yambda-discovery-ranking-v01 / base-sol-high-cli-0-153-4-r05-recovery-20260923T212844Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-23T21:40:07.847Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. 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. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # 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. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. 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 put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### 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. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. 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. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - 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 `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-23T21:40:09.512Z ## 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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$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. ## Step 3 · developer · 2026-09-23T21:40:09.512Z 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. 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 · 2026-09-23T21:40:09.512Z 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 · 2026-09-23T21:40:09.512Z 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": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "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-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "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-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-23T21:40:09.530Z # 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 · 2026-09-23T21:40:12.391Z ### Thinking unavailable — no text recorded I’ll first profile the event volumes, catalog/eligibility shapes, and baseline signal strength. Then I’ll build a leakage-safe validation pipeline, benchmark several ranking blends on the public evaluator, and preserve the best valid artifact before refitting on train+validation. ### exec · 2026-09-23T21:40:17.362Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:40:17.429Z Script completed Wall time 0.1 seconds Output: total 340M -r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 23 21:34 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 23 21:34 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet -r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet -r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 23 21:34 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 23 21:34 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 23 21:34 validation_preference_state.parquet """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. Python itself # is pinned by task.toml and both Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 idna==3.18 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44739b88487d29ae3066db900a1f8", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 186, "reasoning_output_tokens": 26, "total_tokens": 14523 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 186, "reasoning_output_tokens": 26, "total_tokens": 14523 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 186, "reasoning_output_tokens": 26, "total_tokens": 14523 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cffd280c-16df-4e8f-906a-991a284564c5", "process_id": "25076", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,200p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,160p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# 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", "stderr": "", "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3290 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:34 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# 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" } } ## Step 8 · agent · 2026-09-23T21:40:21.337Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:40:31.821Z { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\\nD='/task/data'\\nfor f in os.listdir(D):\\n if f.endswith('.parquet'):\\n p=pq.ParquetFile(D+'/'+f)\\n print(f, p.metadata.num_rows, p.schema_arrow)\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D+'/'+f)\\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n print('eligible count',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).mean(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).max())\\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:40:31.879Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pyarrow' ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-619455e7-932a-441a-9403-3ba6d42adda5", "process_id": "10829", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\nD='/task/data'\nfor f in os.listdir(D):\n if f.endswith('.parquet'):\n p=pq.ParquetFile(D+'/'+f)\n print(f, p.metadata.num_rows, p.schema_arrow)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('eligible count',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).mean(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).max())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\nD='/task/data'\nfor f in os.listdir(D):\n if f.endswith('.parquet'):\n p=pq.ParquetFile(D+'/'+f)\n print(f, p.metadata.num_rows, p.schema_arrow)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('eligible count',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).mean(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).max())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3840 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44741bf1887d2a061806800fbedcd", "usage": { "input_tokens": 18739, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 396, "reasoning_output_tokens": 59, "total_tokens": 19135 }, "turn_token_usage": { "input_tokens": 33076, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 582, "reasoning_output_tokens": 85, "total_tokens": 33658 }, "thread_token_usage": { "input_tokens": 33076, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 582, "reasoning_output_tokens": 85, "total_tokens": 33658 } } ## Step 9 · agent · 2026-09-23T21:40:36.072Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:40:40.166Z { "code": "const r = await tools.exec_command({\"cmd\":\"which python; python -V; python - <<'PY'\\nimport pandas, numpy, scipy, sklearn\\nprint(pandas.__version__,numpy.__version__,scipy.__version__,sklearn.__version__)\\ntry: import fastparquet; print('fastparquet yes')\\nexcept Exception as e: print(e)\\nPY\\nfind / -name 'pyarrow*' -type d 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:40:40.397Z Script completed Wall time 0.2 seconds Output: /usr/local/bin/python Python 3.12.13 Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' /opt/task-venv/lib/python3.12/site-packages/pyarrow-18.1.0.dist-info /opt/task-venv/lib/python3.12/site-packages/pyarrow ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4475175d887d29e4aad16b83a2da0", "usage": { "input_tokens": 19191, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 29, "total_tokens": 19358 }, "turn_token_usage": { "input_tokens": 52267, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 749, "reasoning_output_tokens": 114, "total_tokens": 53016 }, "thread_token_usage": { "input_tokens": 52267, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 749, "reasoning_output_tokens": 114, "total_tokens": 53016 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21c97147-a87f-454e-a10a-12603a8a3511", "process_id": "70844", "command": [ "/bin/sh", "-lc", "which python; python -V; python - <<'PY'\nimport pandas, numpy, scipy, sklearn\nprint(pandas.__version__,numpy.__version__,scipy.__version__,sklearn.__version__)\ntry: import fastparquet; print('fastparquet yes')\nexcept Exception as e: print(e)\nPY\nfind / -name 'pyarrow*' -type d 2>/dev/null | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; python -V; python - <<'PY'\nimport pandas, numpy, scipy, sklearn\nprint(pandas.__version__,numpy.__version__,scipy.__version__,sklearn.__version__)\ntry: import fastparquet; print('fastparquet yes')\nexcept Exception as e: print(e)\nPY\nfind / -name 'pyarrow*' -type d 2>/dev/null | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/pyarrow-18.1.0.dist-info\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow-18.1.0.dist-info\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 63288404 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow-18.1.0.dist-info\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n" } } ## Step 10 · agent · 2026-09-23T21:40:42.303Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:40:52.148Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\\nD='/task/data'\\nfor f in sorted(os.listdir(D)):\\n if f.endswith('.parquet'):\\n p=pq.ParquetFile(D+'/'+f)\\n print(f, p.metadata.num_rows, list(zip(p.schema_arrow.names,[str(x.type) for x in p.schema_arrow])))\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D+'/'+f)\\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1)\\n print('eligible count',cnt.min(),cnt.mean(),cnt.max())\\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\\nprint('catalog head/tail\\\\n',pd.concat([pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail()]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:40:53.701Z Script completed Wall time 1.5 seconds Output: candidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')] target_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')] test_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] train_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] train_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] val_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] validation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible count 24657 29067.306291390727 30000 test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible count 24552 29041.3178807947 30000 targets uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 8456 catalog head/tail item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 29995 1275692 163 81 29996 29996 1280011 163 76 29997 29997 1309246 163 138 29998 29998 1634958 163 46 29999 29999 1670524 163 73 30000 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44758c8a487d2a0634a8d720a1221", "usage": { "input_tokens": 19471, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 331, "reasoning_output_tokens": 14, "total_tokens": 19802 }, "turn_token_usage": { "input_tokens": 71738, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 1080, "reasoning_output_tokens": 128, "total_tokens": 72818 }, "thread_token_usage": { "input_tokens": 71738, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 1080, "reasoning_output_tokens": 128, "total_tokens": 72818 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-61d5d62e-0ca7-4fb9-b43b-941625ff0291", "process_id": "8107", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n p=pq.ParquetFile(D+'/'+f)\n print(f, p.metadata.num_rows, list(zip(p.schema_arrow.names,[str(x.type) for x in p.schema_arrow])))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f)\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1)\n print('eligible count',cnt.min(),cnt.mean(),cnt.max())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\nprint('catalog head/tail\\n',pd.concat([pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail()]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n p=pq.ParquetFile(D+'/'+f)\n print(f, p.metadata.num_rows, list(zip(p.schema_arrow.names,[str(x.type) for x in p.schema_arrow])))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f)\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1)\n print('eligible count',cnt.min(),cnt.mean(),cnt.max())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').head(),len(pd.read_parquet(D+'/target_users.parquet')))\nprint('catalog head/tail\\n',pd.concat([pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail()]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24552 29041.3178807947 30000\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 8456\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24552 29041.3178807947 30000\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 8456\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 385480722 }, "formatted_output": "candidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible count 24552 29041.3178807947 30000\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 8456\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\n" } } ## Step 11 · agent · 2026-09-23T21:40:58.986Z ### Thinking unavailable — no text recorded The task has 8,456 target users and 30,000 catalog tracks; each user retains roughly 29,000 eligible candidates, so retrieval does little of the ranking work. I’m focusing on personalized collaborative signals, recency, organic-vs-recommended behavior, and explicit-state exclusions/boosts rather than a catalog-popularity-only solution. ### exec · 2026-09-23T21:41:07.451Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nprint('rowgroups',p.num_row_groups)\\nfor i in [0,1,p.num_row_groups//2,p.num_row_groups-1]:\\n rg=p.metadata.row_group(i)\\n vals=[]\\n for c in range(rg.num_columns):\\n cc=rg.column(c)\\n vals.append((cc.path_in_schema,cc.statistics.min if cc.statistics else None,cc.statistics.max if cc.statistics else None))\\n print(i,rg.num_rows,vals)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:41:07.838Z Script completed Wall time 0.4 seconds Output: rowgroups 42 0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)] 1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)] 21 1048576 [('uid', 502800, 528400), ('timestamp', 300, 24786790), ('item_id', 206, 9390601), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)] 41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2495)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44765e6a887d2bfe72706b8409e91", "usage": { "input_tokens": 20806, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 394, "reasoning_output_tokens": 125, "total_tokens": 21200 }, "turn_token_usage": { "input_tokens": 92544, "cached_input_tokens": 74112, "cache_write_input_tokens": 0, "output_tokens": 1474, "reasoning_output_tokens": 253, "total_tokens": 94018 }, "thread_token_usage": { "input_tokens": 92544, "cached_input_tokens": 74112, "cache_write_input_tokens": 0, "output_tokens": 1474, "reasoning_output_tokens": 253, "total_tokens": 94018 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-33f967f2-1a37-4eff-908c-c9cbf0e05aa9", "process_id": "59750", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nprint('rowgroups',p.num_row_groups)\nfor i in [0,1,p.num_row_groups//2,p.num_row_groups-1]:\n rg=p.metadata.row_group(i)\n vals=[]\n for c in range(rg.num_columns):\n cc=rg.column(c)\n vals.append((cc.path_in_schema,cc.statistics.min if cc.statistics else None,cc.statistics.max if cc.statistics else None))\n print(i,rg.num_rows,vals)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nprint('rowgroups',p.num_row_groups)\nfor i in [0,1,p.num_row_groups//2,p.num_row_groups-1]:\n rg=p.metadata.row_group(i)\n vals=[]\n for c in range(rg.num_columns):\n cc=rg.column(c)\n vals.append((cc.path_in_schema,cc.statistics.min if cc.statistics else None,cc.statistics.max if cc.statistics else None))\n print(i,rg.num_rows,vals)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rowgroups 42\n0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n21 1048576 [('uid', 502800, 528400), ('timestamp', 300, 24786790), ('item_id', 206, 9390601), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2495)]\n", "stderr": "", "aggregated_output": "rowgroups 42\n0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n21 1048576 [('uid', 502800, 528400), ('timestamp', 300, 24786790), ('item_id', 206, 9390601), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2495)]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 219469442 }, "formatted_output": "rowgroups 42\n0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n21 1048576 [('uid', 502800, 528400), ('timestamp', 300, 24786790), ('item_id', 206, 9390601), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2490)]\n41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159), ('track_length_seconds', 5, 2495)]\n" } } ## Step 12 · agent · 2026-09-23T21:41:13.211Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:30.139Z { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; df -h /app /task; mkdir -p /app/solution; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data'\\nt=pd.read_parquet(D+'/target_users.parquet')\\nvl=pd.read_parquet(D+'/val_listens.parquet')\\nva=pd.read_parquet(D+'/val_likes.parquet')\\ncat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\\nz=np.load(D+'/validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,i):\\n r=urow.get(int(u)); c=icol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\\nstrong=vl[(vl.uid.isin(t.uid))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=va[(va.uid.isin(t.uid))&(va.is_organic==1)][['uid','item_id']].drop_duplicates()\\nfor name,x in [('strong',strong),('likes',likes)]:\\n print(name,len(x),'users',x.uid.nunique(),'catalog',x.item_id.isin(cat).mean(),'eligible',np.mean([elig(u,i) for u,i in x.itertuples(index=False,name=None)]))\\ngold=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\ngold=gold[[elig(u,i) for u,i in gold[['uid','item_id']].itertuples(index=False,name=None)]]\\nprint('gold',len(gold),'users',gold.uid.nunique(),'mean/user',gold.groupby('uid').size().mean(),'grade3',sum(gold.g==3))\\nprint('top validation relevant',gold.groupby('item_id').agg(n=('uid','nunique'),g=('g','sum')).sort_values('g',ascending=False).head(20))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:42:00.305Z Script completed Wall time 30.2 seconds Output: /bin/sh: 1: free: not found Filesystem Size Used Avail Use% Mounted on overlay 96G 11G 86G 11% / overlay 96G 11G 86G 11% / ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44774043487d2806bdae0e55c6019", "usage": { "input_tokens": 21519, "cached_input_tokens": 20608, "cache_write_input_tokens": 0, "output_tokens": 662, "reasoning_output_tokens": 123, "total_tokens": 22181 }, "turn_token_usage": { "input_tokens": 114063, "cached_input_tokens": 94720, "cache_write_input_tokens": 0, "output_tokens": 2136, "reasoning_output_tokens": 376, "total_tokens": 116199 }, "thread_token_usage": { "input_tokens": 114063, "cached_input_tokens": 94720, "cache_write_input_tokens": 0, "output_tokens": 2136, "reasoning_output_tokens": 376, "total_tokens": 116199 } } ## Step 13 · agent · 2026-09-23T21:42:04.653Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:06.154Z { "code": "const r = await tools.exec_command({\"cmd\":\"ps aux | grep '[p]ython'\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:42:06.208Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"a82b82","wall_time_seconds":0.00000376,"exit_code":1,"original_token_count":7,"output":"/bin/sh: 1: ps: not found\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-49e4abb6-a0f4-4baf-b262-051774d44ad1", "process_id": "50582", "command": [ "/bin/sh", "-lc", "ps aux | grep '[p]ython'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps aux | grep '[p]ython'" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3760 }, "formatted_output": "/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab447a89f8087d2bd5afb840a3bb71d", "usage": { "input_tokens": 