_100_d1 0.038177152831413486 39\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_short 21\ncattime_100_d1 40\ncattime_100_d3 62\ncattime_100_d14 84\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\n" } } ## Step 47 · agent · 2026-09-23T05:38:05.620Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bb743f0b-3842-407a-9330-fae2b9ce6b58", "process_id": "40333", "command": [ "/bin/sh", "-lc", "cat > /app/solution/make_neighbors.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys\nstage=sys.argv[1];tu,tr,mask=context(stage);x=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\ncs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\nnp.savez(O/f'{stage}_cat_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,'neighbors done',flush=True)\nPY\ncat > /app/solution/meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nos.environ['OMP_NUM_THREADS']='4'\nfrom common import *\nimport sys,pickle,time,json\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nnames=['cattime_100_'+k for k in ['short','d1','d3','d14','org7','like7']]+['cattime_300_short']\nclass Data:\n def __init__(self,stage):\n self.stage=stage;self.tu,self.tr,self.mask=context(stage);self.scores=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in names];self.pops={};self.uc=[]\n for k in ['count','organic','likes','recent','recentorg','short','d1','d3','d14','org7','like7']:\n x=matrix(k,stage);self.pops[k]=np.asarray(x.sum(axis=0)).ravel();self.uc.append(np.log1p(np.asarray(x.sum(axis=1)).ravel()[self.tr]))\n x=matrix('count',stage);self.pops['degree']=np.asarray((x>0).sum(axis=0)).ravel();self.uc=np.array(self.uc).T\n pool=[]\n for s in self.scores:pool.append(np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(self.tu),256)]))\n for k in ['short','d1','d3','like7']:\n p=self.pops[k];pool.append(np.concatenate([top(np.where(self.mask[b:b+256],p,-np.inf),150) for b in range(0,len(self.tu),256)]))\n self.pool=np.sort(np.concatenate(pool,axis=1),axis=1);self.valid=np.ones(self.pool.shape,bool);self.valid[:,1:]=self.pool[:,1:]!=self.pool[:,:-1]\n self.maxs=np.stack([np.maximum(np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8) for s in self.scores],axis=1)\n def features(self,r,c):\n f=[]\n for j,s in enumerate(self.scores):\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n for p in self.pops.values():f.append(np.log1p(p[c]))\n for a,b in [('d1','d3'),('d3','short'),('short','d14'),('short','recent'),('recent','count'),('organic','count'),('org7','short'),('like7','short'),('likes','organic')]:f.append(np.log((self.pops[a][c]+.01)/(self.pops[b][c]+.1)))\n for j in range(self.uc.shape[1]):f.append(self.uc[r,j])\n return np.stack(f,axis=1).astype(np.float32)\n def sample(self):\n y=sp.load_npz(O/'past_truth.npz') if self.stage=='past' else truth()[0];discount=1/np.log2(np.arange(2,12));idcg=np.zeros(len(self.tu))\n for r in range(len(self.tu)):\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\n rng=np.random.RandomState(42);rr=[];cc=[];ww=[];yy=[]\n for r in np.flatnonzero(idcg):\n c=self.pool[r,self.valid[r]];g=y[r,c].toarray().ravel();pos=g>0;neg=np.flatnonzero(~pos);sel=np.concatenate([np.flatnonzero(pos),rng.choice(neg,min(120,len(neg)),replace=False)]);rr.extend([r]*len(sel));cc.extend(c[sel]);yy.extend(g[sel]>0);w=np.where(g[sel]>0,g[sel],len(neg)/min(120,len(neg)));ww.extend(w/np.sqrt(idcg[r]))\n r=np.array(rr);c=np.array(cc);print('samples',self.stage,len(r),sum(yy),flush=True);return self.features(r,c),np.array(yy),np.array(ww,dtype=np.float32)\n def predict(self,model):\n out=np.empty(self.pool.shape,np.float32)\n for b in range(0,len(self.tu),64):\n p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\n out[~self.valid]=-np.inf;return out\nif __name__=='__main__':\n mode=sys.argv[1];t=time.time()\n if mode=='train':\n d=Data('past');x,y,w=d.sample();np.savez(O/'meta_past_samples.npz',x=x,y=y,w=w);del d\n val=Data('validation');best=0\n for leaves in [15,31]:\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(x,y,sample_weight=w);s=val.predict(model);ix=np.take_along_axis(val.pool,top(s),axis=1);v=evaluate(ix);print('meta',leaves,v,round(time.time()-t),flush=True);save(ix,'validation',f'validation_meta_{leaves}.parquet');np.save(O/f'validation_meta_{leaves}_pool_scores.npy',s)\n if v>best:best=v;pickle.dump(model,open(O/'meta_model.pkl','wb'));json.dump({'leaves':leaves,'score':v},open(O/'meta_config.json','w'));save(ix,'validation','validation_meta.parquet')\n np.savez_compressed(O/'meta_validation_pool.npz',pool=val.pool,valid=val.valid);vx,vy,vw=val.sample();np.savez(O/'meta_val_samples.npz',x=vx,y=vy,w=vw)\n elif mode=='final':\n a=np.load(O/'meta_past_samples.npz');b=np.load(O/'meta_val_samples.npz');config=json.load(open(O/'meta_config.json'));model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=config['leaves'],min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'meta_final_model.pkl','wb'));d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_meta.parquet');print('final meta done',round(time.time()-t),flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py past > /app/solution/past_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past cat > /app/solution/past_predict.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/make_neighbors.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys\nstage=sys.argv[1];tu,tr,mask=context(stage);x=profile(stage);df=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\ncs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,400);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\nnp.savez(O/f'{stage}_cat_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,'neighbors done',flush=True)\nPY\ncat > /app/solution/meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nos.environ['OMP_NUM_THREADS']='4'\nfrom common import *\nimport sys,pickle,time,json\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nnames=['cattime_100_'+k for k in ['short','d1','d3','d14','org7','like7']]+['cattime_300_short']\nclass Data:\n def __init__(self,stage):\n self.stage=stage;self.tu,self.tr,self.mask=context(stage);self.scores=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in names];self.pops={};self.uc=[]\n for k in ['count','organic','likes','recent','recentorg','short','d1','d3','d14','org7','like7']:\n x=matrix(k,stage);self.pops[k]=np.asarray(x.sum(axis=0)).ravel();self.uc.append(np.log1p(np.asarray(x.sum(axis=1)).ravel()[self.tr]))\n x=matrix('count',stage);self.pops['degree']=np.asarray((x>0).sum(axis=0)).ravel();self.uc=np.array(self.uc).T\n pool=[]\n for s in self.scores:pool.append(np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(self.tu),256)]))\n for k in ['short','d1','d3','like7']:\n p=self.pops[k];pool.append(np.concatenate([top(np.where(self.mask[b:b+256],p,-np.inf),150) for b in range(0,len(self.tu),256)]))\n self.pool=np.sort(np.concatenate(pool,axis=1),axis=1);self.valid=np.ones(self.pool.shape,bool);self.valid[:,1:]=self.pool[:,1:]!=self.pool[:,:-1]\n self.maxs=np.stack([np.maximum(np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8) for s in self.scores],axis=1)\n def features(self,r,c):\n f=[]\n for j,s in enumerate(self.scores):\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n for p in self.pops.values():f.append(np.log1p(p[c]))\n for a,b in [('d1','d3'),('d3','short'),('short','d14'),('short','recent'),('recent','count'),('organic','count'),('org7','short'),('like7','short'),('likes','organic')]:f.append(np.log((self.pops[a][c]+.01)/(self.pops[b][c]+.1)))\n for j in range(self.uc.shape[1]):f.append(self.uc[r,j])\n return np.stack(f,axis=1).astype(np.float32)\n def sample(self):\n y=sp.load_npz(O/'past_truth.npz') if self.stage=='past' else truth()[0];discount=1/np.log2(np.arange(2,12));idcg=np.zeros(len(self.tu))\n for r in range(len(self.tu)):\n g=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[::-1][:10];idcg[r]=(g*discount[:len(g)]).sum()\n rng=np.random.RandomState(42);rr=[];cc=[];ww=[];yy=[]\n for r in np.flatnonzero(idcg):\n c=self.pool[r,self.valid[r]];g=y[r,c].toarray().ravel();pos=g>0;neg=np.flatnonzero(~pos);sel=np.concatenate([np.flatnonzero(pos),rng.choice(neg,min(120,len(neg)),replace=False)]);rr.extend([r]*len(sel));cc.extend(c[sel]);yy.extend(g[sel]>0);w=np.where(g[sel]>0,g[sel],len(neg)/min(120,len(neg)));ww.extend(w/np.sqrt(idcg[r]))\n r=np.array(rr);c=np.array(cc);print('samples',self.stage,len(r),sum(yy),flush=True);return self.features(r,c),np.array(yy),np.array(ww,dtype=np.float32)\n def predict(self,model):\n out=np.empty(self.pool.shape,np.float32)\n for b in range(0,len(self.tu),64):\n p=self.pool[b:b+64];r=np.repeat(np.arange(b,b+len(p)),p.shape[1]);f=self.features(r,p.ravel());out[b:b+64]=model.predict_proba(f)[:,1].reshape(p.shape)\n out[~self.valid]=-np.inf;return out\nif __name__=='__main__':\n mode=sys.argv[1];t=time.time()\n if mode=='train':\n d=Data('past');x,y,w=d.sample();np.savez(O/'meta_past_samples.npz',x=x,y=y,w=w);del d\n val=Data('validation');best=0\n for leaves in [15,31]:\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(x,y,sample_weight=w);s=val.predict(model);ix=np.take_along_axis(val.pool,top(s),axis=1);v=evaluate(ix);print('meta',leaves,v,round(time.time()-t),flush=True);save(ix,'validation',f'validation_meta_{leaves}.parquet');np.save(O/f'validation_meta_{leaves}_pool_scores.npy',s)\n if v>best:best=v;pickle.dump(model,open(O/'meta_model.pkl','wb'));json.dump({'leaves':leaves,'score':v},open(O/'meta_config.json','w'));save(ix,'validation','validation_meta.parquet')\n np.savez_compressed(O/'meta_validation_pool.npz',pool=val.pool,valid=val.valid);vx,vy,vw=val.sample();np.savez(O/'meta_val_samples.npz',x=vx,y=vy,w=vw)\n elif mode=='final':\n a=np.load(O/'meta_past_samples.npz');b=np.load(O/'meta_val_samples.npz');config=json.load(open(O/'meta_config.json'));model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=config['leaves'],min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'meta_final_model.pkl','wb'));d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_meta.parquet');print('final meta done',round(time.time()-t),flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py past > /app/solution/past_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past cat > /app/solution/past_predict.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 152, "nanos": 332496822 }, "formatted_output": "" } } ### exec · 2026-09-23T05:38:12.623Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\\\"if mode=='organic':\\\", \\\"if mode=='fresh':\\\\n r=matrix('recent',stage);r.data=np.log1p(r.data);h=matrix('short',stage);h.data=np.log1p(h.data);x=x*.1+r*.25+h\\\\nif mode=='organic':\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation fresh > /app/solution/fresh.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 d3 >> /app/solution/fresh.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 500 d3 >> /app/solution/fresh.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 short >> /app/solution/fresh.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:38:13.796Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"83ce6e","wall_time_seconds":1.000643808,"session_id":64857,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365b0102887d2b97491feafd5aef8", "usage": { "input_tokens": 55323, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 638, "reasoning_output_tokens": 344, "total_tokens": 55961 }, "turn_token_usage": { "input_tokens": 1602211, "cached_input_tokens": 1553920, "cache_write_input_tokens": 0, "output_tokens": 26055, "reasoning_output_tokens": 9027, "total_tokens": 1628266 }, "thread_token_usage": { "input_tokens": 1602211, "cached_input_tokens": 1553920, "cache_write_input_tokens": 0, "output_tokens": 26055, "reasoning_output_tokens": 9027, "total_tokens": 1628266 } } ## Step 48 · agent · 2026-09-23T05:38:19.608Z ### Thinking unavailable — no text recorded Weighting users’ recent listening when choosing similar listeners has raised validation NDCG@10 to 0.048015. A refitted collaborative submission is already saved; I’m testing this stronger variant and the reranker before selecting the final artifact. ### exec · 2026-09-23T05:38:23.933Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -5 /app/solution/extra.log; tail -3 /app/solution/meta.log; tail -3 /app/solution/fresh.log\",\"max_output_tokens\":1200}));\n" } ### Tool output · 2026-09-23T05:38:24.039Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"4f81b2","wall_time_seconds":0.00000746,"exit_code":0,"original_token_count":138,"output":"cattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_300_short 139\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_100_d3 62\ncattime_100_d14 84\ncattime_100_org7 101\ncattime_100_like7 118\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\ntail: cannot open '/app/solution/meta.log' for reading: No such file or directory\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f8778ef-0c1d-4e54-ac03-5406d5a25e31", "process_id": "51061", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -5 /app/solution/extra.log; tail -3 /app/solution/meta.log; tail -3 /app/solution/fresh.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/past_predict.log; tail -4 /app/solution/time_user2.log; tail -4 /app/solution/test_predict.log; tail -5 /app/solution/extra.log; tail -3 /app/solution/meta.log; tail -3 /app/solution/fresh.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_300_short 139\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_100_d3 62\ncattime_100_d14 84\ncattime_100_org7 101\ncattime_100_like7 118\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\ntail: cannot open '/app/solution/meta.log' for reading: No such file or directory\n", "stderr": "", "aggregated_output": "cattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_300_short 139\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_100_d3 62\ncattime_100_d14 84\ncattime_100_org7 101\ncattime_100_like7 118\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\ntail: cannot open '/app/solution/meta.log' for reading: No such file or directory\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7460 }, "formatted_output": "cattime_100_org7 83\ncattime_100_like7 98\ncattime_30_short 116\ncattime_300_short 139\ncattime_100_d3 0.04408932514149987 62\ncattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_100_d3 62\ncattime_100_d14 84\ncattime_100_org7 101\ncattime_100_like7 118\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\ntail: cannot open '/app/solution/meta.log' for reading: No such file or directory\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365c5fe8087d2955ee034db065e06", "usage": { "input_tokens": 56019, "cached_input_tokens": 55168, "cache_write_input_tokens": 0, "output_tokens": 181, "reasoning_output_tokens": 30, "total_tokens": 56200 }, "turn_token_usage": { "input_tokens": 1658230, "cached_input_tokens": 1609088, "cache_write_input_tokens": 0, "output_tokens": 26236, "reasoning_output_tokens": 9057, "total_tokens": 1684466 }, "thread_token_usage": { "input_tokens": 1658230, "cached_input_tokens": 1609088, "cache_write_input_tokens": 0, "output_tokens": 26236, "reasoning_output_tokens": 9057, "total_tokens": 1684466 } } ## Step 49 · agent · 2026-09-23T05:38:45.391Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:38:58.423Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test recent > /app/solution/test_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 d3 >> /app/solution/test_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 short >> /app/solution/test_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test fresh >> /app/solution/test_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test fresh 500 d3 >> /app/solution/test_extra.log 2>&1\",\"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/neighbors_extra.py past recent > /app/solution/past_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 d3 >> /app/solution/past_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 short >> /app/solution/past_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py past fresh >> /app/solution/past_extra.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past fresh 500 d3 >> /app/solution/past_extra.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:39:00.816Z Script completed Wall time 2.4 seconds Output: {"chunk_id":"e3d57d","wall_time_seconds":1.000899177,"session_id":61016,"original_token_count":0,"output":""} {"chunk_id":"780329","wall_time_seconds":1.000588663,"session_id":59339,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365d0629487d284699f2a80e2a721", "usage": { "input_tokens": 56489, "cached_input_tokens": 55808, "cache_write_input_tokens": 0, "output_tokens": 973, "reasoning_output_tokens": 516, "total_tokens": 57462 }, "turn_token_usage": { "input_tokens": 1714719, "cached_input_tokens": 1664896, "cache_write_input_tokens": 0, "output_tokens": 27209, "reasoning_output_tokens": 9573, "total_tokens": 1741928 }, "thread_token_usage": { "input_tokens": 1714719, "cached_input_tokens": 1664896, "cache_write_input_tokens": 0, "output_tokens": 27209, "reasoning_output_tokens": 9573, "total_tokens": 1741928 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0a294a36-3342-4677-8a1b-35d79c03bffb", "process_id": "4900", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py test > /app/solution/test_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test cat > /app/solution/test_predict.