22263, "cached_input_tokens": 21376, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 76, "total_tokens": 22396 }, "turn_token_usage": { "input_tokens": 136326, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 2269, "reasoning_output_tokens": 452, "total_tokens": 138595 }, "thread_token_usage": { "input_tokens": 136326, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 2269, "reasoning_output_tokens": 452, "total_tokens": 138595 } } ## Step 14 · agent · 2026-09-23T21:42:09.163Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:26.416Z { "code": "// @exec: {\"yield_time_ms\": 30000}\nconst r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data'; st=time.time()\\nt=pd.read_parquet(D+'/target_users.parquet'); print('targets')\\nvl=pd.read_parquet(D+'/val_listens.parquet'); va=pd.read_parquet(D+'/val_likes.parquet'); print('read',time.time()-st)\\ncat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\\nz=np.load(D+'/validation_eligible_candidates.npz'); urow=pd.Series(np.arange(len(z['uids'])),index=z['uids']); icol=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids'])\\ndef filterelig(x):\\n rr=urow.reindex(x.uid).to_numpy(); cc=icol.reindex(x.item_id).to_numpy(); ok=~np.isnan(rr)&~np.isnan(cc); rr=np.nan_to_num(rr).astype(int); cc=np.nan_to_num(cc).astype(int); ok &= ((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); return ok\\nstrong=vl[(vl.uid.isin(t.uid))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=va[(va.uid.isin(t.uid))&(va.is_organic==1)][['uid','item_id']].drop_duplicates()\\nfor name,x in [('strong',strong),('likes',likes)]: print(name,len(x),'users',x.uid.nunique(),'catalog',x.item_id.isin(cat).mean(),'eligible',filterelig(x).mean())\\ngold=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold=gold[filterelig(gold)]\\nprint('gold',len(gold),'users',gold.uid.nunique(),'mean/user',gold.groupby('uid').size().mean(),'grade3',sum(gold.g==3))\\nprint(gold.groupby('item_id').agg(n=('uid','nunique'),g=('g','sum')).sort_values('g',ascending=False).head(20))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:42:27.693Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"03af42","wall_time_seconds":1.105919886,"exit_code":0,"original_token_count":163,"output":"targets\nread 0.06823587417602539\nstrong 255608 users 6325 catalog 0.6681754874651811 eligible 0.13926402929485776\nlikes 17195 users 2714 catalog 0.4959581273626054 eligible 0.2568769991276534\ngold 37769 users 4263 mean/user 8.859723199624677 grade3 4417\n n g\nitem_id \n6399259 173 241\n6252104 109 137\n8250570 88 106\n2055777 83 97\n1265696 77 91\n7419525 42 68\n3134183 60 66\n1005397 46 66\n7579671 51 65\n6006390 42 56\n3361052 40 56\n374424 42 54\n7800492 38 54\n5010168 39 53\n6625048 43 51\n4948220 45 49\n8722394 41 49\n6762774 41 47\n5570747 42 46\n7227266 39 45\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab447ae760487d2951b9bc1f79bb40f", "usage": { "input_tokens": 22464, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 583, "reasoning_output_tokens": 35, "total_tokens": 23047 }, "turn_token_usage": { "input_tokens": 158790, "cached_input_tokens": 138240, "cache_write_input_tokens": 0, "output_tokens": 2852, "reasoning_output_tokens": 487, "total_tokens": 161642 }, "thread_token_usage": { "input_tokens": 158790, "cached_input_tokens": 138240, "cache_write_input_tokens": 0, "output_tokens": 2852, "reasoning_output_tokens": 487, "total_tokens": 161642 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5b4bc67-65b9-42be-a90e-f7581333a3ff", "process_id": "31773", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nD='/task/data'; st=time.time()\nt=pd.read_parquet(D+'/target_users.parquet'); print('targets')\nvl=pd.read_parquet(D+'/val_listens.parquet'); va=pd.read_parquet(D+'/val_likes.parquet'); print('read',time.time()-st)\ncat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D+'/validation_eligible_candidates.npz'); urow=pd.Series(np.arange(len(z['uids'])),index=z['uids']); icol=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids'])\ndef filterelig(x):\n rr=urow.reindex(x.uid).to_numpy(); cc=icol.reindex(x.item_id).to_numpy(); ok=~np.isnan(rr)&~np.isnan(cc); rr=np.nan_to_num(rr).astype(int); cc=np.nan_to_num(cc).astype(int); ok &= ((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(t.uid))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=va[(va.uid.isin(t.uid))&(va.is_organic==1)][['uid','item_id']].drop_duplicates()\nfor name,x in [('strong',strong),('likes',likes)]: print(name,len(x),'users',x.uid.nunique(),'catalog',x.item_id.isin(cat).mean(),'eligible',filterelig(x).mean())\ngold=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold=gold[filterelig(gold)]\nprint('gold',len(gold),'users',gold.uid.nunique(),'mean/user',gold.groupby('uid').size().mean(),'grade3',sum(gold.g==3))\nprint(gold.groupby('item_id').agg(n=('uid','nunique'),g=('g','sum')).sort_values('g',ascending=False).head(20))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nD='/task/data'; st=time.time()\nt=pd.read_parquet(D+'/target_users.parquet'); print('targets')\nvl=pd.read_parquet(D+'/val_listens.parquet'); va=pd.read_parquet(D+'/val_likes.parquet'); print('read',time.time()-st)\ncat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D+'/validation_eligible_candidates.npz'); urow=pd.Series(np.arange(len(z['uids'])),index=z['uids']); icol=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids'])\ndef filterelig(x):\n rr=urow.reindex(x.uid).to_numpy(); cc=icol.reindex(x.item_id).to_numpy(); ok=~np.isnan(rr)&~np.isnan(cc); rr=np.nan_to_num(rr).astype(int); cc=np.nan_to_num(cc).astype(int); ok &= ((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(t.uid))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=va[(va.uid.isin(t.uid))&(va.is_organic==1)][['uid','item_id']].drop_duplicates()\nfor name,x in [('strong',strong),('likes',likes)]: print(name,len(x),'users',x.uid.nunique(),'catalog',x.item_id.isin(cat).mean(),'eligible',filterelig(x).mean())\ngold=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold=gold[filterelig(gold)]\nprint('gold',len(gold),'users',gold.uid.nunique(),'mean/user',gold.groupby('uid').size().mean(),'grade3',sum(gold.g==3))\nprint(gold.groupby('item_id').agg(n=('uid','nunique'),g=('g','sum')).sort_values('g',ascending=False).head(20))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "targets\nread 0.06823587417602539\nstrong 255608 users 6325 catalog 0.6681754874651811 eligible 0.13926402929485776\nlikes 17195 users 2714 catalog 0.4959581273626054 eligible 0.2568769991276534\ngold 37769 users 4263 mean/user 8.859723199624677 grade3 4417\n n g\nitem_id \n6399259 173 241\n6252104 109 137\n8250570 88 106\n2055777 83 97\n1265696 77 91\n7419525 42 68\n3134183 60 66\n1005397 46 66\n7579671 51 65\n6006390 42 56\n3361052 40 56\n374424 42 54\n7800492 38 54\n5010168 39 53\n6625048 43 51\n4948220 45 49\n8722394 41 49\n6762774 41 47\n5570747 42 46\n7227266 39 45\n", "stderr": "", "aggregated_output": "targets\nread 0.06823587417602539\nstrong 255608 users 6325 catalog 0.6681754874651811 eligible 0.13926402929485776\nlikes 17195 users 2714 catalog 0.4959581273626054 eligible 0.2568769991276534\ngold 37769 users 4263 mean/user 8.859723199624677 grade3 4417\n n g\nitem_id \n6399259 173 241\n6252104 109 137\n8250570 88 106\n2055777 83 97\n1265696 77 91\n7419525 42 68\n3134183 60 66\n1005397 46 66\n7579671 51 65\n6006390 42 56\n3361052 40 56\n374424 42 54\n7800492 38 54\n5010168 39 53\n6625048 43 51\n4948220 45 49\n8722394 41 49\n6762774 41 47\n5570747 42 46\n7227266 39 45\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 105861594 }, "formatted_output": "targets\nread 0.06823587417602539\nstrong 255608 users 6325 catalog 0.6681754874651811 eligible 0.13926402929485776\nlikes 17195 users 2714 catalog 0.4959581273626054 eligible 0.2568769991276534\ngold 37769 users 4263 mean/user 8.859723199624677 grade3 4417\n n g\nitem_id \n6399259 173 241\n6252104 109 137\n8250570 88 106\n2055777 83 97\n1265696 77 91\n7419525 42 68\n3134183 60 66\n1005397 46 66\n7579671 51 65\n6006390 42 56\n3361052 40 56\n374424 42 54\n7800492 38 54\n5010168 39 53\n6625048 43 51\n4948220 45 49\n8722394 41 49\n6762774 41 47\n5570747 42 46\n7227266 39 45\n" } } ## Step 15 · agent · 2026-09-23T21:42:45.281Z ### Thinking unavailable — no text recorded ## Step 16 · agent · 2026-09-23T21:42:53.474Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:21.357Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_pop.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path('/task/data')\\n+O = Path('/app/solution')\\n+CUTOFF = 24786800\\n+\\n+def recent_counts(filename, conditions, spans=(7,14,30,90)):\\n+ out={s:{} for s in spans}\\n+ pf=pq.ParquetFile(D/filename)\\n+ for b in pf.iter_batches(batch_size=1_000_000):\\n+ x=b.to_pandas()\\n+ m=np.ones(len(x),bool)\\n+ for c,op,v in conditions:\\n+ if op=='>=': m &= x[c].to_numpy()>=v\\n+ elif op=='==': m &= x[c].to_numpy()==v\\n+ x=x.loc[m,['timestamp','item_id']]\\n+ for s in spans:\\n+ y=x[x.timestamp >= CUTOFF-s*86400]\\n+ if len(y):\\n+ z=y.item_id.value_counts()\\n+ d=out[s]\\n+ for k,v in z.items(): d[int(k)]=d.get(int(k),0)+int(v)\\n+ return out\\n+\\n+def write_global(name, score):\\n+ z=np.load(D/'validation_eligible_candidates.npz')\\n+ order=np.argsort(-score,kind='stable')\\n+ rows=[]\\n+ for r,u in enumerate(z['uids']):\\n+ eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\\n+ picks=order[eligible[order]][:100]\\n+ rows.append(pd.DataFrame({'uid':int(u),'item_id':z['item_ids'][picks].astype(np.int64),'rank':np.arange(1,101)}))\\n+ pd.concat(rows,ignore_index=True).to_parquet(O/f'val_{name}.parquet',index=False)\\n+\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+items=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\\n+listen=recent_counts('train_listens.parquet',[('is_organic','==',1),('played_ratio_pct','>=',80)])\\n+like=recent_counts('train_likes.parquet',[('is_organic','==',1)])\\n+features={\\n+ 'catalog': -cat.popularity_rank.to_numpy(dtype=float),\\n+ 'organic_catalog': np.log1p(cat.organic_positive_listens.to_numpy(dtype=float)),\\n+}\\n+for kind,ds in [('listen',listen),('like',like)]:\\n+ for span,d in ds.items():\\n+ a=np.zeros(len(items)); pos=idx.reindex(list(d)).to_numpy(); ok=~np.isnan(pos); a[pos[ok].astype(int)]=np.asarray(list(d.values()))[ok]\\n+ features[f'{kind}{span}']=np.log1p(a)\\n+\\n+# percentile-normalize each signal to make simple blends meaningful\\n+pr={}\\n+for k,a in features.items():\\n+ order=np.argsort(a,kind='stable'); ranks=np.empty(len(a)); ranks[order]=np.arange(len(a))/len(a); pr[k]=ranks\\n+ write_global(k,a)\\n+for span in (7,14,30,90):\\n+ write_global(f'blend{span}', pr['organic_catalog'] + 1.5*pr[f'listen{span}'] + .8*pr[f'like{span}'])\\n+pd.DataFrame(features).to_parquet(O/'pop_features.