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py test > /app/solution/test_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test cat > /app/solution/test_predict.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 182, "nanos": 904681800 }, "formatted_output": "" } } ## Step 50 · agent · 2026-09-23T05:39:20.267Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:39:45.065Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/fusion.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport json,sys,time\\nstage=sys.argv[1];t=time.time();tu,tr,mask=context(stage)\\nif stage=='validation':names=sys.argv[2:] or ['recenttime_500_d3','freshtime_500_d3','cattime_100_d3','cattime_300_short']\\nelse:names=json.load(open(O/'fusion.json'))['names']\\nixs=[];sources={}\\nfor name in names:\\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');sources[name]=s;ix=np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(tu),256)]);ixs.append(ix)\\n if stage=='validation':print(name,evaluate(ix),flush=True)\\nfor k in ['short','d1','d3','like7']:\\n p=np.asarray(matrix(k,stage).sum(axis=0)).ravel();sources['pop_'+k]=p;ixs.append(np.concatenate([top(np.where(mask[b:b+256],p,-np.inf),150) for b in range(0,len(tu),256)]))\\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\\nfor name,s in sources.items():\\n v=s[pool] if s.ndim==1 else s[np.arange(len(tu))[:,None],pool];v=np.maximum(np.nan_to_num(v.astype(np.float32),neginf=0),0);v/=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v\\n if name.startswith('pop'):features[name+'_sq']=v*v\\nkeys=list(features)\\ndef score(s):\\n s=np.where(valid,s,-np.inf);return evaluate(np.take_along_axis(pool,top(s,10),axis=1))\\nif stage=='validation':\\n best=(0,None)\\n for k,v in features.items():\\n q=score(v)\\n if q>best[0]:best=(q,k)\\n weights={k:0 for k in keys};weights[best[1]]=1.;s=features[best[1]].copy();quality=best[0];print('start',quality,best[1],flush=True)\\n for round in range(3):\\n improved=False\\n for k in keys:\\n old=weights[k];base=s-old*features[k];win=old\\n for w in [0,.1,.25,.5,1,2,4]:\\n if w==old:continue\\n q=score(base+w*features[k])\\n if q>quality:quality=q;win=w\\n if win!=old:weights[k]=win;s=base+win*features[k];improved=True;print('improve',quality,k,win,flush=True)\\n if not improved:break\\n cfg={'names':names,'weights':{k:v for k,v in weights.items() if v},'score':quality};json.dump(cfg,open(O/'fusion.json','w'),indent=2)\\nelse:\\n cfg=json.load(open(O/'fusion.json'));s=sum(features[k]*w for k,w in cfg['weights'].items())\\ns[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_fusion.parquet' if stage=='validation' else 'submission_fusion.parquet');print('done',cfg,time.time()-t,flush=True)\\nPY\\ntail -5 /app/solution/time_user2.log; tail -5 /app/solution/meta.log; tail -6 /app/solution/extra.log; tail -5 /app/solution/fresh.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:39:45.401Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"f96c40","wall_time_seconds":0.00000549,"exit_code":0,"original_token_count":131,"output":"cattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_30_short 0.03534805798079699 135\ncattime_300_short 0.04333312959407578 165\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nvalidation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\n"} {"current_time":"2026-09-23 05:39:45 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-986e6679-5324-4abf-832e-e3dd6a8e862a", "process_id": "87809", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fusion.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport json,sys,time\nstage=sys.argv[1];t=time.time();tu,tr,mask=context(stage)\nif stage=='validation':names=sys.argv[2:] or ['recenttime_500_d3','freshtime_500_d3','cattime_100_d3','cattime_300_short']\nelse:names=json.load(open(O/'fusion.json'))['names']\nixs=[];sources={}\nfor name in names:\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');sources[name]=s;ix=np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(tu),256)]);ixs.append(ix)\n if stage=='validation':print(name,evaluate(ix),flush=True)\nfor k in ['short','d1','d3','like7']:\n p=np.asarray(matrix(k,stage).sum(axis=0)).ravel();sources['pop_'+k]=p;ixs.append(np.concatenate([top(np.where(mask[b:b+256],p,-np.inf),150) for b in range(0,len(tu),256)]))\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\nfor name,s in sources.items():\n v=s[pool] if s.ndim==1 else s[np.arange(len(tu))[:,None],pool];v=np.maximum(np.nan_to_num(v.astype(np.float32),neginf=0),0);v/=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v\n if name.startswith('pop'):features[name+'_sq']=v*v\nkeys=list(features)\ndef score(s):\n s=np.where(valid,s,-np.inf);return evaluate(np.take_along_axis(pool,top(s,10),axis=1))\nif stage=='validation':\n best=(0,None)\n for k,v in features.items():\n q=score(v)\n if q>best[0]:best=(q,k)\n weights={k:0 for k in keys};weights[best[1]]=1.;s=features[best[1]].copy();quality=best[0];print('start',quality,best[1],flush=True)\n for round in range(3):\n improved=False\n for k in keys:\n old=weights[k];base=s-old*features[k];win=old\n for w in [0,.1,.25,.5,1,2,4]:\n if w==old:continue\n q=score(base+w*features[k])\n if q>quality:quality=q;win=w\n if win!=old:weights[k]=win;s=base+win*features[k];improved=True;print('improve',quality,k,win,flush=True)\n if not improved:break\n cfg={'names':names,'weights':{k:v for k,v in weights.items() if v},'score':quality};json.dump(cfg,open(O/'fusion.json','w'),indent=2)\nelse:\n cfg=json.load(open(O/'fusion.json'));s=sum(features[k]*w for k,w in cfg['weights'].items())\ns[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_fusion.parquet' if stage=='validation' else 'submission_fusion.parquet');print('done',cfg,time.time()-t,flush=True)\nPY\ntail -5 /app/solution/time_user2.log; tail -5 /app/solution/meta.log; tail -6 /app/solution/extra.log; tail -5 /app/solution/fresh.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fusion.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport json,sys,time\nstage=sys.argv[1];t=time.time();tu,tr,mask=context(stage)\nif stage=='validation':names=sys.argv[2:] or ['recenttime_500_d3','freshtime_500_d3','cattime_100_d3','cattime_300_short']\nelse:names=json.load(open(O/'fusion.json'))['names']\nixs=[];sources={}\nfor name in names:\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');sources[name]=s;ix=np.concatenate([top(np.array(s[b:b+256],np.float32),150) for b in range(0,len(tu),256)]);ixs.append(ix)\n if stage=='validation':print(name,evaluate(ix),flush=True)\nfor k in ['short','d1','d3','like7']:\n p=np.asarray(matrix(k,stage).sum(axis=0)).ravel();sources['pop_'+k]=p;ixs.append(np.concatenate([top(np.where(mask[b:b+256],p,-np.inf),150) for b in range(0,len(tu),256)]))\npool=np.sort(np.concatenate(ixs,axis=1),axis=1);valid=np.ones(pool.shape,bool);valid[:,1:]=pool[:,1:]!=pool[:,:-1];features={}\nfor name,s in sources.items():\n v=s[pool] if s.ndim==1 else s[np.arange(len(tu))[:,None],pool];v=np.maximum(np.nan_to_num(v.astype(np.float32),neginf=0),0);v/=np.maximum(np.max(v,axis=1,keepdims=True),1e-8);features[name]=v\n if name.startswith('pop'):features[name+'_sq']=v*v\nkeys=list(features)\ndef score(s):\n s=np.where(valid,s,-np.inf);return evaluate(np.take_along_axis(pool,top(s,10),axis=1))\nif stage=='validation':\n best=(0,None)\n for k,v in features.items():\n q=score(v)\n if q>best[0]:best=(q,k)\n weights={k:0 for k in keys};weights[best[1]]=1.;s=features[best[1]].copy();quality=best[0];print('start',quality,best[1],flush=True)\n for round in range(3):\n improved=False\n for k in keys:\n old=weights[k];base=s-old*features[k];win=old\n for w in [0,.1,.25,.5,1,2,4]:\n if w==old:continue\n q=score(base+w*features[k])\n if q>quality:quality=q;win=w\n if win!=old:weights[k]=win;s=base+win*features[k];improved=True;print('improve',quality,k,win,flush=True)\n if not improved:break\n cfg={'names':names,'weights':{k:v for k,v in weights.items() if v},'score':quality};json.dump(cfg,open(O/'fusion.json','w'),indent=2)\nelse:\n cfg=json.load(open(O/'fusion.json'));s=sum(features[k]*w for k,w in cfg['weights'].items())\ns[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_fusion.parquet' if stage=='validation' else 'submission_fusion.parquet');print('done',cfg,time.time()-t,flush=True)\nPY\ntail -5 /app/solution/time_user2.log; tail -5 /app/solution/meta.log; tail -6 /app/solution/extra.log; tail -5 /app/solution/fresh.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_30_short 0.03534805798079699 135\ncattime_300_short 0.04333312959407578 165\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nvalidation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\n", "stderr": "", "aggregated_output": "cattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_30_short 0.03534805798079699 135\ncattime_300_short 0.04333312959407578 165\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nvalidation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5490 }, "formatted_output": "cattime_100_d14 0.037098322825909934 85\ncattime_100_org7 0.04100252024730856 101\ncattime_100_like7 0.02258610347105085 118\ncattime_30_short 0.03534805798079699 135\ncattime_300_short 0.04333312959407578 165\nvalidation recent neighbors done 13.673468351364136\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nvalidation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab365f5028087d2ac2d1e7408030031", "usage": { "input_tokens": 57554, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 1423, "reasoning_output_tokens": 516, "total_tokens": 58977 }, "turn_token_usage": { "input_tokens": 1772273, "cached_input_tokens": 1721216, "cache_write_input_tokens": 0, "output_tokens": 28632, "reasoning_output_tokens": 10089, "total_tokens": 1800905 }, "thread_token_usage": { "input_tokens": 1772273, "cached_input_tokens": 1721216, "cache_write_input_tokens": 0, "output_tokens": 28632, "reasoning_output_tokens": 10089, "total_tokens": 1800905 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3f21c004-b5c4-46a1-8a61-294b8a8f5d82", "process_id": "98480", "command": [ "/bin/sh", "-lc", "cat > /app/solution/neighbors_extra.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys,time\nstage=sys.argv[1];mode=sys.argv[2];t=time.time();tu,tr,mask=context(stage);x=profile(stage)\nif mode=='binary':x=profile(stage,'binary')\nif mode=='recent':\n r=matrix('recent',stage);r.data=np.log1p(r.data);x=x*.25+r\nif mode=='organic':x=profile(stage,'organic')\ndf=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\ncs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,1000);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\nnp.savez(O/f'{stage}_{mode}_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,mode,'neighbors done',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation recent > /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 100 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation binary >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 100 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 500 d3 >> /app/solution/extra.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/neighbors_extra.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys,time\nstage=sys.argv[1];mode=sys.argv[2];t=time.time();tu,tr,mask=context(stage);x=profile(stage)\nif mode=='binary':x=profile(stage,'binary')\nif mode=='recent':\n r=matrix('recent',stage);r.data=np.log1p(r.data);x=x*.25+r\nif mode=='organic':x=profile(stage,'organic')\ndf=np.asarray((x>0).sum(axis=0)).ravel();idf=np.log(1+len(uids)/(df+1));z=x@sp.diags(idf**.7);z=sp.diags(1/np.sqrt(np.asarray(z.power(2).sum(axis=1)).ravel()+1e-8))@z\ncs=[];vs=[]\nfor b in range(0,len(tu),256):\n s=(z[tr[b:b+256]]@z.T).toarray();s[np.arange(len(s)),tr[b:b+256]]=0;ix=top(s,1000);cs.append(ix);vs.append(np.take_along_axis(s,ix,axis=1))\nnp.savez(O/f'{stage}_{mode}_usersim.npz',ix=np.concatenate(cs),v=np.concatenate(vs));print(stage,mode,'neighbors done',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation recent > /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 100 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation binary >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 100 d3 >> /app/solution/extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation binary 500 d3 >> /app/solution/extra.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 160, "nanos": 824367445 }, "formatted_output": "" } } ## Step 51 · agent · 2026-09-23T05:40:03.830Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e1f33c4-1aad-47b5-b57d-d6ba00248155", "process_id": "64857", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\"if mode=='organic':\", \"if mode=='fresh':\\n r=matrix('recent',stage);r.data=np.log1p(r.data);h=matrix('short',stage);h.data=np.log1p(h.data);x=x*.1+r*.25+h\\nif mode=='organic':\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation fresh > /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 d3 >> /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 500 d3 >> /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 short >> /app/solution/fresh.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\"if mode=='organic':\", \"if mode=='fresh':\\n r=matrix('recent',stage);r.data=np.log1p(r.data);h=matrix('short',stage);h.data=np.log1p(h.data);x=x*.1+r*.25+h\\nif mode=='organic':\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation fresh > /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 d3 >> /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 500 d3 >> /app/solution/fresh.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation fresh 100 short >> /app/solution/fresh.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 118, "nanos": 106265951 }, "formatted_output": "" } } ## Step 52 · agent · 2026-09-23T05:40:16.801Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:40:29.255Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\\\"if mode=='organic':\\\", \\\"if mode=='state':\\\\n r=matrix('recent',stage);r.data=np.log1p(r.data);l=matrix('likes',stage);l.data[:]=1;x=x*.25+r-l*.75\\\\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');rr=U.get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0)&(d.state.values=='liked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x+a*.75\\\\n m=(rr>=0)&(cc>=0)&(d.state.values=='disliked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x-x.multiply(a);x.eliminate_zeros()\\\\nif mode=='organic':\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 short > /app/solution/state.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation state >> /app/solution/state.log 2>&1\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation state 500 d3 >> /app/solution/state.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/meta.log; tail -5 /app/solution/fresh.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/extra.log\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:40:30.598Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"5af015","wall_time_seconds":1.000685437,"session_id":33934,"original_token_count":0,"output":""} {"chunk_id":"6a997f","wall_time_seconds":0.000010441,"exit_code":0,"original_token_count":131,"output":"validation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\nfreshtime_500_d3 0.0521731404587799 50\nfreshtime_100_short 0.04694179199207414 22\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nbinarytime_500_d3 0.04687133118256751 45\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab366219a8487d2ba9cc7f7f8d47955", "usage": { "input_tokens": 59257, "cached_input_tokens": 57344, "cache_write_input_tokens": 0, "output_tokens": 1385, "reasoning_output_tokens": 916, "total_tokens": 60642 }, "turn_token_usage": { "input_tokens": 1831530, "cached_input_tokens": 1778560, "cache_write_input_tokens": 0, "output_tokens": 30017, "reasoning_output_tokens": 11005, "total_tokens": 1861547 }, "thread_token_usage": { "input_tokens": 1831530, "cached_input_tokens": 1778560, "cache_write_input_tokens": 0, "output_tokens": 30017, "reasoning_output_tokens": 11005, "total_tokens": 1861547 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab4ae6e5-527f-46b8-9a34-c238a6e5bd46", "process_id": "35228", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/meta.log; tail -5 /app/solution/fresh.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/extra.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/meta.log; tail -5 /app/solution/fresh.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/extra.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\nfreshtime_500_d3 0.0521731404587799 50\nfreshtime_100_short 0.04694179199207414 22\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nbinarytime_500_d3 0.04687133118256751 45\n", "stderr": "", "aggregated_output": "validation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\nfreshtime_500_d3 0.0521731404587799 50\nfreshtime_100_short 0.04694179199207414 22\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nbinarytime_500_d3 0.04687133118256751 45\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 10441 }, "formatted_output": "validation fresh neighbors done 18.76214051246643\nfreshtime_100_d3 0.048537002728318644 22\nfreshtime_500_d3 0.0521731404587799 50\nfreshtime_100_short 0.04694179199207414 22\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_100_d3 0.04801537323698959 19\nrecenttime_500_d3 0.05104514932025567 31\nvalidation binary neighbors done 18.94961380958557\nbinarytime_100_d3 0.04048661386799271 24\nbinarytime_500_d3 0.04687133118256751 45\n" } } ## Step 53 · agent · 2026-09-23T05:40:44.609Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:40:53.087Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py validation > /app/solution/fusion.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\\\"+['cattime_300_short']\\\", \\\"+['cattime_300_short','recenttime_500_d3','recenttime_500_short','freshtime_500_d3']\\\").replace(\\\"meta_\\\", \\\"metax_\\\");open('/app/solution/meta_extended.py','w').write(s)\\nPY\\npython - <<'PY'\\nimport time\\nfrom pathlib import Path\\nwhile 'freshtime_500_d3' not in Path('/app/solution/past_extra.log').read_text() or 'recenttime_500_short' not in Path('/app/solution/state.log').read_text():time.sleep(3)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta_extended.py train > /app/solution/metax.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:40:55.449Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"fdd8e8","wall_time_seconds":1.000955739,"session_id":60957,"original_token_count":0,"output":""} {"chunk_id":"2f65b8","wall_time_seconds":1.001288247,"session_id":17668,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3664ee92887d29b2cae353d457c5c", "usage": { "input_tokens": 60936, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 693, "reasoning_output_tokens": 400, "total_tokens": 61629 }, "turn_token_usage": { "input_tokens": 1892466, "cached_input_tokens": 1837696, "cache_write_input_tokens": 0, "output_tokens": 30710, "reasoning_output_tokens": 11405, "total_tokens": 1923176 }, "thread_token_usage": { "input_tokens": 1892466, "cached_input_tokens": 1837696, "cache_write_input_tokens": 0, "output_tokens": 30710, "reasoning_output_tokens": 11405, "total_tokens": 1923176 } } ## Step 54 · agent · 2026-09-23T05:40:59.647Z ### Thinking unavailable — no text recorded The freshest-history model has reached 0.052173 NDCG@10. I’m comparing a simple ensemble with rerankers trained on the earlier holdout, and checking whether using active preference state improves neighbor selection. ### exec · 2026-09-23T05:41:04.356Z { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/state.log; ls -lh /app/solution/meta*; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:41:04.455Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"68cd87","wall_time_seconds":0.00000723,"exit_code":0,"original_token_count":190,"output":"samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\n-rw-r--r-- 1 agent agent 213 Sep 23 05:40 /app/solution/meta.log\n-rw-r--r-- 1 agent agent 4.8K Sep 23 05:36 /app/solution/meta.py\n-rw-r--r-- 1 agent agent 4.9K Sep 23 05:40 /app/solution/meta_extended.py\n-rw-r--r-- 1 agent agent 101M Sep 23 05:40 /app/solution/meta_past_samples.npz\nlow 0\nhigh 0\nmax 10653\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"} {"current_time":"2026-09-23 05:41:04 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ffcd5d67-0426-4027-bf52-31e8390634b6", "process_id": "56439", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/state.log; ls -lh /app/solution/meta*; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/state.log; ls -lh /app/solution/meta*; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\n-rw-r--r-- 1 agent agent 213 Sep 23 05:40 /app/solution/meta.log\n-rw-r--r-- 1 agent agent 4.8K Sep 23 05:36 /app/solution/meta.py\n-rw-r--r-- 1 agent agent 4.9K Sep 23 05:40 /app/solution/meta_extended.py\n-rw-r--r-- 1 agent agent 101M Sep 23 05:40 /app/solution/meta_past_samples.npz\nlow 0\nhigh 0\nmax 10653\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\n-rw-r--r-- 1 agent agent 213 Sep 23 05:40 /app/solution/meta.log\n-rw-r--r-- 1 agent agent 4.8K Sep 23 05:36 /app/solution/meta.py\n-rw-r--r-- 1 agent agent 4.9K Sep 23 05:40 /app/solution/meta_extended.py\n-rw-r--r-- 1 agent agent 101M Sep 23 05:40 /app/solution/meta_past_samples.npz\nlow 0\nhigh 0\nmax 10653\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7230 }, "formatted_output": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\n-rw-r--r-- 1 agent agent 213 Sep 23 05:40 /app/solution/meta.log\n-rw-r--r-- 1 agent agent 4.8K Sep 23 05:36 /app/solution/meta.py\n-rw-r--r-- 1 agent agent 4.9K Sep 23 05:40 /app/solution/meta_extended.py\n-rw-r--r-- 1 agent agent 101M Sep 23 05:40 /app/solution/meta_past_samples.npz\nlow 0\nhigh 0\nmax 10653\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36667a21c87d289a3a90226617bd5", "usage": { "input_tokens": 61724, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 241, "reasoning_output_tokens": 79, "total_tokens": 61965 }, "turn_token_usage": { "input_tokens": 1954190, "cached_input_tokens": 1898496, "cache_write_input_tokens": 0, "output_tokens": 30951, "reasoning_output_tokens": 11484, "total_tokens": 1985141 }, "thread_token_usage": { "input_tokens": 1954190, "cached_input_tokens": 1898496, "cache_write_input_tokens": 0, "output_tokens": 30951, "reasoning_output_tokens": 11484, "total_tokens": 1985141 } } ## Step 55 · agent · 2026-09-23T05:41:23.309Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:41:32.671Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/verify.py <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd,numpy as np\\nfrom ranking_evaluation import validate_ranking\\np=Path('/app/solution');d=Path('/task/data');a=np.load(d/'test_eligible_candidates.npz');u=a['uids'];i=a['item_ids'];packed=a['packed_eligible'];f=validate_ranking(pd.read_parquet(p/'submission.parquet'),set(map(int,u)));r=pd.Index(u).get_indexer(f.uid);c=pd.Index(i).get_indexer(f.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(f):,} rows; {len(u):,} users; 100 distinct eligible items per user; ranks 1–100.')\\nPY\\npython - <<'PY'\\nimport shutil,os\\nfrom pathlib import Path\\np=Path('/app/solution');src=p/'test_recenttime_500_d3.parquet'\\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet')\\nPY\\n/opt/task-venv/bin/python /app/solution/verify.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:41:33.908Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"acc198","wall_time_seconds":1.000905408,"session_id":6958,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36670ad8c87d2b5b896f9617a656b", "usage": { "input_tokens": 62328, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 854, "reasoning_output_tokens": 516, "total_tokens": 63182 }, "turn_token_usage": { "input_tokens": 2016518, "cached_input_tokens": 1960064, "cache_write_input_tokens": 0, "output_tokens": 31805, "reasoning_output_tokens": 12000, "total_tokens": 2048323 }, "thread_token_usage": { "input_tokens": 2016518, "cached_input_tokens": 1960064, "cache_write_input_tokens": 0, "output_tokens": 31805, "reasoning_output_tokens": 12000, "total_tokens": 2048323 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8020942d-ece3-4690-b263-b4f2012ef821", "process_id": "6958", "command": [ "/bin/sh", "-lc", "cat > /app/solution/verify.py <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nimport pandas as pd,numpy as np\nfrom ranking_evaluation import validate_ranking\np=Path('/app/solution');d=Path('/task/data');a=np.load(d/'test_eligible_candidates.npz');u=a['uids'];i=a['item_ids'];packed=a['packed_eligible'];f=validate_ranking(pd.read_parquet(p/'submission.parquet'),set(map(int,u)));r=pd.Index(u).get_indexer(f.uid);c=pd.Index(i).get_indexer(f.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(f):,} rows; {len(u):,} users; 100 distinct eligible items per user; ranks 1–100.')\nPY\npython - <<'PY'\nimport shutil,os\nfrom pathlib import Path\np=Path('/app/solution');src=p/'test_recenttime_500_d3.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/verify.py <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nimport pandas as pd,numpy as np\nfrom ranking_evaluation import validate_ranking\np=Path('/app/solution');d=Path('/task/data');a=np.load(d/'test_eligible_candidates.npz');u=a['uids'];i=a['item_ids'];packed=a['packed_eligible'];f=validate_ranking(pd.read_parquet(p/'submission.parquet'),set(map(int,u)));r=pd.Index(u).get_indexer(f.uid);c=pd.Index(i).get_indexer(f.item_id);assert (r>=0).all() and (c>=0).all();assert (((packed[r,c//8]>>(c%8))&1)==1).all();print(f'VALID: {len(f):,} rows; {len(u):,} users; 100 distinct eligible items per user; ranks 1–100.')\nPY\npython - <<'PY'\nimport shutil,os\nfrom pathlib import Path\np=Path('/app/solution');src=p/'test_recenttime_500_d3.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 849145667 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n" } } ## Step 56 · agent · 2026-09-23T05:41:42.835Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:41:45.291Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -6 /app/solution/state.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/metax.log\",\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:6958,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:41:45.432Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"1083de","wall_time_seconds":0.00000871,"exit_code":1,"original_token_count":228,"output":"samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_d3 0.05111810031266231\nfreshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\npast fresh neighbors done 18.401851654052734\ntail: cannot open '/app/solution/metax.log' for reading: No such file or directory\n"} {"chunk_id":"b0db6d","wall_time_seconds":0.000002811,"exit_code":0,"original_token_count":22,"output":"VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3668e1ad487d297688fd14f2298fe", "usage": { "input_tokens": 63238, "cached_input_tokens": 62208, "cache_write_input_tokens": 0, "output_tokens": 322, "reasoning_output_tokens": 200, "total_tokens": 63560 }, "turn_token_usage": { "input_tokens": 2079756, "cached_input_tokens": 2022272, "cache_write_input_tokens": 0, "output_tokens": 32127, "reasoning_output_tokens": 12200, "total_tokens": 2111883 }, "thread_token_usage": { "input_tokens": 2079756, "cached_input_tokens": 2022272, "cache_write_input_tokens": 0, "output_tokens": 32127, "reasoning_output_tokens": 12200, "total_tokens": 2111883 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-32a2d66a-2f60-45d3-a2af-26dd15153097", "process_id": "75881", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -6 /app/solution/state.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/metax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/meta.log; tail -8 /app/solution/fusion.log; tail -6 /app/solution/state.log; tail -5 /app/solution/test_extra.log; tail -5 /app/solution/past_extra.log; tail -5 /app/solution/metax.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_d3 0.05111810031266231\nfreshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\npast fresh neighbors done 18.401851654052734\ntail: cannot open '/app/solution/metax.log' for reading: No such file or directory\n", "stderr": "", "aggregated_output": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_d3 0.05111810031266231\nfreshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\npast fresh neighbors done 18.401851654052734\ntail: cannot open '/app/solution/metax.log' for reading: No such file or directory\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 8710 }, "formatted_output": "samples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_d3 0.05111810031266231\nfreshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\ntest recent neighbors done 31.027137756347656\nrecenttime_500_d3 40\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\npast recent neighbors done 28.770633697509766\nrecenttime_500_d3 40\nrecenttime_500_short 39\npast fresh neighbors done 18.401851654052734\ntail: cannot open '/app/solution/metax.log' for reading: No such file or directory\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2511eccd-e5b8-47cf-8f3f-2b293eadc4ae", "process_id": "59339", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py past recent > /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 d3 >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 short >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py past fresh >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past fresh 500 d3 >> /app/solution/past_extra.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py past recent > /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 d3 >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past recent 500 short >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py past fresh >> /app/solution/past_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py past fresh 500 d3 >> /app/solution/past_extra.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 179, "nanos": 982237753 }, "formatted_output": "" } } ## Step 57 · agent · 2026-09-23T05:42:05.001Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c8a06c99-8b98-489d-87a4-dfbb72b5cae4", "process_id": "61016", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test recent > /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 d3 >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 short >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test fresh >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test fresh 500 d3 >> /app/solution/test_extra.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test recent > /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 d3 >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test recent 500 short >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py test fresh >> /app/solution/test_extra.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py test fresh 500 d3 >> /app/solution/test_extra.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 188, "nanos": 603327321 }, "formatted_output": "" } } ### exec · 2026-09-23T05:42:09.925Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nfor name in ['meta.py','meta_extended.py']:\\n p=Path('/app/solution')/name;s=p.read_text().replace(\\\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8)\\\",\\\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1).astype(np.float32),1e-8)\\\");p.write_text(s)\\nPY\\ntail -8 /app/solution/fusion.log; tail -5 /app/solution/state.log; tail -4 /app/solution/meta.log; tail -4 /app/solution/metax.log; tail -3 /app/solution/test_extra.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:42:10.154Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"f9f8c2","wall_time_seconds":0.00000758,"exit_code":0,"original_token_count":181,"output":"freshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\nfreshtime_500_d3 47\n"} {"current_time":"2026-09-23 05:42:10 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36699a8ec87d2952657e8d5a2cc46", "usage": { "input_tokens": 63998, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 714, "reasoning_output_tokens": 516, "total_tokens": 64712 }, "turn_token_usage": { "input_tokens": 2143754, "cached_input_tokens": 2085376, "cache_write_input_tokens": 0, "output_tokens": 32841, "reasoning_output_tokens": 12716, "total_tokens": 2176595 }, "thread_token_usage": { "input_tokens": 2143754, "cached_input_tokens": 2085376, "cache_write_input_tokens": 0, "output_tokens": 32841, "reasoning_output_tokens": 12716, "total_tokens": 2176595 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3de5607b-57c9-47a3-80ed-7667c026d653", "process_id": "96479", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nfor name in ['meta.py','meta_extended.py']:\n p=Path('/app/solution')/name;s=p.read_text().replace(\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8)\",\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1).astype(np.float32),1e-8)\");p.write_text(s)\nPY\ntail -8 /app/solution/fusion.log; tail -5 /app/solution/state.log; tail -4 /app/solution/meta.log; tail -4 /app/solution/metax.log; tail -3 /app/solution/test_extra.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nfor name in ['meta.py','meta_extended.py']:\n p=Path('/app/solution')/name;s=p.read_text().replace(\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1),1e-8)\",\"np.max(s[np.arange(len(self.tu))[:,None],self.pool],axis=1).astype(np.float32),1e-8)\");p.write_text(s)\nPY\ntail -8 /app/solution/fusion.log; tail -5 /app/solution/state.log; tail -4 /app/solution/meta.log; tail -4 /app/solution/metax.log; tail -3 /app/solution/test_extra.