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:43:21.402Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7bd5636c-860c-4d59-8a0c-9a441a9fa40f", "changes": { "/app/solution/explore_pop.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nO = Path('/app/solution')\nCUTOFF = 24786800\n\ndef recent_counts(filename, conditions, spans=(7,14,30,90)):\n out={s:{} for s in spans}\n pf=pq.ParquetFile(D/filename)\n for b in pf.iter_batches(batch_size=1_000_000):\n x=b.to_pandas()\n m=np.ones(len(x),bool)\n for c,op,v in conditions:\n if op=='>=': m &= x[c].to_numpy()>=v\n elif op=='==': m &= x[c].to_numpy()==v\n x=x.loc[m,['timestamp','item_id']]\n for s in spans:\n y=x[x.timestamp >= CUTOFF-s*86400]\n if len(y):\n z=y.item_id.value_counts()\n d=out[s]\n for k,v in z.items(): d[int(k)]=d.get(int(k),0)+int(v)\n return out\n\ndef write_global(name, score):\n z=np.load(D/'validation_eligible_candidates.npz')\n order=np.argsort(-score,kind='stable')\n rows=[]\n for r,u in enumerate(z['uids']):\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n picks=order[eligible[order]][:100]\n rows.append(pd.DataFrame({'uid':int(u),'item_id':z['item_ids'][picks].astype(np.int64),'rank':np.arange(1,101)}))\n pd.concat(rows,ignore_index=True).to_parquet(O/f'val_{name}.parquet',index=False)\n\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nitems=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\nlisten=recent_counts('train_listens.parquet',[('is_organic','==',1),('played_ratio_pct','>=',80)])\nlike=recent_counts('train_likes.parquet',[('is_organic','==',1)])\nfeatures={\n 'catalog': -cat.popularity_rank.to_numpy(dtype=float),\n 'organic_catalog': np.log1p(cat.organic_positive_listens.to_numpy(dtype=float)),\n}\nfor kind,ds in [('listen',listen),('like',like)]:\n for span,d in ds.items():\n a=np.zeros(len(items)); pos=idx.reindex(list(d)).to_numpy(); ok=~np.isnan(pos); a[pos[ok].astype(int)]=np.asarray(list(d.values()))[ok]\n features[f'{kind}{span}']=np.log1p(a)\n\n# percentile-normalize each signal to make simple blends meaningful\npr={}\nfor k,a in features.items():\n order=np.argsort(a,kind='stable'); ranks=np.empty(len(a)); ranks[order]=np.arange(len(a))/len(a); pr[k]=ranks\n write_global(k,a)\nfor span in (7,14,30,90):\n write_global(f'blend{span}', pr['organic_catalog'] + 1.5*pr[f'listen{span}'] + .8*pr[f'like{span}'])\npd.DataFrame(features).to_parquet(O/'pop_features.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/explore_pop.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab447c3e39c87d2b73e2153d029dfac", "usage": { "input_tokens": 23407, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 1647, "reasoning_output_tokens": 766, "total_tokens": 25054 }, "turn_token_usage": { "input_tokens": 182197, "cached_input_tokens": 160512, "cache_write_input_tokens": 0, "output_tokens": 4499, "reasoning_output_tokens": 1253, "total_tokens": 186696 }, "thread_token_usage": { "input_tokens": 182197, "cached_input_tokens": 160512, "cache_write_input_tokens": 0, "output_tokens": 4499, "reasoning_output_tokens": 1253, "total_tokens": 186696 } } ## Step 17 · agent · 2026-09-23T21:43:24.123Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:27.247Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:43:57.425Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"c2b507","wall_time_seconds":30.001886134,"session_id":1072,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab447f9a9bc87d2bd3010c7229d3755", "usage": { "input_tokens": 25078, "cached_input_tokens": 23168, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 11, "total_tokens": 25196 }, "turn_token_usage": { "input_tokens": 207275, "cached_input_tokens": 183680, "cache_write_input_tokens": 0, "output_tokens": 4617, "reasoning_output_tokens": 1264, "total_tokens": 211892 }, "thread_token_usage": { "input_tokens": 207275, "cached_input_tokens": 183680, "cache_write_input_tokens": 0, "output_tokens": 4617, "reasoning_output_tokens": 1264, "total_tokens": 211892 } } ## Step 18 · agent · 2026-09-23T21:43:59.618Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:00.849Z { "code": "const r = await tools.write_stdin({\"session_id\":1072,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:44:30.858Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"82fb47","wall_time_seconds":30.001303406,"session_id":1072,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4481da5e087d295b17877d1bb8902", "usage": { "input_tokens": 25254, "cached_input_tokens": 24960, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 25309 }, "turn_token_usage": { "input_tokens": 232529, "cached_input_tokens": 208640, "cache_write_input_tokens": 0, "output_tokens": 4672, "reasoning_output_tokens": 1271, "total_tokens": 237201 }, "thread_token_usage": { "input_tokens": 232529, "cached_input_tokens": 208640, "cache_write_input_tokens": 0, "output_tokens": 4672, "reasoning_output_tokens": 1271, "total_tokens": 237201 } } ## Step 19 · agent · 2026-09-23T21:44:33.463Z ### Thinking unavailable — no text recorded A first sanity check shows only about 14% of future strong organic listens survive the discovery eligibility boundary, confirming that eligible relevance is largely unseen/new-to-user music. I’m now benchmarking global trend signals while preparing a latent collaborative model that can generalize from each listener’s history to unseen catalog tracks. ### exec · 2026-09-23T21:44:37.034Z { "code": "const r = await tools.write_stdin({\"session_id\":1072,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:45:07.044Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"2cc9a3","wall_time_seconds":30.001969612,"session_id":1072,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4483f0c8087d2871566078b98e369", "usage": { "input_tokens": 25366, "cached_input_tokens": 25088, "cache_write_input_tokens": 0, "output_tokens": 140, "reasoning_output_tokens": 25, "total_tokens": 25506 }, "turn_token_usage": { "input_tokens": 257895, "cached_input_tokens": 233728, "cache_write_input_tokens": 0, "output_tokens": 4812, "reasoning_output_tokens": 1296, "total_tokens": 262707 }, "thread_token_usage": { "input_tokens": 257895, "cached_input_tokens": 233728, "cache_write_input_tokens": 0, "output_tokens": 4812, "reasoning_output_tokens": 1296, "total_tokens": 262707 } } ## Step 20 · agent · 2026-09-23T21:45:09.080Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:10.469Z { "code": "const r = await tools.write_stdin({\"session_id\":1072,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:45:10.577Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3fb27b","wall_time_seconds":0.098650216,"exit_code":130,"original_token_count":299,"output":"Traceback (most recent call last):\n File \"/app/solution/explore_pop.py\", line 55, in \n write_global(k,a)\n File \"/app/solution/explore_pop.py\", line 33, in write_global\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab448633e3487d28e78bb4031939ef6", "usage": { "input_tokens": 25565, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 21, "total_tokens": 25639 }, "turn_token_usage": { "input_tokens": 283460, "cached_input_tokens": 258944, "cache_write_input_tokens": 0, "output_tokens": 4886, "reasoning_output_tokens": 1317, "total_tokens": 288346 }, "thread_token_usage": { "input_tokens": 283460, "cached_input_tokens": 258944, "cache_write_input_tokens": 0, "output_tokens": 4886, "reasoning_output_tokens": 1317, "total_tokens": 288346 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21aeec00-8318-4440-8d2a-3530f40a14c9", "process_id": "1072", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/explore_pop.py\", line 55, in \n write_global(k,a)\n File \"/app/solution/explore_pop.py\", line 33, in write_global\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/explore_pop.py\", line 55, in \n write_global(k,a)\n File \"/app/solution/explore_pop.py\", line 33, in write_global\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 103, "nanos": 153036850 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/explore_pop.py\", line 55, in \n write_global(k,a)\n File \"/app/solution/explore_pop.py\", line 33, in write_global\n eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } ## Step 21 · agent · 2026-09-23T21:45:13.954Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:27.467Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/explore_pop.py\\n@@\\n-def write_global(name, score):\\n- z=np.load(D/'validation_eligible_candidates.npz')\\n+def write_global(name, score):\\n@@\\n- rows=[]\\n- for r,u in enumerate(z['uids']):\\n- eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\\n+ uu=np.repeat(elig_uids,100); ii=np.empty(len(uu),np.int64)\\n+ for r,u in enumerate(elig_uids):\\n+ eligible=np.unpackbits(elig_packed[r],count=len(order),bitorder='little').astype(bool)\\n picks=order[eligible[order]][:100]\\n- rows.append(pd.DataFrame({'uid':int(u),'item_id':z['item_ids'][picks].astype(np.int64),'rank':np.arange(1,101)}))\\n- pd.concat(rows,ignore_index=True).to_parquet(O/f'val_{name}.parquet',index=False)\\n+ ii[r*100:(r+1)*100]=elig_items[picks]\\n+ pd.DataFrame({'uid':uu,'item_id':ii,'rank':np.tile(np.arange(1,101),len(elig_uids))}).to_parquet(O/f'val_{name}.parquet',index=False)\\n@@\\n cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n items=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ elig_uids=z['uids'].copy(); elig_items=z['item_ids'].copy(); elig_packed=z['packed_eligible'].copy()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:45:27.541Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e30c3cdd-467d-4735-9a1d-e35d45e83f1f", "changes": { "/app/solution/explore_pop.py": { "type": "update", "unified_diff": "@@ -28,10 +28,9 @@\n def write_global(name, score):\n- z=np.load(D/'validation_eligible_candidates.npz')\n order=np.argsort(-score,kind='stable')\n- rows=[]\n- for r,u in enumerate(z['uids']):\n- eligible=np.unpackbits(z['packed_eligible'][r],count=len(order),bitorder='little').astype(bool)\n+ uu=np.repeat(elig_uids,100); ii=np.empty(len(uu),np.int64)\n+ for r,u in enumerate(elig_uids):\n+ eligible=np.unpackbits(elig_packed[r],count=len(order),bitorder='little').astype(bool)\n picks=order[eligible[order]][:100]\n- rows.append(pd.DataFrame({'uid':int(u),'item_id':z['item_ids'][picks].astype(np.int64),'rank':np.arange(1,101)}))\n- pd.concat(rows,ignore_index=True).to_parquet(O/f'val_{name}.parquet',index=False)\n+ ii[r*100:(r+1)*100]=elig_items[picks]\n+ pd.DataFrame({'uid':uu,'item_id':ii,'rank':np.tile(np.arange(1,101),len(elig_uids))}).to_parquet(O/f'val_{name}.parquet',index=False)\n \n@@ -39,2 +38,4 @@\n items=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\n+with np.load(D/'validation_eligible_candidates.npz') as z:\n+ elig_uids=z['uids'].copy(); elig_items=z['item_ids'].copy(); elig_packed=z['packed_eligible'].copy()\n listen=recent_counts('train_listens.parquet',[('is_organic','==',1),('played_ratio_pct','>=',80)])\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/explore_pop.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44866d8e087d2807ffe123bfd5db5", "usage": { "input_tokens": 26049, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 493, "reasoning_output_tokens": 61, "total_tokens": 26542 }, "turn_token_usage": { "input_tokens": 309509, "cached_input_tokens": 284288, "cache_write_input_tokens": 0, "output_tokens": 5379, "reasoning_output_tokens": 1378, "total_tokens": 314888 }, "thread_token_usage": { "input_tokens": 309509, "cached_input_tokens": 284288, "cache_write_input_tokens": 0, "output_tokens": 5379, "reasoning_output_tokens": 1378, "total_tokens": 314888 } } ## Step 22 · agent · 2026-09-23T21:45:29.451Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:32.725Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tail -4 | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:46:02.891Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"8858d3","wall_time_seconds":30.001409537,"session_id":33119,"original_token_count":115,"output":" \"quality_ndcg@10\": 0.017047, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01624, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016781, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.014862, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.026726, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44877db9087d2803e280031e5a7fb", "usage": { "input_tokens": 26566, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 6, "total_tokens": 26683 }, "turn_token_usage": { "input_tokens": 336075, "cached_input_tokens": 310144, "cache_write_input_tokens": 0, "output_tokens": 5496, "reasoning_output_tokens": 1384, "total_tokens": 341571 }, "thread_token_usage": { "input_tokens": 336075, "cached_input_tokens": 310144, "cache_write_input_tokens": 0, "output_tokens": 5496, "reasoning_output_tokens": 1384, "total_tokens": 341571 } } ## Step 23 · agent · 2026-09-23T21:46:04.741Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:46:05.963Z { "code": "const r = await tools.write_stdin({\"session_id\":33119,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:46:19.738Z Script completed Wall time 13.8 seconds Output: {"chunk_id":"37e68e","wall_time_seconds":13.765079383,"exit_code":0,"original_token_count":116,"output":" \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4489b141887d2941ae1ffd2523c96", "usage": { "input_tokens": 26929, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 9, "total_tokens": 26986 }, "turn_token_usage": { "input_tokens": 363004, "cached_input_tokens": 336512, "cache_write_input_tokens": 0, "output_tokens": 5553, "reasoning_output_tokens": 1393, "total_tokens": 368557 }, "thread_token_usage": { "input_tokens": 363004, "cached_input_tokens": 336512, "cache_write_input_tokens": 0, "output_tokens": 5553, "reasoning_output_tokens": 1393, "total_tokens": 368557 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a8f2936c-1b18-4379-a489-f6fe1a822732", "process_id": "33119", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -4 | tr '\\n' ' '; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/explore_pop.py && for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -4 | tr '\\n' ' '; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.017047, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01624, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016781, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.014862, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.026726, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.017047, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01624, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016781, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.014862, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.026726, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n", "exit_code": 0, "duration": { "secs": 46, "nanos": 846850293 }, "formatted_output": " \"quality_ndcg@10\": 0.017047, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01624, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016781, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.014862, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.026726, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n" } } ## Step 24 · agent · 2026-09-23T21:46:22.266Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:46:25.144Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/val_*.parquet; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:46:55.313Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"1ade81","wall_time_seconds":30.000759511,"session_id":51394,"original_token_count":150,"output":"val_blend14.parquet \"quality_ndcg@10\": 0.017047,\nval_blend30.parquet \"quality_ndcg@10\": 0.01624,\nval_blend7.parquet \"quality_ndcg@10\": 0.016781,\nval_blend90.parquet \"quality_ndcg@10\": 0.014862,\nval_catalog.parquet \"quality_ndcg@10\": 0.01071,\nval_like14.parquet \"quality_ndcg@10\": 0.026726,\nval_like30.parquet \"quality_ndcg@10\": 0.018429,\nval_like7.parquet \"quality_ndcg@10\": 0.029297,\nval_like90.parquet \"quality_ndcg@10\": 0.016217,\nval_listen14.parquet \"quality_ndcg@10\": 0.022878,\nval_listen30.parquet \"quality_ndcg@10\": 0.013314,\nval_listen7.parquet "} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab448abeee487d287adc21591256ee4", "usage": { "input_tokens": 27231, "cached_input_tokens": 26752, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 24, "total_tokens": 27354 }, "turn_token_usage": { "input_tokens": 390235, "cached_input_tokens": 363264, "cache_write_input_tokens": 0, "output_tokens": 5676, "reasoning_output_tokens": 1417, "total_tokens": 395911 }, "thread_token_usage": { "input_tokens": 390235, "cached_input_tokens": 363264, "cache_write_input_tokens": 0, "output_tokens": 5676, "reasoning_output_tokens": 1417, "total_tokens": 395911 } } ### exec · 2026-09-23T21:46:58.748Z { "code": "const r = await tools.write_stdin({\"session_id\":51394,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:47:01.399Z Script completed Wall time 2.6 seconds Output: {"chunk_id":"65c968","wall_time_seconds":2.640979738,"exit_code":0,"original_token_count":37,"output":" \"quality_ndcg@10\": 0.028131,\nval_listen90.parquet \"quality_ndcg@10\": 0.015822,\nval_organic_catalog.parquet \"quality_ndcg@10\": 0.010577,\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab448cf81e087d2a1c23be249b09780", "usage": { "input_tokens": 27641, "cached_input_tokens": 27008, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 27687 }, "turn_token_usage": { "input_tokens": 417876, "cached_input_tokens": 390272, "cache_write_input_tokens": 0, "output_tokens": 5722, "reasoning_output_tokens": 1417, "total_tokens": 423598 }, "thread_token_usage": { "input_tokens": 417876, "cached_input_tokens": 390272, "cache_write_input_tokens": 0, "output_tokens": 5722, "reasoning_output_tokens": 1417, "total_tokens": 423598 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c2cd133f-0002-4418-9fca-ab4fdf86e5cf", "process_id": "51394", "command": [ "/bin/sh", "-lc", "for f in /app/solution/val_*.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/val_*.