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "freshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\nfreshtime_500_d3 47\n", "stderr": "", "aggregated_output": "freshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\nfreshtime_500_d3 47\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7580 }, "formatted_output": "freshtime_500_d3 0.052187610632199004\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nrecenttime_500_short 40\ntest fresh neighbors done 22.26416563987732\nfreshtime_500_d3 47\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5962c37-2e25-44d9-bc37-e4b5ff41cb83", "process_id": "60957", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py validation > /app/solution/fusion.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py validation > /app/solution/fusion.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 77, "nanos": 197194927 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f38e6026-f82b-4fd5-88c5-d6c84e0aad3f", "process_id": "33934", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\"if mode=='organic':\", \"if mode=='state':\\n r=matrix('recent',stage);r.data=np.log1p(r.data);l=matrix('likes',stage);l.data[:]=1;x=x*.25+r-l*.75\\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');rr=U.get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0)&(d.state.values=='liked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x+a*.75\\n m=(rr>=0)&(cc>=0)&(d.state.values=='disliked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x-x.multiply(a);x.eliminate_zeros()\\nif mode=='organic':\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 short > /app/solution/state.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation state >> /app/solution/state.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation state 500 d3 >> /app/solution/state.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/neighbors_extra.py';s=open(p).read().replace(\"if mode=='organic':\", \"if mode=='state':\\n r=matrix('recent',stage);r.data=np.log1p(r.data);l=matrix('likes',stage);l.data[:]=1;x=x*.25+r-l*.75\\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');rr=U.get_indexer(d.uid);cc=I.get_indexer(d.item_id);m=(rr>=0)&(cc>=0)&(d.state.values=='liked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x+a*.75\\n m=(rr>=0)&(cc>=0)&(d.state.values=='disliked');a=sp.coo_matrix((np.ones(m.sum(),np.float32),(rr[m],cc[m])),shape=x.shape).tocsr();x=x-x.multiply(a);x.eliminate_zeros()\\nif mode=='organic':\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation recent 500 short > /app/solution/state.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors_extra.py validation state >> /app/solution/state.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation state 500 d3 >> /app/solution/state.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 108, "nanos": 234989378 }, "formatted_output": "" } } ## Step 58 · agent · 2026-09-23T05:42:29.832Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:42:32.180Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport shutil,os\\nfrom pathlib import Path\\np=Path('/app/solution');src=p/'test_freshtime_500_d3.parquet'\\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Saved refitted freshest-history ranker')\\nPY\\ntail -8 /app/solution/meta.log; tail -8 /app/solution/metax.log; tail -8 /app/solution/fusion.log; tail -4 /app/solution/state.log\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:42:32.450Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"4dc43e","wall_time_seconds":0.069907278,"exit_code":0,"original_token_count":236,"output":"Saved refitted freshest-history ranker\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\ndone {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': np.float64(0.05303681918746273)} 76.1865930557251\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nstatetime_500_d3 0.05146998481441888 45\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab366b25df887d2870dba98c0e91641", "usage": { "input_tokens": 65071, "cached_input_tokens": 63872, "cache_write_input_tokens": 0, "output_tokens": 639, "reasoning_output_tokens": 483, "total_tokens": 65710 }, "turn_token_usage": { "input_tokens": 2208825, "cached_input_tokens": 2149248, "cache_write_input_tokens": 0, "output_tokens": 33480, "reasoning_output_tokens": 13199, "total_tokens": 2242305 }, "thread_token_usage": { "input_tokens": 2208825, "cached_input_tokens": 2149248, "cache_write_input_tokens": 0, "output_tokens": 33480, "reasoning_output_tokens": 13199, "total_tokens": 2242305 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fedccfe1-9e11-426a-9a47-9511bfd0f23a", "process_id": "77735", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport shutil,os\nfrom pathlib import Path\np=Path('/app/solution');src=p/'test_freshtime_500_d3.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Saved refitted freshest-history ranker')\nPY\ntail -8 /app/solution/meta.log; tail -8 /app/solution/metax.log; tail -8 /app/solution/fusion.log; tail -4 /app/solution/state.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport shutil,os\nfrom pathlib import Path\np=Path('/app/solution');src=p/'test_freshtime_500_d3.parquet'\nif src.exists():shutil.copyfile(src,p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');print('Saved refitted freshest-history ranker')\nPY\ntail -8 /app/solution/meta.log; tail -8 /app/solution/metax.log; tail -8 /app/solution/fusion.log; tail -4 /app/solution/state.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Saved refitted freshest-history ranker\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\ndone {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': np.float64(0.05303681918746273)} 76.1865930557251\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nstatetime_500_d3 0.05146998481441888 45\n", "stderr": "", "aggregated_output": "Saved refitted freshest-history ranker\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\ndone {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': np.float64(0.05303681918746273)} 76.1865930557251\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nstatetime_500_d3 0.05146998481441888 45\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 69905068 }, "formatted_output": "Saved refitted freshest-history ranker\nsamples past 557024 11144\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\ncattime_100_d3 0.04407927278811843\ncattime_300_short 0.04332227893854272\nstart 0.05217449385105529 freshtime_500_d3\nimprove 0.052248203747237376 recenttime_500_d3 0.5\nimprove 0.05248323521049822 pop_d1 0.1\nimprove 0.05296026638198792 pop_d1_sq 0.25\nimprove 0.05303681918746273 pop_d3 0.1\ndone {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': np.float64(0.05303681918746273)} 76.1865930557251\nrecenttime_500_short 0.046311889284623244 36\nvalidation state neighbors done 23.27647590637207\nstatetime_500_d3 0.05146998481441888 45\n" } } ## Step 59 · agent · 2026-09-23T05:42:36.325Z ### Thinking unavailable — no text recorded The ensemble now scores 0.053037 NDCG@10, up from the 0.025879 popularity baseline. The saved refitted submission passes the structural and eligibility checks. I’m finishing the learned-reranker comparison and refitting the ensemble as another saved candidate. ### exec · 2026-09-23T05:42:44.092Z { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py test > /app/solution/fusion_test.log 2>&1\\npython - <<'PY'\\nimport shutil,os\\np='/app/solution/';shutil.copyfile(p+'submission_fusion.parquet',p+'submission.next.parquet');os.replace(p+'submission.next.parquet',p+'submission.parquet')\\nPY\\n/opt/task-venv/bin/python /app/solution/verify.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion.parquet > /app/solution/public_score.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:42:46.469Z Script completed Wall time 2.4 seconds Output: {"chunk_id":"b68ffe","wall_time_seconds":1.000467552,"session_id":95632,"original_token_count":0,"output":""} {"chunk_id":"0e1366","wall_time_seconds":1.000850292,"session_id":93876,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab366c89f6487d286fba75684266057", "usage": { "input_tokens": 66148, "cached_input_tokens": 64896, "cache_write_input_tokens": 0, "output_tokens": 310, "reasoning_output_tokens": 47, "total_tokens": 66458 }, "turn_token_usage": { "input_tokens": 2274973, "cached_input_tokens": 2214144, "cache_write_input_tokens": 0, "output_tokens": 33790, "reasoning_output_tokens": 13246, "total_tokens": 2308763 }, "thread_token_usage": { "input_tokens": 2274973, "cached_input_tokens": 2214144, "cache_write_input_tokens": 0, "output_tokens": 33790, "reasoning_output_tokens": 13246, "total_tokens": 2308763 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-75465eea-2bd6-40f0-98da-36c6310fba3f", "process_id": "93876", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion.parquet > /app/solution/public_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion.parquet > /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 5, "nanos": 761034160 }, "formatted_output": "" } } ## Step 60 · agent · 2026-09-23T05:43:04.415Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:43:10.315Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport glob,time\\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\\n try:\\n a=open(f,'rb').read().split(b'\\\\0')\\n if len(a)>1 and a[0].endswith(b'python') and b'/app/solution/' in a[1]:\\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\\n except OSError:pass\\nPY\\ntail -5 /app/solution/meta.log; tail -5 /app/solution/metax.log; tail -4 /app/solution/fusion_test.log; cat /app/solution/public_score.json\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:43:10.615Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"ec328f","wall_time_seconds":0.080555063,"exit_code":0,"original_token_count":185,"output":"1125 [b'/app/solution/meta.py', b'train'] cpu_s 216.35 state R\n1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 40.28 state R\n1383 [b'/app/solution/fusion.py', b'test'] cpu_s 14.46 state R\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nmeta 15 0.04845492338460859 229\n{\n \"model\": \"/app/solution/validation_fusion.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.053037,\n \"quality_users\": 4263\n }\n}\n"} {"current_time":"2026-09-23 05:43:10 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab366d6a98887d28104d4352e26b0f3", "usage": { "input_tokens": 66550, "cached_input_tokens": 65920, "cache_write_input_tokens": 0, "output_tokens": 730, "reasoning_output_tokens": 509, "total_tokens": 67280 }, "turn_token_usage": { "input_tokens": 2341523, "cached_input_tokens": 2280064, "cache_write_input_tokens": 0, "output_tokens": 34520, "reasoning_output_tokens": 13755, "total_tokens": 2376043 }, "thread_token_usage": { "input_tokens": 2341523, "cached_input_tokens": 2280064, "cache_write_input_tokens": 0, "output_tokens": 34520, "reasoning_output_tokens": 13755, "total_tokens": 2376043 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7cd91621-243e-4c67-82a1-a761a4b5171e", "process_id": "51181", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport glob,time\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(f,'rb').read().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and b'/app/solution/' in a[1]:\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\n except OSError:pass\nPY\ntail -5 /app/solution/meta.log; tail -5 /app/solution/metax.log; tail -4 /app/solution/fusion_test.log; cat /app/solution/public_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport glob,time\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(f,'rb').read().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and b'/app/solution/' in a[1]:\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\n except OSError:pass\nPY\ntail -5 /app/solution/meta.log; tail -5 /app/solution/metax.log; tail -4 /app/solution/fusion_test.log; cat /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1125 [b'/app/solution/meta.py', b'train'] cpu_s 216.35 state R\n1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 40.28 state R\n1383 [b'/app/solution/fusion.py', b'test'] cpu_s 14.46 state R\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nmeta 15 0.04845492338460859 229\n{\n \"model\": \"/app/solution/validation_fusion.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.053037,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "1125 [b'/app/solution/meta.py', b'train'] cpu_s 216.35 state R\n1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 40.28 state R\n1383 [b'/app/solution/fusion.py', b'test'] cpu_s 14.46 state R\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nmeta 15 0.04845492338460859 229\n{\n \"model\": \"/app/solution/validation_fusion.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.053037,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 80436257 }, "formatted_output": "1125 [b'/app/solution/meta.py', b'train'] cpu_s 216.35 state R\n1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 40.28 state R\n1383 [b'/app/solution/fusion.py', b'test'] cpu_s 14.46 state R\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\n/app/solution/meta.py:23: RuntimeWarning: invalid value encountered in divide\n v=np.maximum(np.nan_to_num(s[r,c].astype(np.float32),neginf=0),0);f.extend([np.log1p(v),v/self.maxs[r,j]])\nmeta 15 0.04845492338460859 229\n{\n \"model\": \"/app/solution/validation_fusion.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.053037,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 61 · agent · 2026-09-23T05:43:30.255Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f9e59d26-7465-48bd-8102-9cf331cd4d72", "process_id": "95632", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py test > /app/solution/fusion_test.log 2>&1\npython - <<'PY'\nimport shutil,os\np='/app/solution/';shutil.copyfile(p+'submission_fusion.parquet',p+'submission.next.parquet');os.replace(p+'submission.next.parquet',p+'submission.parquet')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/fusion.py test > /app/solution/fusion_test.log 2>&1\npython - <<'PY'\nimport shutil,os\np='/app/solution/';shutil.copyfile(p+'submission_fusion.parquet',p+'submission.next.parquet');os.replace(p+'submission.next.parquet',p+'submission.parquet')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n", "exit_code": 0, "duration": { "secs": 54, "nanos": 824326742 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n" } } ## Step 62 · agent · 2026-09-23T05:43:46.841Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:43:58.746Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os\\np='/proc/1125/cmdline'\\ntry:\\n if b'/app/solution/meta.py' in open(p,'rb').read():os.kill(1125,15);print('Stopped weaker reranker experiment after its first result')\\nexcept OSError:pass\\np='/app/solution/meta_extended.py';s=open(p).read();needle=\\\"self.pops={};self.uc=[]\\\";s=s.replace(needle,needle+\\\"\\\\n cache=O/f'{stage}_metax_data_cache.npz'\\\\n if cache.exists():\\\\n z=np.load(cache,allow_pickle=True);self.pops=z['pops'].item();self.uc=z['uc'];self.pool=z['pool'];self.valid=z['valid'];self.maxs=z['maxs'];return\\\")\\nneedle=\\\" def features(self,r,c):\\\";s=s.replace(needle,\\\" np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\\\\n\\\"+needle);open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY' > /app/solution/meta_cache.log 2>&1\\nimport sys\\nsys.path.insert(0,'/app/solution')\\nfrom meta_extended import *\\nv=Data('validation');x,y,w=v.sample();np.savez(O/'metax_val_samples.npz',x=x,y=y,w=w);print('validation samples ready',flush=True)\\nd=Data('test');print('test cache ready',flush=True)\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -6 /app/solution/metax.log; tail -3 /app/solution/fusion_test.log; ls -lh /app/solution/metax*\",\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:43:59.975Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d6b2bc","wall_time_seconds":1.000876964,"session_id":63729,"original_token_count":15,"output":"Stopped weaker reranker experiment after its first result\n"} {"chunk_id":"90d6cc","wall_time_seconds":0.00000371,"exit_code":0,"original_token_count":81,"output":"done {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': 0.05303681918746273} 51.483962059020996\n-rw-r--r-- 1 agent agent 0 Sep 23 05:42 /app/solution/metax.log\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab366eed99c87d29f6f1543ec6410f1", "usage": { "input_tokens": 67616, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 1472, "reasoning_output_tokens": 1034, "total_tokens": 69088 }, "turn_token_usage": { "input_tokens": 2409139, "cached_input_tokens": 2346496, "cache_write_input_tokens": 0, "output_tokens": 35992, "reasoning_output_tokens": 14789, "total_tokens": 2445131 }, "thread_token_usage": { "input_tokens": 2409139, "cached_input_tokens": 2346496, "cache_write_input_tokens": 0, "output_tokens": 35992, "reasoning_output_tokens": 14789, "total_tokens": 2445131 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8b57d5ea-ebb8-4774-874d-36a0bb050651", "process_id": "40689", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py validation > /app/solution/validation_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user2.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta.py train > /app/solution/meta.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/make_neighbors.py validation > /app/solution/validation_neighbors.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/time_user.py validation cat > /app/solution/time_user2.log 2>&1\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta.py train > /app/solution/meta.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 473, "nanos": 37101211 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-59f68553-e881-40b5-82a0-a7d36c3128e2", "process_id": "39443", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/metax.log; tail -3 /app/solution/fusion_test.log; ls -lh /app/solution/metax*" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/metax.log; tail -3 /app/solution/fusion_test.log; ls -lh /app/solution/metax*" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "done {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': 0.05303681918746273} 51.483962059020996\n-rw-r--r-- 1 agent agent 0 Sep 23 05:42 /app/solution/metax.log\n", "stderr": "", "aggregated_output": "done {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': 0.05303681918746273} 51.483962059020996\n-rw-r--r-- 1 agent agent 0 Sep 23 05:42 /app/solution/metax.log\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3710 }, "formatted_output": "done {'names': ['recenttime_500_d3', 'freshtime_500_d3', 'cattime_100_d3', 'cattime_300_short'], 'weights': {'recenttime_500_d3': 0.5, 'freshtime_500_d3': 1.0, 'pop_d1': 0.1, 'pop_d1_sq': 0.25, 'pop_d3': 0.1}, 'score': 0.05303681918746273} 51.483962059020996\n-rw-r--r-- 1 agent agent 0 Sep 23 05:42 /app/solution/metax.log\n" } } ## Step 63 · agent · 2026-09-23T05:44:12.790Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:44:24.001Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/refit_meta.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nos.environ['OMP_NUM_THREADS']='3'\\nfrom meta_extended import *\\nimport time\\nfor filename in ['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']:\\n while not (O/filename).exists():time.sleep(2)\\n# The cache completion message follows the final write.\\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\\na=np.load(O/'metax_past_samples.npz');b=np.load(O/'metax_val_samples.npz');model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'));print('final model fit',flush=True);d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_metax.parquet');print('final meta done',flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:44:25.180Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"cb77f4","wall_time_seconds":1.000750086,"session_id":27099,"original_token_count":0,"output":""} {"current_time":"2026-09-23 05:44:25 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367202c5c87d2821d403732a77475", "usage": { "input_tokens": 69339, "cached_input_tokens": 67456, "cache_write_input_tokens": 0, "output_tokens": 748, "reasoning_output_tokens": 355, "total_tokens": 70087 }, "turn_token_usage": { "input_tokens": 2478478, "cached_input_tokens": 2413952, "cache_write_input_tokens": 0, "output_tokens": 36740, "reasoning_output_tokens": 15144, "total_tokens": 2515218 }, "thread_token_usage": { "input_tokens": 2478478, "cached_input_tokens": 2413952, "cache_write_input_tokens": 0, "output_tokens": 36740, "reasoning_output_tokens": 15144, "total_tokens": 2515218 } } ## Step 64 · agent · 2026-09-23T05:44:46.670Z ### Thinking unavailable — no text recorded ## Step 65 · agent · 2026-09-23T05:45:00.704Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:45:18.632Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/refit_meta.py';s=open(p).read().replace(\\\"s=d.predict(model);ix=\\\", \\\"s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);ix=\\\");open(p,'w').write(s)\\nPY\\ncat > /app/solution/blend_meta.py <<'PY'\\nimport os\\nos.environ['OPENBLAS_NUM_THREADS']='2'\\nfrom common import *\\nimport sys,json\\nstage=sys.argv[1];cache=np.load(O/f'{stage}_metax_data_cache.npz',allow_pickle=True);pool=cache['pool'];valid=cache['valid'];pops=cache['pops'].item();r=np.arange(len(pool))[:,None];cfg=json.load(open(O/'fusion.json'));f=np.zeros(pool.shape,np.float32)\\nfor name,w in cfg['weights'].items():\\n if name.startswith('pop_'):\\n k=name[4:];sq=k.endswith('_sq');k=k[:-3] if sq else k;v=pops[k][pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8)\\n if sq:v=v*v\\n else:\\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.maximum(np.nan_to_num(a[r,pool].astype(np.float32),neginf=0),0);v/=np.maximum(v.max(axis=1,keepdims=True),1e-8)\\n f+=w*v\\nf/=np.maximum(f.max(axis=1,keepdims=True),1e-8);m=np.load(O/f'{stage}_metax_15_pool_scores.npy');m=np.maximum(np.nan_to_num(m,neginf=0),0);m/=np.maximum(m.max(axis=1,keepdims=True),1e-8)\\nif stage=='validation':\\n best=(-1,None,None)\\n for power in [.5,1,2]:\\n for w in [0,.1,.25,.5,.75,1]:\\n s=(1-w)*f+w*m**power;s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s,10),axis=1);score=evaluate(ix)\\n if score>best[0]:best=(score,w,power);print('best',best,flush=True)\\n config={'score':best[0],'weight':best[1],'power':best[2]};json.dump(config,open(O/'blend_meta.json','w'))\\nelse:config=json.load(open(O/'blend_meta.json'))\\ns=(1-config['weight'])*f+config['weight']*m**config['power'];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_final.parquet' if stage=='validation' else 'submission_blended.parquet');print(config,flush=True)\\nPY\\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -3 /app/solution/refit_meta.log\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:45:18.736Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"37086a","wall_time_seconds":0.00000455,"exit_code":0,"original_token_count":66,"output":"samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-417d4cdb-e80a-4ec0-87b5-edb81f5fab63", "process_id": "89474", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/refit_meta.py';s=open(p).read().replace(\"s=d.predict(model);ix=\", \"s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);ix=\");open(p,'w').write(s)\nPY\ncat > /app/solution/blend_meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys,json\nstage=sys.argv[1];cache=np.load(O/f'{stage}_metax_data_cache.npz',allow_pickle=True);pool=cache['pool'];valid=cache['valid'];pops=cache['pops'].item();r=np.arange(len(pool))[:,None];cfg=json.load(open(O/'fusion.json'));f=np.zeros(pool.shape,np.float32)\nfor name,w in cfg['weights'].items():\n if name.startswith('pop_'):\n k=name[4:];sq=k.endswith('_sq');k=k[:-3] if sq else k;v=pops[k][pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8)\n if sq:v=v*v\n else:\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.maximum(np.nan_to_num(a[r,pool].astype(np.float32),neginf=0),0);v/=np.maximum(v.max(axis=1,keepdims=True),1e-8)\n f+=w*v\nf/=np.maximum(f.max(axis=1,keepdims=True),1e-8);m=np.load(O/f'{stage}_metax_15_pool_scores.npy');m=np.maximum(np.nan_to_num(m,neginf=0),0);m/=np.maximum(m.max(axis=1,keepdims=True),1e-8)\nif stage=='validation':\n best=(-1,None,None)\n for power in [.5,1,2]:\n for w in [0,.1,.25,.5,.75,1]:\n s=(1-w)*f+w*m**power;s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s,10),axis=1);score=evaluate(ix)\n if score>best[0]:best=(score,w,power);print('best',best,flush=True)\n config={'score':best[0],'weight':best[1],'power':best[2]};json.dump(config,open(O/'blend_meta.json','w'))\nelse:config=json.load(open(O/'blend_meta.json'))\ns=(1-config['weight'])*f+config['weight']*m**config['power'];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_final.parquet' if stage=='validation' else 'submission_blended.parquet');print(config,flush=True)\nPY\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -3 /app/solution/refit_meta.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/refit_meta.py';s=open(p).read().replace(\"s=d.predict(model);ix=\", \"s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);ix=\");open(p,'w').write(s)\nPY\ncat > /app/solution/blend_meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nfrom common import *\nimport sys,json\nstage=sys.argv[1];cache=np.load(O/f'{stage}_metax_data_cache.npz',allow_pickle=True);pool=cache['pool'];valid=cache['valid'];pops=cache['pops'].item();r=np.arange(len(pool))[:,None];cfg=json.load(open(O/'fusion.json'));f=np.zeros(pool.shape,np.float32)\nfor name,w in cfg['weights'].items():\n if name.startswith('pop_'):\n k=name[4:];sq=k.endswith('_sq');k=k[:-3] if sq else k;v=pops[k][pool];v=v/np.maximum(v.max(axis=1,keepdims=True),1e-8)\n if sq:v=v*v\n else:\n a=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');v=np.maximum(np.nan_to_num(a[r,pool].astype(np.float32),neginf=0),0);v/=np.maximum(v.max(axis=1,keepdims=True),1e-8)\n f+=w*v\nf/=np.maximum(f.max(axis=1,keepdims=True),1e-8);m=np.load(O/f'{stage}_metax_15_pool_scores.npy');m=np.maximum(np.nan_to_num(m,neginf=0),0);m/=np.maximum(m.max(axis=1,keepdims=True),1e-8)\nif stage=='validation':\n best=(-1,None,None)\n for power in [.5,1,2]:\n for w in [0,.1,.25,.5,.75,1]:\n s=(1-w)*f+w*m**power;s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s,10),axis=1);score=evaluate(ix)\n if score>best[0]:best=(score,w,power);print('best',best,flush=True)\n config={'score':best[0],'weight':best[1],'power':best[2]};json.dump(config,open(O/'blend_meta.json','w'))\nelse:config=json.load(open(O/'blend_meta.json'))\ns=(1-config['weight'])*f+config['weight']*m**config['power'];s[~valid]=-np.inf;ix=np.take_along_axis(pool,top(s),axis=1);save(ix,stage,'validation_final.parquet' if stage=='validation' else 'submission_blended.parquet');print(config,flush=True)\nPY\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -3 /app/solution/refit_meta.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n", "stderr": "", "aggregated_output": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4550 }, "formatted_output": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367395c1887d2b8aa20dcac31ad18", "usage": { "input_tokens": 70163, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 1685, "reasoning_output_tokens": 980, "total_tokens": 71848 }, "turn_token_usage": { "input_tokens": 2548641, "cached_input_tokens": 2483072, "cache_write_input_tokens": 0, "output_tokens": 38425, "reasoning_output_tokens": 16124, "total_tokens": 2587066 }, "thread_token_usage": { "input_tokens": 2548641, "cached_input_tokens": 2483072, "cache_write_input_tokens": 0, "output_tokens": 38425, "reasoning_output_tokens": 16124, "total_tokens": 2587066 } } ## Step 66 · agent · 2026-09-23T05:45:26.142Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:45:30.398Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os,glob\\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\\n try:\\n a=open(p,'rb').read().split(b'\\\\0')\\n if len(a)>1 and a[1]==b'/app/solution/refit_meta.py':os.kill(int(p.split('/')[2]),15)\\n except OSError:pass\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:45:31.577Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"37c5f2","wall_time_seconds":1.000786531,"session_id":77159,"original_token_count":0,"output":""} {"current_time":"2026-09-23 05:45:31 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d90d276d-e116-42ce-8bf2-6cfa42aa2cb4", "process_id": "27099", "command": [ "/bin/sh", "-lc", "cat > /app/solution/refit_meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nos.environ['OMP_NUM_THREADS']='3'\nfrom meta_extended import *\nimport time\nfor filename in ['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']:\n while not (O/filename).exists():time.sleep(2)\n# The cache completion message follows the final write.\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\na=np.load(O/'metax_past_samples.npz');b=np.load(O/'metax_val_samples.npz');model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'));print('final model fit',flush=True);d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_metax.parquet');print('final meta done',flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/refit_meta.py <<'PY'\nimport os\nos.environ['OPENBLAS_NUM_THREADS']='2'\nos.environ['OMP_NUM_THREADS']='3'\nfrom meta_extended import *\nimport time\nfor filename in ['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']:\n while not (O/filename).exists():time.sleep(2)\n# The cache completion message follows the final write.\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\na=np.load(O/'metax_past_samples.npz');b=np.load(O/'metax_val_samples.npz');model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42);model.fit(np.concatenate([a['x'],b['x']]),np.concatenate([a['y'],b['y']]),sample_weight=np.concatenate([a['w'],b['w']]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'));print('final model fit',flush=True);d=Data('test');s=d.predict(model);ix=np.take_along_axis(d.pool,top(s),axis=1);save(ix,'test','submission_metax.parquet');print('final meta done',flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 66, "nanos": 343249699 }, "formatted_output": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3676f173487d2bb8741f2ae39d0bd", "usage": { "input_tokens": 71972, "cached_input_tokens": 70016, "cache_write_input_tokens": 0, "output_tokens": 328, "reasoning_output_tokens": 157, "total_tokens": 72300 }, "turn_token_usage": { "input_tokens": 2620613, "cached_input_tokens": 2553088, "cache_write_input_tokens": 0, "output_tokens": 38753, "reasoning_output_tokens": 16281, "total_tokens": 2659366 }, "thread_token_usage": { "input_tokens": 2620613, "cached_input_tokens": 2553088, "cache_write_input_tokens": 0, "output_tokens": 38753, "reasoning_output_tokens": 16281, "total_tokens": 2659366 } } ## Step 67 · agent · 2026-09-23T05:45:51.996Z ### Thinking unavailable — no text recorded The public evaluator confirms 0.053037 NDCG@10 for the ensemble, which is refitted, saved, and valid. The first reranker scored lower; I’m finishing one version with the stronger recent-history features and preparing the rebuild script and documentation. ### exec · 2026-09-23T05:46:22.751Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/rebuild.py <<'PY'\\n\\\"\\\"\\\"Rebuild the selected submission from read-only task inputs.\\\"\\\"\\\"\\nimport os,sys,json,subprocess,shutil,re\\nfrom pathlib import Path\\nO=Path(__file__).resolve().parent\\nos.environ['OPENBLAS_NUM_THREADS']='2';os.environ['OMP_NUM_THREADS']='4'\\ndef run(name,*args):subprocess.run([sys.executable,str(O/name),*map(str,args)],check=True,cwd=O)\\ndef score_stage(stage,names):\\n modes=sorted(set(re.match(r'(.+)time_',n)[1] for n in names))\\n for mode in modes:\\n if mode=='cat':run('make_neighbors.py',stage)\\n else:run('neighbors_extra.py',stage,mode)\\n for name in names:\\n mode,k,kind=re.match(r'(.+)time_(\\\\d+)_(.+)',name).groups();run('time_user.py',stage,mode,k,kind)\\ndef main():\\n selection=json.load(open(O/'selection.json'));run('prepare.py');run('temporal.py')\\n if selection['method']=='fusion':\\n names=json.load(open(O/'fusion.json'))['names'];score_stage('test',names);run('fusion.py','test');src=O/'submission_fusion.parquet'\\n else:\\n from meta_extended import names,Data,HistGradientBoostingClassifier,np,pickle,top,save\\n score_stage('test',names)\\n if '--retrain' in sys.argv:\\n run('build_past.py')\\n samples=[]\\n for stage in ['past','validation']:\\n score_stage(stage,names);cache=O/f'{stage}_metax_data_cache.npz'\\n if cache.exists():cache.unlink()\\n samples.append(Data(stage).sample())\\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42)\\n model.fit(np.concatenate([a[0] for a in samples]),np.concatenate([a[1] for a in samples]),sample_weight=np.concatenate([a[2] for a in samples]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'))\\n model=pickle.load(open(O/'metax_final_model.pkl','rb'));cache=O/'test_metax_data_cache.npz'\\n if cache.exists():cache.unlink()\\n d=Data('test');s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);run('blend_meta.py','test');src=O/'submission_blended.parquet'\\n shutil.copyfile(src,O/'submission.next.parquet');os.replace(O/'submission.next.parquet',O/'submission.parquet');run('verify.py')\\nif __name__=='__main__':main()\\nPY\\ncat > /app/solution/selection.json <<'JSON'\\n{\\\"method\\\":\\\"fusion\\\",\\\"validation_ndcg_at_10\\\":0.053037}\\nJSON\\ncat > /app/solution/README.md <<'MD'\\n# Music discovery ranker\\n\\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\\n\\nThe selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\\n\\nPublic validation NDCG@10: **0.053037** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\\n\\nRebuild with the installed environment:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/rebuild.py\\n```\\n\\n`selection.json` and `fusion.json` record the chosen model. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\\nMD\\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -5 /app/solution/refit_meta.