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.017047,\nval_blend30.parquet \"quality_ndcg@10\": 0.01624,\nval_blend7.parquet \"quality_ndcg@10\": 0.016781,\nval_blend90.parquet \"quality_ndcg@10\": 0.014862,\nval_catalog.parquet \"quality_ndcg@10\": 0.01071,\nval_like14.parquet \"quality_ndcg@10\": 0.026726,\nval_like30.parquet \"quality_ndcg@10\": 0.018429,\nval_like7.parquet \"quality_ndcg@10\": 0.029297,\nval_like90.parquet \"quality_ndcg@10\": 0.016217,\nval_listen14.parquet \"quality_ndcg@10\": 0.022878,\nval_listen30.parquet \"quality_ndcg@10\": 0.013314,\nval_listen7.parquet \"quality_ndcg@10\": 0.028131,\nval_listen90.parquet \"quality_ndcg@10\": 0.015822,\nval_organic_catalog.parquet \"quality_ndcg@10\": 0.010577,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.017047,\nval_blend30.parquet \"quality_ndcg@10\": 0.01624,\nval_blend7.parquet \"quality_ndcg@10\": 0.016781,\nval_blend90.parquet \"quality_ndcg@10\": 0.014862,\nval_catalog.parquet \"quality_ndcg@10\": 0.01071,\nval_like14.parquet \"quality_ndcg@10\": 0.026726,\nval_like30.parquet \"quality_ndcg@10\": 0.018429,\nval_like7.parquet \"quality_ndcg@10\": 0.029297,\nval_like90.parquet \"quality_ndcg@10\": 0.016217,\nval_listen14.parquet \"quality_ndcg@10\": 0.022878,\nval_listen30.parquet \"quality_ndcg@10\": 0.013314,\nval_listen7.parquet \"quality_ndcg@10\": 0.028131,\nval_listen90.parquet \"quality_ndcg@10\": 0.015822,\nval_organic_catalog.parquet \"quality_ndcg@10\": 0.010577,\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 85820940 }, "formatted_output": " \"quality_ndcg@10\": 0.017047,\nval_blend30.parquet \"quality_ndcg@10\": 0.01624,\nval_blend7.parquet \"quality_ndcg@10\": 0.016781,\nval_blend90.parquet \"quality_ndcg@10\": 0.014862,\nval_catalog.parquet \"quality_ndcg@10\": 0.01071,\nval_like14.parquet \"quality_ndcg@10\": 0.026726,\nval_like30.parquet \"quality_ndcg@10\": 0.018429,\nval_like7.parquet \"quality_ndcg@10\": 0.029297,\nval_like90.parquet \"quality_ndcg@10\": 0.016217,\nval_listen14.parquet \"quality_ndcg@10\": 0.022878,\nval_listen30.parquet \"quality_ndcg@10\": 0.013314,\nval_listen7.parquet \"quality_ndcg@10\": 0.028131,\nval_listen90.parquet \"quality_ndcg@10\": 0.015822,\nval_organic_catalog.parquet \"quality_ndcg@10\": 0.010577,\n" } } ## Step 25 · agent · 2026-09-23T21:47:21.460Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-09-23T21:47:38.025Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-09-23T21:47:54.599Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-09-23T21:48:05.715Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:06.647Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/cf_validate.py\\n+from pathlib import Path\\n+import json, time\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUTOFF=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); nitems=len(items)\\n+imap=pd.Series(np.arange(nitems,dtype=np.int32),index=items)\\n+\\n+def build_matrix():\\n+ cache=O/'R_train.npz'\\n+ if cache.exists(): return sparse.load_npz(cache)\\n+ rr=[]; cc=[]; vv=[]; pf=pq.ParquetFile(D/'train_listens.parquet')\\n+ for bi,b in enumerate(pf.iter_batches(batch_size=1_100_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\\n+ x=b.to_pandas(); m=x.played_ratio_pct.to_numpy()>=50; x=x.loc[m]\\n+ col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\\n+ row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32)\\n+ age=(CUTOFF-x.timestamp.to_numpy())/86400\\n+ rec=.35+.65*np.exp(-age/90)\\n+ qual=np.where(x.played_ratio_pct.to_numpy()>=80,1.0,.35)\\n+ org=np.where(x.is_organic.to_numpy()==1,1.0,.6)\\n+ val=(rec*qual*org).astype(np.float32)\\n+ key=row.astype(np.int64)*nitems+col; uq,inv=np.unique(key,return_inverse=True); sums=np.bincount(inv,weights=val)\\n+ rr.append((uq//nitems).astype(np.int32)); cc.append((uq%nitems).astype(np.int32)); vv.append(sums.astype(np.float32))\\n+ print('batch',bi,'kept',len(x),flush=True)\\n+ # explicit positive signal\\n+ x=pd.read_parquet(D/'train_likes.parquet'); col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\\n+ row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32); age=(CUTOFF-x.timestamp.to_numpy())/86400\\n+ val=(2.0*(.35+.65*np.exp(-age/120))*np.where(x.is_organic.to_numpy()==1,1,.7)).astype(np.float32)\\n+ rr.append(row);cc.append(col);vv.append(val)\\n+ R=sparse.coo_matrix((np.concatenate(vv),(np.concatenate(rr),np.concatenate(cc))),shape=(10000,nitems)).tocsr(); R.sum_duplicates()\\n+ R.data=np.log1p(R.data)\\n+ sparse.save_npz(cache,R,compressed=False)\\n+ return R\\n+\\n+def truth():\\n+ vl=pd.read_parquet(D/'val_listens.parquet'); va=pd.read_parquet(D/'val_likes.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\n+ a=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1)\\n+ b=va[(va.uid.isin(targets))&(va.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3)\\n+ x=pd.concat([a,b]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\n+ return {(int(u),int(i)):int(g) for u,i,g in x.itertuples(index=False,name=None)}\\n+\\n+R=build_matrix(); print('R',R.shape,R.nnz,flush=True)\\n+norm=np.sqrt(np.asarray(R.multiply(R).sum(axis=1)).ravel()); P=sparse.diags(1/np.maximum(norm,1e-8))@R\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\\n+assert np.array_equal(ei,items)\\n+# best global trend from development: 7-day organic likes\\n+f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.argsort(glob,kind='stable')]=np.arange(nitems,dtype=np.float32)/nitems\\n+gold=truth(); discounts=1/np.log2(np.arange(2,12));\\n+idcg={}\\n+for (u,i),g in gold.items(): idcg.setdefault(u,[]).append(g)\\n+idcg={u:np.sum((2**np.sort(gs)[::-1][:10]-1)*discounts[:min(10,len(gs))]) for u,gs in idcg.items()}\\n+\\n+configs=[(50,b) for b in (0,.25,.5,.75,1.0,1.5,2,3)]+[(150,b) for b in (0,.5,1,1.5,2)]\\n+dcg={c:0. for c in configs}; outs={c:np.empty((len(eu),100),np.int32) for c in configs}\\n+bs=96\\n+for st in range(0,len(eu),bs):\\n+ en=min(len(eu),st+bs); rows=(eu[st:en].astype(np.int64)//100-1).astype(int)\\n+ sim=(P[rows]@P.T).toarray(); sim[np.arange(en-st),rows]=0\\n+ # weak/negative cosine matches are useless\\n+ for K in sorted(set(k for k,b in configs)):\\n+ kth=np.argpartition(sim,-K,axis=1)[:,-K:]; ss=np.zeros_like(sim); np.put_along_axis(ss,kth,np.take_along_axis(sim,kth,axis=1),axis=1)\\n+ pred=np.asarray(ss@R)\\n+ pred=np.log1p(5*pred/np.maximum(ss.sum(axis=1,keepdims=True),1e-8))\\n+ for beta in [b for k,b in configs if k==K]:\\n+ sc=pred+beta*globrank[None,:]\\n+ out=outs[(K,beta)]\\n+ for j in range(en-st):\\n+ eligible=np.unpackbits(ep[st+j],count=nitems,bitorder='little').astype(bool); sc[j,~eligible]=-1e20\\n+ ix=np.argpartition(sc[j],-100)[-100:]; ix=ix[np.argsort(sc[j,ix])[::-1]]; out[st+j]=ix\\n+ print('score batch',st,en,flush=True)\\n+for cfg,out in outs.items():\\n+ s=0.\\n+ for r,u0 in enumerate(eu):\\n+ u=int(u0)\\n+ if u not in idcg: continue\\n+ gains=np.array([gold.get((u,int(items[i])),0) for i in out[r,:10]])\\n+ s += np.sum((2**gains-1)*discounts[:10])/idcg[u]\\n+ val=s/len(idcg); print(cfg,val)\\n+ if val==max([] or [val]): pass\\n+best=max(configs,key=lambda c: sum(np.sum((2**np.array([gold.get((int(u),int(items[i])),0) for i in outs[c][r,:10]])-1)*discounts[:10])/idcg[int(u)] for r,u in enumerate(eu) if int(u) in idcg))\\n+print('BEST',best)\\n+out=outs[best]\\n+pd.DataFrame({'uid':np.repeat(eu,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(eu))}).to_parquet(O/'validation_cf.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:49:06.728Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2cf0632a-4891-419e-a4ca-7b10212bfd61", "changes": { "/app/solution/cf_validate.py": { "type": "add", "content": "from pathlib import Path\nimport json, time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\n\nD=Path('/task/data'); O=Path('/app/solution'); CUTOFF=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); nitems=len(items)\nimap=pd.Series(np.arange(nitems,dtype=np.int32),index=items)\n\ndef build_matrix():\n cache=O/'R_train.npz'\n if cache.exists(): return sparse.load_npz(cache)\n rr=[]; cc=[]; vv=[]; pf=pq.ParquetFile(D/'train_listens.parquet')\n for bi,b in enumerate(pf.iter_batches(batch_size=1_100_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\n x=b.to_pandas(); m=x.played_ratio_pct.to_numpy()>=50; x=x.loc[m]\n col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\n row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32)\n age=(CUTOFF-x.timestamp.to_numpy())/86400\n rec=.35+.65*np.exp(-age/90)\n qual=np.where(x.played_ratio_pct.to_numpy()>=80,1.0,.35)\n org=np.where(x.is_organic.to_numpy()==1,1.0,.6)\n val=(rec*qual*org).astype(np.float32)\n key=row.astype(np.int64)*nitems+col; uq,inv=np.unique(key,return_inverse=True); sums=np.bincount(inv,weights=val)\n rr.append((uq//nitems).astype(np.int32)); cc.append((uq%nitems).astype(np.int32)); vv.append(sums.astype(np.float32))\n print('batch',bi,'kept',len(x),flush=True)\n # explicit positive signal\n x=pd.read_parquet(D/'train_likes.parquet'); col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\n row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32); age=(CUTOFF-x.timestamp.to_numpy())/86400\n val=(2.0*(.35+.65*np.exp(-age/120))*np.where(x.is_organic.to_numpy()==1,1,.7)).astype(np.float32)\n rr.append(row);cc.append(col);vv.append(val)\n R=sparse.coo_matrix((np.concatenate(vv),(np.concatenate(rr),np.concatenate(cc))),shape=(10000,nitems)).tocsr(); R.sum_duplicates()\n R.data=np.log1p(R.data)\n sparse.save_npz(cache,R,compressed=False)\n return R\n\ndef truth():\n vl=pd.read_parquet(D/'val_listens.parquet'); va=pd.read_parquet(D/'val_likes.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\n a=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1)\n b=va[(va.uid.isin(targets))&(va.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3)\n x=pd.concat([a,b]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n return {(int(u),int(i)):int(g) for u,i,g in x.itertuples(index=False,name=None)}\n\nR=build_matrix(); print('R',R.shape,R.nnz,flush=True)\nnorm=np.sqrt(np.asarray(R.multiply(R).sum(axis=1)).ravel()); P=sparse.diags(1/np.maximum(norm,1e-8))@R\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\nassert np.array_equal(ei,items)\n# best global trend from development: 7-day organic likes\nf=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.argsort(glob,kind='stable')]=np.arange(nitems,dtype=np.float32)/nitems\ngold=truth(); discounts=1/np.log2(np.arange(2,12));\nidcg={}\nfor (u,i),g in gold.items(): idcg.setdefault(u,[]).append(g)\nidcg={u:np.sum((2**np.sort(gs)[::-1][:10]-1)*discounts[:min(10,len(gs))]) for u,gs in idcg.items()}\n\nconfigs=[(50,b) for b in (0,.25,.5,.75,1.0,1.5,2,3)]+[(150,b) for b in (0,.5,1,1.5,2)]\ndcg={c:0. for c in configs}; outs={c:np.empty((len(eu),100),np.int32) for c in configs}\nbs=96\nfor st in range(0,len(eu),bs):\n en=min(len(eu),st+bs); rows=(eu[st:en].astype(np.int64)//100-1).astype(int)\n sim=(P[rows]@P.T).toarray(); sim[np.arange(en-st),rows]=0\n # weak/negative cosine matches are useless\n for K in sorted(set(k for k,b in configs)):\n kth=np.argpartition(sim,-K,axis=1)[:,-K:]; ss=np.zeros_like(sim); np.put_along_axis(ss,kth,np.take_along_axis(sim,kth,axis=1),axis=1)\n pred=np.asarray(ss@R)\n pred=np.log1p(5*pred/np.maximum(ss.sum(axis=1,keepdims=True),1e-8))\n for beta in [b for k,b in configs if k==K]:\n sc=pred+beta*globrank[None,:]\n out=outs[(K,beta)]\n for j in range(en-st):\n eligible=np.unpackbits(ep[st+j],count=nitems,bitorder='little').astype(bool); sc[j,~eligible]=-1e20\n ix=np.argpartition(sc[j],-100)[-100:]; ix=ix[np.argsort(sc[j,ix])[::-1]]; out[st+j]=ix\n print('score batch',st,en,flush=True)\nfor cfg,out in outs.items():\n s=0.\n for r,u0 in enumerate(eu):\n u=int(u0)\n if u not in idcg: continue\n gains=np.array([gold.get((u,int(items[i])),0) for i in out[r,:10]])\n s += np.sum((2**gains-1)*discounts[:10])/idcg[u]\n val=s/len(idcg); print(cfg,val)\n if val==max([] or [val]): pass\nbest=max(configs,key=lambda c: sum(np.sum((2**np.array([gold.get((int(u),int(items[i])),0) for i in outs[c][r,:10]])-1)*discounts[:10])/idcg[int(u)] for r,u in enumerate(eu) if int(u) in idcg))\nprint('BEST',best)\nout=outs[best]\npd.DataFrame({'uid':np.repeat(eu,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(eu))}).to_parquet(O/'validation_cf.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/cf_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab448d59af487d29f5c503acb8494a5", "usage": { "input_tokens": 27799, "cached_input_tokens": 27520, "cache_write_input_tokens": 0, "output_tokens": 3807, "reasoning_output_tokens": 1893, "total_tokens": 31606 }, "turn_token_usage": { "input_tokens": 445675, "cached_input_tokens": 417792, "cache_write_input_tokens": 0, "output_tokens": 9529, "reasoning_output_tokens": 3310, "total_tokens": 455204 }, "thread_token_usage": { "input_tokens": 445675, "cached_input_tokens": 417792, "cache_write_input_tokens": 0, "output_tokens": 9529, "reasoning_output_tokens": 3310, "total_tokens": 455204 } } ## Step 29 · agent · 2026-09-23T21:49:13.330Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:18.205Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/cf_validate.py\\n@@\\n-gold=truth(); discounts=1/np.log2(np.arange(2,12));\\n+gold=truth()\\n+urow={int(u):r for r,u in enumerate(eu)}\\n+gold={(u,i):g for (u,i),g in gold.items() if u in urow and i in imap.index and ((ep[urow[u],int(imap[i])//8]>>(int(imap[i])%8))&1)}\\n+discounts=1/np.log2(np.arange(2,12));\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:49:18.263Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-58668aef-975e-448f-9eea-e79d28b2729f", "changes": { "/app/solution/cf_validate.py": { "type": "update", "unified_diff": "@@ -51,3 +51,6 @@\n f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.argsort(glob,kind='stable')]=np.arange(nitems,dtype=np.float32)/nitems\n-gold=truth(); discounts=1/np.log2(np.arange(2,12));\n+gold=truth()\n+urow={int(u):r for r,u in enumerate(eu)}\n+gold={(u,i):g for (u,i),g in gold.items() if u in urow and i in imap.index and ((ep[urow[u],int(imap[i])//8]>>(int(imap[i])%8))&1)}\n+discounts=1/np.log2(np.arange(2,12));\n idcg={}\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/cf_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44952fbec87d2b400adf50a898c45", "usage": { "input_tokens": 31630, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 292, "reasoning_output_tokens": 125, "total_tokens": 31922 }, "turn_token_usage": { "input_tokens": 477305, "cached_input_tokens": 437120, "cache_write_input_tokens": 0, "output_tokens": 9821, "reasoning_output_tokens": 3435, "total_tokens": 487126 }, "thread_token_usage": { "input_tokens": 477305, "cached_input_tokens": 437120, "cache_write_input_tokens": 0, "output_tokens": 9821, "reasoning_output_tokens": 3435, "total_tokens": 487126 } } ## Step 30 · agent · 2026-09-23T21:49:20.089Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:21.926Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/cf_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:49:52.091Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"91ec60","wall_time_seconds":30.000312178,"session_id":61577,"original_token_count":406,"output":"batch 0 kept 503150\nbatch 1 kept 488738\nbatch 2 kept 480923\nbatch 3 kept 488970\nbatch 4 kept 520993\nbatch 5 kept 510031\nbatch 6 kept 517318\nbatch 7 kept 499099\nbatch 8 kept 478682\nbatch 9 kept 516230\nbatch 10 kept 518257\nbatch 11 kept 537077\nbatch 12 kept 503534\nbatch 13 kept 537603\nbatch 14 kept 513441\nbatch 15 kept 470942\nbatch 16 kept 487415\nbatch 17 kept 524025\nbatch 18 kept 526687\nbatch 19 kept 532221\nbatch 20 kept 514721\nbatch 21 kept 480212\nbatch 22 kept 488199\nbatch 23 kept 538575\nbatch 24 kept 506675\nbatch 25 kept 490243\nbatch 26 kept 490872\nbatch 27 kept 508536\nbatch 28 kept 516455\nbatch 29 kept 515074\nbatch 30 kept 512849\nbatch 31 kept 495220\nbatch 32 kept 498879\nbatch 33 kept 515499\nbatch 34 kept 509523\nbatch 35 kept 506148\nbatch 36 kept 516821\nbatch 37 kept 513611\nbatch 38 kept 530375\nbatch 39 kept 252506\nR (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4495ea0a087d2964e293c0a2da5b5", "usage": { "input_tokens": 31946, "cached_input_tokens": 31488, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 6, "total_tokens": 32018 }, "turn_token_usage": { "input_tokens": 509251, "cached_input_tokens": 468608, "cache_write_input_tokens": 0, "output_tokens": 9893, "reasoning_output_tokens": 3441, "total_tokens": 519144 }, "thread_token_usage": { "input_tokens": 509251, "cached_input_tokens": 468608, "cache_write_input_tokens": 0, "output_tokens": 9893, "reasoning_output_tokens": 3441, "total_tokens": 519144 } } ## Step 31 · agent · 2026-09-23T21:49:54.386Z ### Thinking unavailable — no text recorded The strongest non-personalized baseline so far is seven-day organic-like momentum at NDCG@10 ≈ 0.0293, nearly triple raw catalog popularity (≈ 0.0107). The collaborative matrix is now built from 5.69 million weighted user–track pairs; I’m tuning neighborhood size and the balance between personalized affinity and short-term momentum. ### exec · 2026-09-23T21:49:58.309Z { "code": "const r = await tools.write_stdin({\"session_id\":61577,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:50:28.319Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"62e459","wall_time_seconds":30.001525564,"session_id":61577,"original_token_count":393,"output":"score batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0) 0.01949490507941361\n(50, 0.25) 0.01999415342787708\n(50, 0.5) 0.020361720316188454\n(50, 0.75) 0.02058260619138934\n(50, 1.0) 0.021148150938599196\n(50, 1.5) 0.021594402592070735\n(50, 2) 0.021607212366280132\n(50, 3) 0.021588981871920144\n(150, 0) 0.019330200878118254\n(150, 0.5) 0.019851736779115812\n(150, 1) 0.02024065456741478\n(150, 1.5) 0.020357687250859974\n(150, 2) 0.02055523984184607\nBEST (50, 2)\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab4498056dc87d2b3e475e1193c3540", "usage": { "input_tokens": 32711, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 145, "reasoning_output_tokens": 14, "total_tokens": 32856 }, "turn_token_usage": { "input_tokens": 541962, "cached_input_tokens": 500352, "cache_write_input_tokens": 0, "output_tokens": 10038, "reasoning_output_tokens": 3455, "total_tokens": 552000 }, "thread_token_usage": { "input_tokens": 541962, "cached_input_tokens": 500352, "cache_write_input_tokens": 0, "output_tokens": 10038, "reasoning_output_tokens": 3455, "total_tokens": 552000 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b8d86c0-0595-44ad-bd78-611b88b0a435", "process_id": "61577", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/cf_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/cf_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "batch 0 kept 503150\nbatch 1 kept 488738\nbatch 2 kept 480923\nbatch 3 kept 488970\nbatch 4 kept 520993\nbatch 5 kept 510031\nbatch 6 kept 517318\nbatch 7 kept 499099\nbatch 8 kept 478682\nbatch 9 kept 516230\nbatch 10 kept 518257\nbatch 11 kept 537077\nbatch 12 kept 503534\nbatch 13 kept 537603\nbatch 14 kept 513441\nbatch 15 kept 470942\nbatch 16 kept 487415\nbatch 17 kept 524025\nbatch 18 kept 526687\nbatch 19 kept 532221\nbatch 20 kept 514721\nbatch 21 kept 480212\nbatch 22 kept 488199\nbatch 23 kept 538575\nbatch 24 kept 506675\nbatch 25 kept 490243\nbatch 26 kept 490872\nbatch 27 kept 508536\nbatch 28 kept 516455\nbatch 29 kept 515074\nbatch 30 kept 512849\nbatch 31 kept 495220\nbatch 32 kept 498879\nbatch 33 kept 515499\nbatch 34 kept 509523\nbatch 35 kept 506148\nbatch 36 kept 516821\nbatch 37 kept 513611\nbatch 38 kept 530375\nbatch 39 