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:46:22.859Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"4d1686","wall_time_seconds":0.00000538,"exit_code":0,"original_token_count":68,"output":"samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\nTerminated\n"} {"current_time":"2026-09-23 05:46:22 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3677bc11487d299d3f498a6f9f334", "usage": { "input_tokens": 72377, "cached_input_tokens": 71808, "cache_write_input_tokens": 0, "output_tokens": 1608, "reasoning_output_tokens": 516, "total_tokens": 73985 }, "turn_token_usage": { "input_tokens": 2692990, "cached_input_tokens": 2624896, "cache_write_input_tokens": 0, "output_tokens": 40361, "reasoning_output_tokens": 16797, "total_tokens": 2733351 }, "thread_token_usage": { "input_tokens": 2692990, "cached_input_tokens": 2624896, "cache_write_input_tokens": 0, "output_tokens": 40361, "reasoning_output_tokens": 16797, "total_tokens": 2733351 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-87545029-e020-4a91-9b3b-aee694c0b248", "process_id": "53987", "command": [ "/bin/sh", "-lc", "cat > /app/solution/rebuild.py <<'PY'\n\"\"\"Rebuild the selected submission from read-only task inputs.\"\"\"\nimport os,sys,json,subprocess,shutil,re\nfrom pathlib import Path\nO=Path(__file__).resolve().parent\nos.environ['OPENBLAS_NUM_THREADS']='2';os.environ['OMP_NUM_THREADS']='4'\ndef run(name,*args):subprocess.run([sys.executable,str(O/name),*map(str,args)],check=True,cwd=O)\ndef score_stage(stage,names):\n modes=sorted(set(re.match(r'(.+)time_',n)[1] for n in names))\n for mode in modes:\n if mode=='cat':run('make_neighbors.py',stage)\n else:run('neighbors_extra.py',stage,mode)\n for name in names:\n mode,k,kind=re.match(r'(.+)time_(\\d+)_(.+)',name).groups();run('time_user.py',stage,mode,k,kind)\ndef main():\n selection=json.load(open(O/'selection.json'));run('prepare.py');run('temporal.py')\n if selection['method']=='fusion':\n names=json.load(open(O/'fusion.json'))['names'];score_stage('test',names);run('fusion.py','test');src=O/'submission_fusion.parquet'\n else:\n from meta_extended import names,Data,HistGradientBoostingClassifier,np,pickle,top,save\n score_stage('test',names)\n if '--retrain' in sys.argv:\n run('build_past.py')\n samples=[]\n for stage in ['past','validation']:\n score_stage(stage,names);cache=O/f'{stage}_metax_data_cache.npz'\n if cache.exists():cache.unlink()\n samples.append(Data(stage).sample())\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42)\n model.fit(np.concatenate([a[0] for a in samples]),np.concatenate([a[1] for a in samples]),sample_weight=np.concatenate([a[2] for a in samples]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'))\n model=pickle.load(open(O/'metax_final_model.pkl','rb'));cache=O/'test_metax_data_cache.npz'\n if cache.exists():cache.unlink()\n d=Data('test');s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);run('blend_meta.py','test');src=O/'submission_blended.parquet'\n shutil.copyfile(src,O/'submission.next.parquet');os.replace(O/'submission.next.parquet',O/'submission.parquet');run('verify.py')\nif __name__=='__main__':main()\nPY\ncat > /app/solution/selection.json <<'JSON'\n{\"method\":\"fusion\",\"validation_ndcg_at_10\":0.053037}\nJSON\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.053037** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json` and `fusion.json` record the chosen model. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\nMD\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -5 /app/solution/refit_meta.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/rebuild.py <<'PY'\n\"\"\"Rebuild the selected submission from read-only task inputs.\"\"\"\nimport os,sys,json,subprocess,shutil,re\nfrom pathlib import Path\nO=Path(__file__).resolve().parent\nos.environ['OPENBLAS_NUM_THREADS']='2';os.environ['OMP_NUM_THREADS']='4'\ndef run(name,*args):subprocess.run([sys.executable,str(O/name),*map(str,args)],check=True,cwd=O)\ndef score_stage(stage,names):\n modes=sorted(set(re.match(r'(.+)time_',n)[1] for n in names))\n for mode in modes:\n if mode=='cat':run('make_neighbors.py',stage)\n else:run('neighbors_extra.py',stage,mode)\n for name in names:\n mode,k,kind=re.match(r'(.+)time_(\\d+)_(.+)',name).groups();run('time_user.py',stage,mode,k,kind)\ndef main():\n selection=json.load(open(O/'selection.json'));run('prepare.py');run('temporal.py')\n if selection['method']=='fusion':\n names=json.load(open(O/'fusion.json'))['names'];score_stage('test',names);run('fusion.py','test');src=O/'submission_fusion.parquet'\n else:\n from meta_extended import names,Data,HistGradientBoostingClassifier,np,pickle,top,save\n score_stage('test',names)\n if '--retrain' in sys.argv:\n run('build_past.py')\n samples=[]\n for stage in ['past','validation']:\n score_stage(stage,names);cache=O/f'{stage}_metax_data_cache.npz'\n if cache.exists():cache.unlink()\n samples.append(Data(stage).sample())\n model=HistGradientBoostingClassifier(max_iter=130,learning_rate=.07,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,max_bins=127,early_stopping=False,random_state=42)\n model.fit(np.concatenate([a[0] for a in samples]),np.concatenate([a[1] for a in samples]),sample_weight=np.concatenate([a[2] for a in samples]));pickle.dump(model,open(O/'metax_final_model.pkl','wb'))\n model=pickle.load(open(O/'metax_final_model.pkl','rb'));cache=O/'test_metax_data_cache.npz'\n if cache.exists():cache.unlink()\n d=Data('test');s=d.predict(model);np.save(O/'test_metax_15_pool_scores.npy',s);run('blend_meta.py','test');src=O/'submission_blended.parquet'\n shutil.copyfile(src,O/'submission.next.parquet');os.replace(O/'submission.next.parquet',O/'submission.parquet');run('verify.py')\nif __name__=='__main__':main()\nPY\ncat > /app/solution/selection.json <<'JSON'\n{\"method\":\"fusion\",\"validation_ndcg_at_10\":0.053037}\nJSON\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.053037** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json` and `fusion.json` record the chosen model. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\nMD\ntail -6 /app/solution/metax.log; tail -5 /app/solution/meta_cache.log; tail -5 /app/solution/refit_meta.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\nTerminated\n", "stderr": "", "aggregated_output": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\nTerminated\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5380 }, "formatted_output": "samples past 557600 11720\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\nTerminated\n" } } ## Step 68 · agent · 2026-09-23T05:46:35.815Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:46:41.664Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport glob,os\\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\\n try:\\n a=open(f,'rb').read().split(b'\\\\0')\\n if len(a)>1 and a[0].endswith(b'python') and (b'/app/solution/' in a[1] or a[1]==b'-'):\\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\\n except OSError:pass\\nPY\\nls -lh /app/solution/*metax* /app/solution/metax*; tail -6 /app/solution/metax.log; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:46:41.904Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"674e99","wall_time_seconds":0.025100251,"exit_code":0,"original_token_count":340,"output":"1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 166.05 state D\n1461 [b'-', b''] cpu_s 103.65 state D\n1506 [b'/app/solution/refit_meta.py', b''] cpu_s 1.87 state S\n1540 [b'-', b''] cpu_s 0.07 state R\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 155M Sep 23 05:45 /app/solution/validation_metax_data_cache.npz\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nlow 0\nhigh 0\nmax 21001\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 112\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367af3d8487d2b1bdc078e31cf00b", "usage": { "input_tokens": 74131, "cached_input_tokens": 72192, "cache_write_input_tokens": 0, "output_tokens": 501, "reasoning_output_tokens": 282, "total_tokens": 74632 }, "turn_token_usage": { "input_tokens": 2767121, "cached_input_tokens": 2697088, "cache_write_input_tokens": 0, "output_tokens": 40862, "reasoning_output_tokens": 17079, "total_tokens": 2807983 }, "thread_token_usage": { "input_tokens": 2767121, "cached_input_tokens": 2697088, "cache_write_input_tokens": 0, "output_tokens": 40862, "reasoning_output_tokens": 17079, "total_tokens": 2807983 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a2ad7565-be85-4fad-b231-951172fe5725", "process_id": "98316", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport glob,os\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(f,'rb').read().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and (b'/app/solution/' in a[1] or a[1]==b'-'):\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\n except OSError:pass\nPY\nls -lh /app/solution/*metax* /app/solution/metax*; tail -6 /app/solution/metax.log; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport glob,os\nfor f in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(f,'rb').read().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and (b'/app/solution/' in a[1] or a[1]==b'-'):\n st=open(f.replace('cmdline','stat')).read().split();print(f.split('/')[2],a[1:3], 'cpu_s', (int(st[13])+int(st[14]))/100,'state',st[2])\n except OSError:pass\nPY\nls -lh /app/solution/*metax* /app/solution/metax*; tail -6 /app/solution/metax.log; cat /sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 166.05 state D\n1461 [b'-', b''] cpu_s 103.65 state D\n1506 [b'/app/solution/refit_meta.py', b''] cpu_s 1.87 state S\n1540 [b'-', b''] cpu_s 0.07 state R\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 155M Sep 23 05:45 /app/solution/validation_metax_data_cache.npz\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nlow 0\nhigh 0\nmax 21001\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 112\n", "stderr": "", "aggregated_output": "1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 166.05 state D\n1461 [b'-', b''] cpu_s 103.65 state D\n1506 [b'/app/solution/refit_meta.py', b''] cpu_s 1.87 state S\n1540 [b'-', b''] cpu_s 0.07 state R\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 155M Sep 23 05:45 /app/solution/validation_metax_data_cache.npz\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nlow 0\nhigh 0\nmax 21001\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 112\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 25090561 }, "formatted_output": "1334 [b'/app/solution/meta_extended.py', b'train'] cpu_s 166.05 state D\n1461 [b'-', b''] cpu_s 103.65 state D\n1506 [b'/app/solution/refit_meta.py', b''] cpu_s 1.87 state S\n1540 [b'-', b''] cpu_s 0.07 state R\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 738 Sep 23 05:46 /app/solution/metax.log\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 114M Sep 23 05:44 /app/solution/metax_past_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 106M Sep 23 05:45 /app/solution/metax_val_samples.npz\n-rw-r--r-- 1 agent agent 155M Sep 23 05:45 /app/solution/validation_metax_data_cache.npz\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nlow 0\nhigh 0\nmax 21001\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 112\n" } } ## Step 69 · agent · 2026-09-23T05:46:56.578Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:46:59.095Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os,signal\\nos.kill(1461,signal.SIGSTOP)\\nprint('Paused test-cache construction while validation reranking finishes')\\nPY\\ntail -8 /app/solution/metax.log; tail -3 /app/solution/meta_cache.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:46:59.346Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"720108","wall_time_seconds":0.00000397,"exit_code":0,"original_token_count":209,"output":"Paused test-cache construction while validation reranking finishes\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n"} {"current_time":"2026-09-23 05:46:59 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367c22c5887d29cce74ff682d22a5", "usage": { "input_tokens": 75169, "cached_input_tokens": 73984, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 414, "total_tokens": 75682 }, "turn_token_usage": { "input_tokens": 2842290, "cached_input_tokens": 2771072, "cache_write_input_tokens": 0, "output_tokens": 41375, "reasoning_output_tokens": 17493, "total_tokens": 2883665 }, "thread_token_usage": { "input_tokens": 2842290, "cached_input_tokens": 2771072, "cache_write_input_tokens": 0, "output_tokens": 41375, "reasoning_output_tokens": 17493, "total_tokens": 2883665 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8b635be5-00e0-4618-a9d1-cd2b6d29c636", "process_id": "1170", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os,signal\nos.kill(1461,signal.SIGSTOP)\nprint('Paused test-cache construction while validation reranking finishes')\nPY\ntail -8 /app/solution/metax.log; tail -3 /app/solution/meta_cache.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os,signal\nos.kill(1461,signal.SIGSTOP)\nprint('Paused test-cache construction while validation reranking finishes')\nPY\ntail -8 /app/solution/metax.log; tail -3 /app/solution/meta_cache.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Paused test-cache construction while validation reranking finishes\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n", "stderr": "", "aggregated_output": "Paused test-cache construction while validation reranking finishes\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3970 }, "formatted_output": "Paused test-cache construction while validation reranking finishes\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nsamples validation 521754 10194\nvalidation samples ready\n" } } ## Step 70 · agent · 2026-09-23T05:47:12.418Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:47:18.904Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os\\nos.kill(1506,15)\\np='/app/solution/refit_meta.py';s=open(p).read().replace(\\\"['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']\\\",\\\"['metax_past_samples.npz','metax_val_samples.npz']\\\").replace(\\\"while 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\\\",\\\"\\\")\\ns=s.replace(\\\"print('final model fit',flush=True);d=Data('test');\\\", \\\"print('final model fit',flush=True)\\\\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\\\\nd=Data('test');\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:47:20.080Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c46ec3","wall_time_seconds":1.001432475,"session_id":29613,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367d3866487d2a4b3237ce5fbb36b", "usage": { "input_tokens": 75965, "cached_input_tokens": 75008, "cache_write_input_tokens": 0, "output_tokens": 556, "reasoning_output_tokens": 301, "total_tokens": 76521 }, "turn_token_usage": { "input_tokens": 2918255, "cached_input_tokens": 2846080, "cache_write_input_tokens": 0, "output_tokens": 41931, "reasoning_output_tokens": 17794, "total_tokens": 2960186 }, "thread_token_usage": { "input_tokens": 2918255, "cached_input_tokens": 2846080, "cache_write_input_tokens": 0, "output_tokens": 41931, "reasoning_output_tokens": 17794, "total_tokens": 2960186 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c9d3e799-ab43-4417-8b75-cafce6dcd41c", "process_id": "77159", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os,glob\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(p,'rb').read().split(b'\\0')\n if len(a)>1 and a[1]==b'/app/solution/refit_meta.py':os.kill(int(p.split('/')[2]),15)\n except OSError:pass\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os,glob\nfor p in glob.glob('/proc/[0-9]*/cmdline'):\n try:\n a=open(p,'rb').read().split(b'\\0')\n if len(a)>1 and a[1]==b'/app/solution/refit_meta.py':os.kill(int(p.split('/')[2]),15)\n except OSError:pass\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 108, "nanos": 415423703 }, "formatted_output": "" } } ## Step 71 · agent · 2026-09-23T05:47:37.917Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:47:39.208Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/metax.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/validation_metax_15_pool_scores.npy /app/solution/metax_config.json\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:47:39.451Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"6c59ca","wall_time_seconds":0.000002781,"exit_code":0,"original_token_count":223,"output":" np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nmeta 15 0.05248247249958812 313\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nTerminated\nsamples validation 521754 10194\nvalidation samples ready\n-rw-r--r-- 1 agent agent 44 Sep 23 05:47 /app/solution/metax_config.json\n-rw-r--r-- 1 agent agent 68M Sep 23 05:47 /app/solution/validation_metax_15_pool_scores.npy\n"} {"current_time":"2026-09-23 05:47:39 UTC"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367e84a2887d283d481dbb1dc39a2", "usage": { "input_tokens": 76579, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 568, "reasoning_output_tokens": 473, "total_tokens": 77147 }, "turn_token_usage": { "input_tokens": 2994834, "cached_input_tokens": 2921856, "cache_write_input_tokens": 0, "output_tokens": 42499, "reasoning_output_tokens": 18267, "total_tokens": 3037333 }, "thread_token_usage": { "input_tokens": 2994834, "cached_input_tokens": 2921856, "cache_write_input_tokens": 0, "output_tokens": 42499, "reasoning_output_tokens": 18267, "total_tokens": 3037333 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5512e62d-2e8f-44e9-b780-21f7adcede3b", "process_id": "98707", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/metax.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/validation_metax_15_pool_scores.npy /app/solution/metax_config.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/metax.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/validation_metax_15_pool_scores.npy /app/solution/metax_config.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nmeta 15 0.05248247249958812 313\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nTerminated\nsamples validation 521754 10194\nvalidation samples ready\n-rw-r--r-- 1 agent agent 44 Sep 23 05:47 /app/solution/metax_config.json\n-rw-r--r-- 1 agent agent 68M Sep 23 05:47 /app/solution/validation_metax_15_pool_scores.npy\n", "stderr": "", "aggregated_output": " np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nmeta 15 0.05248247249958812 313\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nTerminated\nsamples validation 521754 10194\nvalidation samples ready\n-rw-r--r-- 1 agent agent 44 Sep 23 05:47 /app/solution/metax_config.json\n-rw-r--r-- 1 agent agent 68M Sep 23 05:47 /app/solution/validation_metax_15_pool_scores.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2781 }, "formatted_output": " np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nmeta 15 0.05248247249958812 313\n/app/solution/meta_extended.py:23: RuntimeWarning: invalid value encountered in divide\n np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\nTerminated\nsamples validation 521754 10194\nvalidation samples ready\n-rw-r--r-- 1 agent agent 44 Sep 23 05:47 /app/solution/metax_config.json\n-rw-r--r-- 1 agent agent 68M Sep 23 05:47 /app/solution/validation_metax_15_pool_scores.npy\n" } } ## Step 72 · agent · 2026-09-23T05:47:43.744Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:47:46.555Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nimport os,signal\\nos.kill(1334,15);os.kill(1461,signal.SIGCONT)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py validation > /app/solution/blend_meta.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:47:47.736Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"451bfd","wall_time_seconds":1.000459302,"session_id":96898,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab367fba1c487d2a5c4a1264956cfe4", "usage": { "input_tokens": 77485, "cached_input_tokens": 76416, "cache_write_input_tokens": 0, "output_tokens": 185, "reasoning_output_tokens": 79, "total_tokens": 77670 }, "turn_token_usage": { "input_tokens": 3072319, "cached_input_tokens": 2998272, "cache_write_input_tokens": 0, "output_tokens": 42684, "reasoning_output_tokens": 18346, "total_tokens": 3115003 }, "thread_token_usage": { "input_tokens": 3072319, "cached_input_tokens": 2998272, "cache_write_input_tokens": 0, "output_tokens": 42684, "reasoning_output_tokens": 18346, "total_tokens": 3115003 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ce5ec173-2786-4b20-83af-6f041ee0b769", "process_id": "17668", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\"+['cattime_300_short']\", \"+['cattime_300_short','recenttime_500_d3','recenttime_500_short','freshtime_500_d3']\").replace(\"meta_\", \"metax_\");open('/app/solution/meta_extended.py','w').write(s)\nPY\npython - <<'PY'\nimport time\nfrom pathlib import Path\nwhile 'freshtime_500_d3' not in Path('/app/solution/past_extra.log').read_text() or 'recenttime_500_short' not in Path('/app/solution/state.log').read_text():time.sleep(3)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta_extended.py train > /app/solution/metax.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/meta.py';s=open(p).read();s=s.replace(\"+['cattime_300_short']\", \"+['cattime_300_short','recenttime_500_d3','recenttime_500_short','freshtime_500_d3']\").replace(\"meta_\", \"metax_\");open('/app/solution/meta_extended.py','w').write(s)\nPY\npython - <<'PY'\nimport time\nfrom pathlib import Path\nwhile 'freshtime_500_d3' not in Path('/app/solution/past_extra.log').read_text() or 'recenttime_500_short' not in Path('/app/solution/state.log').read_text():time.sleep(3)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/meta_extended.py train > /app/solution/metax.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 412, "nanos": 383257057 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f66e35d7-e9ec-4440-b81c-2a269097d66f", "process_id": "96898", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os,signal\nos.kill(1334,15);os.kill(1461,signal.SIGCONT)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py validation > /app/solution/blend_meta.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os,signal\nos.kill(1334,15);os.kill(1461,signal.SIGCONT)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py validation > /app/solution/blend_meta.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 18, "nanos": 156180025 }, "formatted_output": "" } } ## Step 73 · agent · 2026-09-23T05:48:05.612Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:48:09.029Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/verify.py';s=open(p).read().replace(\\\"pd.read_parquet(p/'submission.parquet')\\\", \\\"pd.read_parquet(p/(sys.argv[1] if len(sys.argv)>1 else 'submission.parquet'))\\\");open(p,'w').write(s)\\nPY\\ntail -8 /app/solution/blend_meta.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/metax_final_model.pkl /app/solution/test_metax_data_cache.npz\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:48:09.108Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"03f73a","wall_time_seconds":0.000006051,"exit_code":0,"original_token_count":131,"output":"best (np.float64(0.05303681918746273), 0, 0.5)\nbest (np.float64(0.053461051544088395), 0.1, 0.5)\nbest (np.float64(0.054512693998842725), 0.25, 0.5)\nbest (np.float64(0.055056424798221155), 0.5, 0.5)\n{'score': np.float64(0.055056424798221155), 'weight': 0.5, 'power': 0.5}\nfinal model fit\nsamples validation 521754 10194\nvalidation samples ready\ntest cache ready\n-rw-r--r-- 1 agent agent 280K Sep 23 05:47 /app/solution/metax_final_model.pkl\n-rw-r--r-- 1 agent agent 155M Sep 23 05:48 /app/solution/test_metax_data_cache.npz\n"} {"current_time":"2026-09-23 05:48:09 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17793615-fd0a-4227-a62d-9c2c899214e1", "process_id": "13230", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/verify.py';s=open(p).read().replace(\"pd.read_parquet(p/'submission.parquet')\", \"pd.read_parquet(p/(sys.argv[1] if len(sys.argv)>1 else 'submission.parquet'))\");open(p,'w').write(s)\nPY\ntail -8 /app/solution/blend_meta.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/metax_final_model.pkl /app/solution/test_metax_data_cache.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/verify.py';s=open(p).read().replace(\"pd.read_parquet(p/'submission.parquet')\", \"pd.read_parquet(p/(sys.argv[1] if len(sys.argv)>1 else 'submission.parquet'))\");open(p,'w').write(s)\nPY\ntail -8 /app/solution/blend_meta.log; tail -4 /app/solution/refit_meta.log; tail -4 /app/solution/meta_cache.log; ls -lh /app/solution/metax_final_model.pkl /app/solution/test_metax_data_cache.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "best (np.float64(0.05303681918746273), 0, 0.5)\nbest (np.float64(0.053461051544088395), 0.1, 0.5)\nbest (np.float64(0.054512693998842725), 0.25, 0.5)\nbest (np.float64(0.055056424798221155), 0.5, 0.5)\n{'score': np.float64(0.055056424798221155), 'weight': 0.5, 'power': 0.5}\nfinal model fit\nsamples validation 521754 10194\nvalidation samples ready\ntest cache ready\n-rw-r--r-- 1 agent agent 280K Sep 23 05:47 /app/solution/metax_final_model.pkl\n-rw-r--r-- 1 agent agent 155M Sep 23 05:48 /app/solution/test_metax_data_cache.npz\n", "stderr": "", "aggregated_output": "best (np.float64(0.05303681918746273), 0, 0.5)\nbest (np.float64(0.053461051544088395), 0.1, 0.5)\nbest (np.float64(0.054512693998842725), 0.25, 0.5)\nbest (np.float64(0.055056424798221155), 0.5, 0.5)\n{'score': np.float64(0.055056424798221155), 'weight': 0.5, 'power': 0.5}\nfinal model fit\nsamples validation 521754 10194\nvalidation samples ready\ntest cache ready\n-rw-r--r-- 1 agent agent 280K Sep 23 05:47 /app/solution/metax_final_model.pkl\n-rw-r--r-- 1 agent agent 155M Sep 23 05:48 /app/solution/test_metax_data_cache.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6051 }, "formatted_output": "best (np.float64(0.05303681918746273), 0, 0.5)\nbest (np.float64(0.053461051544088395), 0.1, 0.5)\nbest (np.float64(0.054512693998842725), 0.25, 0.5)\nbest (np.float64(0.055056424798221155), 0.5, 0.5)\n{'score': np.float64(0.055056424798221155), 'weight': 0.5, 'power': 0.5}\nfinal model fit\nsamples validation 521754 10194\nvalidation samples ready\ntest cache ready\n-rw-r--r-- 1 agent agent 280K Sep 23 05:47 /app/solution/metax_final_model.pkl\n-rw-r--r-- 1 agent agent 155M Sep 23 05:48 /app/solution/test_metax_data_cache.npz\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36803fd9c87d2b00004e76e5e1654", "usage": { "input_tokens": 77726, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 641, "reasoning_output_tokens": 474, "total_tokens": 78367 }, "turn_token_usage": { "input_tokens": 3150045, "cached_input_tokens": 3075584, "cache_write_input_tokens": 0, "output_tokens": 43325, "reasoning_output_tokens": 18820, "total_tokens": 3193370 }, "thread_token_usage": { "input_tokens": 3150045, "cached_input_tokens": 3075584, "cache_write_input_tokens": 0, "output_tokens": 43325, "reasoning_output_tokens": 18820, "total_tokens": 3193370 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-231d0d66-ad04-4d09-b83f-b563414fc666", "process_id": "63729", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os\np='/proc/1125/cmdline'\ntry:\n if b'/app/solution/meta.py' in open(p,'rb').read():os.kill(1125,15);print('Stopped weaker reranker experiment after its first result')\nexcept OSError:pass\np='/app/solution/meta_extended.py';s=open(p).read();needle=\"self.pops={};self.uc=[]\";s=s.replace(needle,needle+\"\\n cache=O/f'{stage}_metax_data_cache.npz'\\n if cache.exists():\\n z=np.load(cache,allow_pickle=True);self.pops=z['pops'].item();self.uc=z['uc'];self.pool=z['pool'];self.valid=z['valid'];self.maxs=z['maxs'];return\")\nneedle=\" def features(self,r,c):\";s=s.replace(needle,\" np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\\n\"+needle);open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY' > /app/solution/meta_cache.log 2>&1\nimport sys\nsys.path.insert(0,'/app/solution')\nfrom meta_extended import *\nv=Data('validation');x,y,w=v.sample();np.savez(O/'metax_val_samples.npz',x=x,y=y,w=w);print('validation samples ready',flush=True)\nd=Data('test');print('test cache ready',flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os\np='/proc/1125/cmdline'\ntry:\n if b'/app/solution/meta.py' in open(p,'rb').read():os.kill(1125,15);print('Stopped weaker reranker experiment after its first result')\nexcept OSError:pass\np='/app/solution/meta_extended.py';s=open(p).read();needle=\"self.pops={};self.uc=[]\";s=s.replace(needle,needle+\"\\n cache=O/f'{stage}_metax_data_cache.npz'\\n if cache.exists():\\n z=np.load(cache,allow_pickle=True);self.pops=z['pops'].item();self.uc=z['uc'];self.pool=z['pool'];self.valid=z['valid'];self.maxs=z['maxs'];return\")\nneedle=\" def features(self,r,c):\";s=s.replace(needle,\" np.savez(cache,pops=self.pops,uc=self.uc,pool=self.pool,valid=self.valid,maxs=self.maxs)\\n\"+needle);open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python - <<'PY' > /app/solution/meta_cache.log 2>&1\nimport sys\nsys.path.insert(0,'/app/solution')\nfrom meta_extended import *\nv=Data('validation');x,y,w=v.sample();np.savez(O/'metax_val_samples.npz',x=x,y=y,w=w);print('validation samples ready',flush=True)\nd=Data('test');print('test cache ready',flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 250, "nanos": 285724733 }, "formatted_output": "" } } ## Step 74 · agent · 2026-09-23T05:48:22.042Z ### Thinking unavailable — no text recorded Blending the learned reranker with the collaborative ensemble raised validation NDCG@10 to 0.055056. The reranker has been refitted using the allowed validation history; I’m generating its final rankings and checking the combined artifact. ### exec · 2026-09-23T05:48:38.784Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_final.parquet > /app/solution/public_score_final.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\nimport time\\nfrom pathlib import Path\\nwhile 'final meta done' not in Path('/app/solution/refit_meta.log').read_text():time.sleep(2)\\nPY\\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py test > /app/solution/blend_meta_test.log 2>&1\\n/opt/task-venv/bin/python /app/solution/verify.py submission_blended.parquet\\npython - <<'PY'\\nimport shutil,os,json\\nfrom pathlib import Path\\np=Path('/app/solution');shutil.copyfile(p/'submission_blended.parquet',p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');json.dump({'method':'meta_blend','validation_ndcg_at_10':0.055056,'leaves':15},open(p/'selection.json','w'));print('Selected final blended submission')\\ns=(p/'README.md').read_text().replace('The selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends.', 'The selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes.').replace('**0.053037**','**0.055056**').replace('`selection.json` and `fusion.json` record the chosen model.', '`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it.').replace('`public_score.json` records', '`public_score_final.json` records');(p/'README.md').write_text(s)\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" } ### Tool output · 2026-09-23T05:48:41.125Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"345d28","wall_time_seconds":1.001305975,"session_id":20146,"original_token_count":0,"output":""} {"chunk_id":"05fc14","wall_time_seconds":1.00121357,"session_id":43627,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36819573c87d2b41f4cc7919e6ae7", "usage": { "input_tokens": 78660, "cached_input_tokens": 77568, "cache_write_input_tokens": 0, "output_tokens": 905, "reasoning_output_tokens": 331, "total_tokens": 79565 }, "turn_token_usage": { "input_tokens": 3228705, "cached_input_tokens": 3153152, "cache_write_input_tokens": 0, "output_tokens": 44230, "reasoning_output_tokens": 19151, "total_tokens": 3272935 }, "thread_token_usage": { "input_tokens": 3228705, "cached_input_tokens": 3153152, "cache_write_input_tokens": 0, "output_tokens": 44230, "reasoning_output_tokens": 19151, "total_tokens": 3272935 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a9a8b90e-de8c-4698-9ba8-0a1f4f4ba69f", "process_id": "29613", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport os\nos.kill(1506,15)\np='/app/solution/refit_meta.py';s=open(p).read().replace(\"['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']\",\"['metax_past_samples.npz','metax_val_samples.npz']\").replace(\"while 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\",\"\")\ns=s.replace(\"print('final model fit',flush=True);d=Data('test');\", \"print('final model fit',flush=True)\\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\\nd=Data('test');\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport os\nos.kill(1506,15)\np='/app/solution/refit_meta.py';s=open(p).read().replace(\"['metax_past_samples.npz','metax_val_samples.npz','test_metax_data_cache.npz']\",\"['metax_past_samples.npz','metax_val_samples.npz']\").replace(\"while 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\",\"\")\ns=s.replace(\"print('final model fit',flush=True);d=Data('test');\", \"print('final model fit',flush=True)\\nwhile 'test cache ready' not in (O/'meta_cache.log').read_text():time.sleep(2)\\nd=Data('test');\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/refit_meta.py > /app/solution/refit_meta.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 81, "nanos": 37519824 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a4e428c1-a137-4fd1-bba7-aff17b23727a", "process_id": "20146", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_final.parquet > /app/solution/public_score_final.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_final.parquet > /app/solution/public_score_final.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 3, "nanos": 94990087 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9fedb741-3c7b-4522-bd46-bb39f149fc48", "process_id": "43627", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport time\nfrom pathlib import Path\nwhile 'final meta done' not in Path('/app/solution/refit_meta.log').read_text():time.sleep(2)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py test > /app/solution/blend_meta_test.log 2>&1\n/opt/task-venv/bin/python /app/solution/verify.py submission_blended.parquet\npython - <<'PY'\nimport shutil,os,json\nfrom pathlib import Path\np=Path('/app/solution');shutil.copyfile(p/'submission_blended.parquet',p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');json.dump({'method':'meta_blend','validation_ndcg_at_10':0.055056,'leaves':15},open(p/'selection.json','w'));print('Selected final blended submission')\ns=(p/'README.md').read_text().replace('The selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends.', 'The selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes.').replace('**0.053037**','**0.055056**').replace('`selection.json` and `fusion.json` record the chosen model.', '`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it.').replace('`public_score.json` records', '`public_score_final.json` records');(p/'README.md').write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport time\nfrom pathlib import Path\nwhile 'final meta done' not in Path('/app/solution/refit_meta.log').read_text():time.sleep(2)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/blend_meta.py test > /app/solution/blend_meta_test.log 2>&1\n/opt/task-venv/bin/python /app/solution/verify.py submission_blended.parquet\npython - <<'PY'\nimport shutil,os,json\nfrom pathlib import Path\np=Path('/app/solution');shutil.copyfile(p/'submission_blended.parquet',p/'submission.next.parquet');os.replace(p/'submission.next.parquet',p/'submission.parquet');json.dump({'method':'meta_blend','validation_ndcg_at_10':0.055056,'leaves':15},open(p/'selection.json','w'));print('Selected final blended submission')\ns=(p/'README.md').read_text().replace('The selected model combines cosine user neighborhoods built from long-term and recent listening preferences, three-day exponentially decayed neighbor activity, and recent popularity trends.', 'The selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes.').replace('**0.053037**','**0.055056**').replace('`selection.json` and `fusion.json` record the chosen model.', '`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it.').replace('`public_score.json` records', '`public_score_final.json` records');(p/'README.md').write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\nSelected final blended submission\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\nSelected final blended submission\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 191783592 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\nSelected final blended submission\n" } } ## Step 75 · agent · 2026-09-23T05:48:50.347Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:48:55.563Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/prepare.py';s=open(p).read().replace(\\\"'submission.parquet' if stage=='test'\\\", \\\"'submission_baseline.parquet' if stage=='test'\\\");s += \\\"\\\\nif not (O/'submission.parquet').exists():\\\\n import shutil\\\\n shutil.copyfile(O/'submission_baseline.parquet',O/'submission.parquet')\\\\n\\\";open(p,'w').write(s)\\nPY\\ntail -4 /app/solution/refit_meta.log; tail -4 /app/solution/blend_meta_test.log; cat /app/solution/public_score_final.json; cat /app/solution/selection.json\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" } ### Tool output · 2026-09-23T05:48:55.654Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"e8419a","wall_time_seconds":0.00000316,"exit_code":0,"original_token_count":75,"output":"final model fit\nfinal meta done\n{'score': 0.055056424798221155, 'weight': 0.5, 'power': 0.5}\n{\n \"model\": \"/app/solution/validation_final.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055056,\n \"quality_users\": 4263\n }\n}\n{\"method\": \"meta_blend\", \"validation_ndcg_at_10\": 0.055056, \"leaves\": 15}"} {"current_time":"2026-09-23 05:48:55 UTC"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7d2cb6d8-8b0c-480b-a9ff-d4d0cd159323", "process_id": "50139", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/prepare.py';s=open(p).read().replace(\"'submission.parquet' if stage=='test'\", \"'submission_baseline.parquet' if stage=='test'\");s += \"\\nif not (O/'submission.parquet').exists():\\n import shutil\\n shutil.copyfile(O/'submission_baseline.parquet',O/'submission.parquet')\\n\";open(p,'w').write(s)\nPY\ntail -4 /app/solution/refit_meta.log; tail -4 /app/solution/blend_meta_test.log; cat /app/solution/public_score_final.json; cat /app/solution/selection.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/prepare.py';s=open(p).read().replace(\"'submission.parquet' if stage=='test'\", \"'submission_baseline.parquet' if stage=='test'\");s += \"\\nif not (O/'submission.parquet').exists():\\n import shutil\\n shutil.copyfile(O/'submission_baseline.parquet',O/'submission.parquet')\\n\";open(p,'w').write(s)\nPY\ntail -4 /app/solution/refit_meta.log; tail -4 /app/solution/blend_meta_test.log; cat /app/solution/public_score_final.json; cat /app/solution/selection.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final model fit\nfinal meta done\n{'score': 0.055056424798221155, 'weight': 0.5, 'power': 0.5}\n{\n \"model\": \"/app/solution/validation_final.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055056,\n \"quality_users\": 4263\n }\n}\n{\"method\": \"meta_blend\", \"validation_ndcg_at_10\": 0.055056, \"leaves\": 15}", "stderr": "", "aggregated_output": "final model fit\nfinal meta done\n{'score': 0.055056424798221155, 'weight': 0.5, 'power': 0.5}\n{\n \"model\": \"/app/solution/validation_final.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055056,\n \"quality_users\": 4263\n }\n}\n{\"method\": \"meta_blend\", \"validation_ndcg_at_10\": 0.055056, \"leaves\": 15}", "exit_code": 0, "duration": { "secs": 0, "nanos": 3160 }, "formatted_output": "final model fit\nfinal meta done\n{'score': 0.055056424798221155, 'weight': 0.5, 'power': 0.5}\n{\n \"model\": \"/app/solution/validation_final.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055056,\n \"quality_users\": 4263\n }\n}\n{\"method\": \"meta_blend\", \"validation_ndcg_at_10\": 0.055056, \"leaves\": 15}" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3683950ac87d29bfbefcebd2ba049", "usage": { "input_tokens": 79656, "cached_input_tokens": 78464, "cache_write_input_tokens": 0, "output_tokens": 392, "reasoning_output_tokens": 209, "total_tokens": 80048 }, "turn_token_usage": { "input_tokens": 3308361, "cached_input_tokens": 3231616, "cache_write_input_tokens": 0, "output_tokens": 44622, "reasoning_output_tokens": 19360, "total_tokens": 3352983 }, "thread_token_usage": { "input_tokens": 3308361, "cached_input_tokens": 3231616, "cache_write_input_tokens": 0, "output_tokens": 44622, "reasoning_output_tokens": 19360, "total_tokens": 3352983 } } ## Step 76 · agent · 2026-09-23T05:48:58.053Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:49:02.152Z { "code": "text(await tools.write_stdin({session_id:43627,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/verify.py\\n/opt/task-venv/bin/python -m py_compile /app/solution/rebuild.py /app/solution/common.py /app/solution/prepare.py /app/solution/temporal.py /app/solution/meta_extended.py /app/solution/blend_meta.py\\ncat /app/solution/README.md\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n" } ### Tool output · 2026-09-23T05:49:03.330Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"eeb9dc","wall_time_seconds":0.00000272,"exit_code":0,"original_token_count":31,"output":"VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\nSelected final blended submission\n"} {"chunk_id":"e2fb00","wall_time_seconds":1.000963291,"session_id":28052,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab36847e7d887d29ce7143bd7f3a85b", "usage": { "input_tokens": 80242, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 173, "reasoning_output_tokens": 28, "total_tokens": 80415 }, "turn_token_usage": { "input_tokens": 3388603, "cached_input_tokens": 3311104, "cache_write_input_tokens": 0, "output_tokens": 44795, "reasoning_output_tokens": 19388, "total_tokens": 3433398 }, "thread_token_usage": { "input_tokens": 3388603, "cached_input_tokens": 3311104, "cache_write_input_tokens": 0, "output_tokens": 44795, "reasoning_output_tokens": 19388, "total_tokens": 3433398 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-df716be2-20ba-41bf-a15b-aee799be8ff2", "process_id": "28052", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/verify.py\n/opt/task-venv/bin/python -m py_compile /app/solution/rebuild.py /app/solution/common.py /app/solution/prepare.py /app/solution/temporal.py /app/solution/meta_extended.py /app/solution/blend_meta.py\ncat /app/solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/verify.py\n/opt/task-venv/bin/python -m py_compile /app/solution/rebuild.py /app/solution/common.py /app/solution/prepare.py /app/solution/temporal.py /app/solution/meta_extended.py /app/solution/blend_meta.py\ncat /app/solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 288457162 }, "formatted_output": "VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n" } } ## Step 77 · agent · 2026-09-23T05:49:26.021Z ### Thinking unavailable — no text recorded ## Step 78 · agent · 2026-09-23T05:49:28.961Z ### Thinking unavailable — no text recorded The final submission is saved and passes all checks: 845,600 rows, exactly 100 distinct eligible tracks per target user, and ranks 1–100. Public validation NDCG@10 is 0.055056. I’m removing disposable experiment caches while retaining the rebuild code, fitted reranker, configuration, and validation evidence. ### exec · 2026-09-23T05:49:32.150Z { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution');removed=0\\nfor f in p.iterdir():\\n if f.suffix in ['.npy','.npz'] and f.name!='ids.npz':removed+=f.stat().st_size;f.unlink()\\n elif f.suffix=='.parquet' and f.name not in ['submission.parquet','validation_final.parquet','validation_fusion.parquet']:removed+=f.stat().st_size;f.unlink()\\nprint('Removed',round(removed/1024**3,2),'GiB of disposable intermediate artifacts')\\nPY\\n/opt/task-venv/bin/python /app/solution/verify.py\\ndu -sh /app/solution\\ncat /app/solution/README.md\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" } ### Tool output · 2026-09-23T05:49:33.329Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"4640aa","wall_time_seconds":1.001088839,"session_id":57145,"original_token_count":14,"output":"Removed 25.97 GiB of disposable intermediate artifacts\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3684f7f4887d2a9fabfbb3a034eda", "usage": { "input_tokens": 80540, "cached_input_tokens": 80000, "cache_write_input_tokens": 0, "output_tokens": 871, "reasoning_output_tokens": 593, "total_tokens": 81411 }, "turn_token_usage": { "input_tokens": 3469143, "cached_input_tokens": 3391104, "cache_write_input_tokens": 0, "output_tokens": 45666, "reasoning_output_tokens": 19981, "total_tokens": 3514809 }, "thread_token_usage": { "input_tokens": 3469143, "cached_input_tokens": 3391104, "cache_write_input_tokens": 0, "output_tokens": 45666, "reasoning_output_tokens": 19981, "total_tokens": 3514809 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2966c43b-39d4-4cf4-a4d3-178a38a6b45d", "process_id": "57145", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution');removed=0\nfor f in p.iterdir():\n if f.suffix in ['.npy','.npz'] and f.name!='ids.npz':removed+=f.stat().st_size;f.unlink()\n elif f.suffix=='.parquet' and f.name not in ['submission.parquet','validation_final.parquet','validation_fusion.parquet']:removed+=f.stat().st_size;f.unlink()\nprint('Removed',round(removed/1024**3,2),'GiB of disposable intermediate artifacts')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py\ndu -sh /app/solution\ncat /app/solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution');removed=0\nfor f in p.iterdir():\n if f.suffix in ['.npy','.npz'] and f.name!='ids.npz':removed+=f.stat().st_size;f.unlink()\n elif f.suffix=='.parquet' and f.name not in ['submission.parquet','validation_final.parquet','validation_fusion.parquet']:removed+=f.stat().st_size;f.unlink()\nprint('Removed',round(removed/1024**3,2),'GiB of disposable intermediate artifacts')\nPY\n/opt/task-venv/bin/python /app/solution/verify.py\ndu -sh /app/solution\ncat /app/solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Removed 25.97 GiB of disposable intermediate artifacts\nVALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n6.1M\t/app/solution\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n", "stderr": "", "aggregated_output": "Removed 25.97 GiB of disposable intermediate artifacts\nVALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n6.1M\t/app/solution\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 642197457 }, "formatted_output": "Removed 25.97 GiB of disposable intermediate artifacts\nVALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n6.1M\t/app/solution\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n" } } ## Step 79 · agent · 2026-09-23T05:49:46.511Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:49:47.491Z { "code": "text(await tools.write_stdin({session_id:57145,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:28052,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" } ### Tool output · 2026-09-23T05:49:47.583Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"7dff03","wall_time_seconds":0.00000253,"exit_code":0,"original_token_count":444,"output":"VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n6.1M\t/app/solution\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n"} {"chunk_id":"501a32","wall_time_seconds":9.6e-7,"exit_code":0,"original_token_count":439,"output":"VALID: 845,600 rows; 8,456 users; 100 distinct eligible items per user; ranks 1–100.\n# Music discovery ranker\n\n`submission.parquet` contains 100 distinct, test-eligible tracks for each of 8,456 target users (845,600 rows).\n\nThe selected model blends cosine user neighborhoods, three-day exponentially decayed neighbor activity, recent popularity trends, and a gradient-boosted reranker. The reranker learns from an earlier seven-day holdout inside training history; model selection uses the untouched public validation window. For final fitting, its supervised examples include that earlier holdout and the observed validation outcomes. Organic completion and explicit likes contribute to the user profiles. All recommendations are filtered through the supplied cutoff-specific eligibility masks.\n\nPublic validation NDCG@10: **0.055056** over 4,263 users with eligible relevance. The recent-popularity baseline scored 0.025879. Validation features use only training history; the final user profiles and activity statistics are refitted with both training and validation events.\n\nRebuild with the installed environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`selection.json`, `fusion.json`, and `blend_meta.json` record the chosen model. The default rebuild uses the saved fitted reranker; add `--retrain` to regenerate its supervised training examples and refit it. `verify.py` checks the final schema, exact user coverage, ranks, uniqueness, and test eligibility. `public_score_final.json` records the public evaluator output. Other scripts and logs preserve the experiments, including an auxiliary earlier temporal holdout for supervised reranking. No packages, external data, network services, or hidden outcomes were used.\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3686d921c87d2b861cd471cecebc3", "usage": { "input_tokens": 81479, "cached_input_tokens": 80384, "cache_write_input_tokens": 0, "output_tokens": 256, "reasoning_output_tokens": 184, "total_tokens": 81735 }, "turn_token_usage": { "input_tokens": 3550622, "cached_input_tokens": 3471488, "cache_write_input_tokens": 0, "output_tokens": 45922, "reasoning_output_tokens": 20165, "total_tokens": 3596544 }, "thread_token_usage": { "input_tokens": 3550622, "cached_input_tokens": 3471488, "cache_write_input_tokens": 0, "output_tokens": 45922, "reasoning_output_tokens": 20165, "total_tokens": 3596544 } } ## Step 80 · agent · 2026-09-23T05:50:01.801Z ### Thinking unavailable — no text recorded Created [submission.parquet](/app/solution/submission.parquet) with 100 eligible tracks for all 8,456 users. All structural and eligibility checks pass. Public validation NDCG@10: **0.055056**. [Rebuild code](/app/solution/rebuild.py) and [README](/app/solution/README.md) are included. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "session_id": "01a0ccb5-811d-73f0-bd28-be1ef715258e", "root_turn_id": "01a0ccb5-812c-7692-8841-7ac43b8af359", "response_id": "resp_09167faa8fc8eeb0016ab3687be08487d29c07c5366782785e", "usage": { "input_tokens": 82577, "cached_input_tokens": 81280, "cache_write_input_tokens": 0, "output_tokens": 413, "reasoning_output_tokens": 329, "total_tokens": 82990 }, "turn_token_usage": { "input_tokens": 3633199, "cached_input_tokens": 3552768, "cache_write_input_tokens": 0, "output_tokens": 46335, "reasoning_output_tokens": 20494, "total_tokens": 3679534 }, "thread_token_usage": { "input_tokens": 3633199, "cached_input_tokens": 3552768, "cache_write_input_tokens": 0, "output_tokens": 46335, "reasoning_output_tokens": 20494, "total_tokens": 3679534 } }