kept 252506\nR (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0) 0.01949490507941361\n(50, 0.25) 0.01999415342787708\n(50, 0.5) 0.020361720316188454\n(50, 0.75) 0.02058260619138934\n(50, 1.0) 0.021148150938599196\n(50, 1.5) 0.021594402592070735\n(50, 2) 0.021607212366280132\n(50, 3) 0.021588981871920144\n(150, 0) 0.019330200878118254\n(150, 0.5) 0.019851736779115812\n(150, 1) 0.02024065456741478\n(150, 1.5) 0.020357687250859974\n(150, 2) 0.02055523984184607\nBEST (50, 2)\n", "stderr": "", "aggregated_output": "batch 0 kept 503150\nbatch 1 kept 488738\nbatch 2 kept 480923\nbatch 3 kept 488970\nbatch 4 kept 520993\nbatch 5 kept 510031\nbatch 6 kept 517318\nbatch 7 kept 499099\nbatch 8 kept 478682\nbatch 9 kept 516230\nbatch 10 kept 518257\nbatch 11 kept 537077\nbatch 12 kept 503534\nbatch 13 kept 537603\nbatch 14 kept 513441\nbatch 15 kept 470942\nbatch 16 kept 487415\nbatch 17 kept 524025\nbatch 18 kept 526687\nbatch 19 kept 532221\nbatch 20 kept 514721\nbatch 21 kept 480212\nbatch 22 kept 488199\nbatch 23 kept 538575\nbatch 24 kept 506675\nbatch 25 kept 490243\nbatch 26 kept 490872\nbatch 27 kept 508536\nbatch 28 kept 516455\nbatch 29 kept 515074\nbatch 30 kept 512849\nbatch 31 kept 495220\nbatch 32 kept 498879\nbatch 33 kept 515499\nbatch 34 kept 509523\nbatch 35 kept 506148\nbatch 36 kept 516821\nbatch 37 kept 513611\nbatch 38 kept 530375\nbatch 39 kept 252506\nR (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0) 0.01949490507941361\n(50, 0.25) 0.01999415342787708\n(50, 0.5) 0.020361720316188454\n(50, 0.75) 0.02058260619138934\n(50, 1.0) 0.021148150938599196\n(50, 1.5) 0.021594402592070735\n(50, 2) 0.021607212366280132\n(50, 3) 0.021588981871920144\n(150, 0) 0.019330200878118254\n(150, 0.5) 0.019851736779115812\n(150, 1) 0.02024065456741478\n(150, 1.5) 0.020357687250859974\n(150, 2) 0.02055523984184607\nBEST (50, 2)\n", "exit_code": 0, "duration": { "secs": 66, "nanos": 321225174 }, "formatted_output": "batch 0 kept 503150\nbatch 1 kept 488738\nbatch 2 kept 480923\nbatch 3 kept 488970\nbatch 4 kept 520993\nbatch 5 kept 510031\nbatch 6 kept 517318\nbatch 7 kept 499099\nbatch 8 kept 478682\nbatch 9 kept 516230\nbatch 10 kept 518257\nbatch 11 kept 537077\nbatch 12 kept 503534\nbatch 13 kept 537603\nbatch 14 kept 513441\nbatch 15 kept 470942\nbatch 16 kept 487415\nbatch 17 kept 524025\nbatch 18 kept 526687\nbatch 19 kept 532221\nbatch 20 kept 514721\nbatch 21 kept 480212\nbatch 22 kept 488199\nbatch 23 kept 538575\nbatch 24 kept 506675\nbatch 25 kept 490243\nbatch 26 kept 490872\nbatch 27 kept 508536\nbatch 28 kept 516455\nbatch 29 kept 515074\nbatch 30 kept 512849\nbatch 31 kept 495220\nbatch 32 kept 498879\nbatch 33 kept 515499\nbatch 34 kept 509523\nbatch 35 kept 506148\nbatch 36 kept 516821\nbatch 37 kept 513611\nbatch 38 kept 530375\nbatch 39 kept 252506\nR (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0) 0.01949490507941361\n(50, 0.25) 0.01999415342787708\n(50, 0.5) 0.020361720316188454\n(50, 0.75) 0.02058260619138934\n(50, 1.0) 0.021148150938599196\n(50, 1.5) 0.021594402592070735\n(50, 2) 0.021607212366280132\n(50, 3) 0.021588981871920144\n(150, 0) 0.019330200878118254\n(150, 0.5) 0.019851736779115812\n(150, 1) 0.02024065456741478\n(150, 1.5) 0.020357687250859974\n(150, 2) 0.02055523984184607\nBEST (50, 2)\n" } } ## Step 32 · agent · 2026-09-23T21:50:46.734Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-09-23T21:50:58.681Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:12.444Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/cf_validate.py\\n@@\\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.argsort(glob,kind='stable')]=np.arange(nitems,dtype=np.float32)/nitems\\n+f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy()\\n+# Percentile with the catalog popularity order as the deterministic tie-break.\\n+globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\\n@@\\n-configs=[(50,b) for b in (0,.25,.5,.75,1.0,1.5,2,3)]+[(150,b) for b in (0,.5,1,1.5,2)]\\n+configs=[(50,b,d) for d in (0,.2,.4) for b in (.5,1,2,4,8,16,32)]\\n@@\\n- for K in sorted(set(k for k,b in configs)):\\n+ for K in sorted(set(k for k,b,d in configs)):\\n@@\\n- for beta in [b for k,b in configs if k==K]:\\n- sc=pred+beta*globrank[None,:]\\n- out=outs[(K,beta)]\\n+ df=np.asarray((R>0).sum(axis=0)).ravel()\\n+ for _,beta,debiased in [c for c in configs if c[0]==K]:\\n+ sc=pred/np.maximum(df[None,:],1)**debiased+beta*globrank[None,:]\\n+ out=outs[(K,beta,debiased)]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:51:12.547Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-912e1c46-6f59-469e-b9ee-a8c093818d6b", "changes": { "/app/solution/cf_validate.py": { "type": "update", "unified_diff": "@@ -50,3 +50,5 @@\n # best global trend from development: 7-day organic likes\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.argsort(glob,kind='stable')]=np.arange(nitems,dtype=np.float32)/nitems\n+f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy()\n+# Percentile with the catalog popularity order as the deterministic tie-break.\n+globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\n gold=truth()\n@@ -59,3 +61,3 @@\n \n-configs=[(50,b) for b in (0,.25,.5,.75,1.0,1.5,2,3)]+[(150,b) for b in (0,.5,1,1.5,2)]\n+configs=[(50,b,d) for d in (0,.2,.4) for b in (.5,1,2,4,8,16,32)]\n dcg={c:0. for c in configs}; outs={c:np.empty((len(eu),100),np.int32) for c in configs}\n@@ -66,3 +68,3 @@\n # weak/negative cosine matches are useless\n- for K in sorted(set(k for k,b in configs)):\n+ for K in sorted(set(k for k,b,d in configs)):\n kth=np.argpartition(sim,-K,axis=1)[:,-K:]; ss=np.zeros_like(sim); np.put_along_axis(ss,kth,np.take_along_axis(sim,kth,axis=1),axis=1)\n@@ -70,5 +72,6 @@\n pred=np.log1p(5*pred/np.maximum(ss.sum(axis=1,keepdims=True),1e-8))\n- for beta in [b for k,b in configs if k==K]:\n- sc=pred+beta*globrank[None,:]\n- out=outs[(K,beta)]\n+ df=np.asarray((R>0).sum(axis=0)).ravel()\n+ for _,beta,debiased in [c for c in configs if c[0]==K]:\n+ sc=pred/np.maximum(df[None,:],1)**debiased+beta*globrank[None,:]\n+ out=outs[(K,beta,debiased)]\n for j in range(en-st):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/cf_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab449a4c6c487d2b62c5fe223589b27", "usage": { "input_tokens": 33619, "cached_input_tokens": 32512, "cache_write_input_tokens": 0, "output_tokens": 1322, "reasoning_output_tokens": 883, "total_tokens": 34941 }, "turn_token_usage": { "input_tokens": 575581, "cached_input_tokens": 532864, "cache_write_input_tokens": 0, "output_tokens": 11360, "reasoning_output_tokens": 4338, "total_tokens": 586941 }, "thread_token_usage": { "input_tokens": 575581, "cached_input_tokens": 532864, "cache_write_input_tokens": 0, "output_tokens": 11360, "reasoning_output_tokens": 4338, "total_tokens": 586941 } } ## Step 34 · agent · 2026-09-23T21:51:14.583Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:16.399Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/cf_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2500});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:51:46.588Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"9a1c30","wall_time_seconds":30.000560134,"session_id":40698,"original_token_count":187,"output":"R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab449d0dc0c87d280d73866036ff218", "usage": { "input_tokens": 34965, "cached_input_tokens": 33408, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 13, "total_tokens": 35044 }, "turn_token_usage": { "input_tokens": 610546, "cached_input_tokens": 566272, "cache_write_input_tokens": 0, "output_tokens": 11439, "reasoning_output_tokens": 4351, "total_tokens": 621985 }, "thread_token_usage": { "input_tokens": 610546, "cached_input_tokens": 566272, "cache_write_input_tokens": 0, "output_tokens": 11439, "reasoning_output_tokens": 4351, "total_tokens": 621985 } } ## Step 35 · agent · 2026-09-23T21:51:48.230Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:49.522Z { "code": "const r = await tools.write_stdin({\"session_id\":40698,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:52:19.531Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"4facfd","wall_time_seconds":30.00043969,"session_id":40698,"original_token_count":226,"output":"score batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab449f2c48887d2a042a9c494e36479", "usage": { "input_tokens": 35401, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 35455 }, "turn_token_usage": { "input_tokens": 645947, "cached_input_tokens": 601088, "cache_write_input_tokens": 0, "output_tokens": 11493, "reasoning_output_tokens": 4357, "total_tokens": 657440 }, "thread_token_usage": { "input_tokens": 645947, "cached_input_tokens": 601088, "cache_write_input_tokens": 0, "output_tokens": 11493, "reasoning_output_tokens": 4357, "total_tokens": 657440 } } ## Step 36 · agent · 2026-09-23T21:52:21.688Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:22.895Z { "code": "const r = await tools.write_stdin({\"session_id\":40698,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:52:31.975Z Script completed Wall time 9.1 seconds Output: {"chunk_id":"9b3f1a","wall_time_seconds":9.07118067,"exit_code":0,"original_token_count":258,"output":"score batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a13c1f087d2bc7cade9e75f6818", "usage": { "input_tokens": 35880, "cached_input_tokens": 35200, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 35934 }, "turn_token_usage": { "input_tokens": 681827, "cached_input_tokens": 636288, "cache_write_input_tokens": 0, "output_tokens": 11547, "reasoning_output_tokens": 4363, "total_tokens": 693374 }, "thread_token_usage": { "input_tokens": 681827, "cached_input_tokens": 636288, "cache_write_input_tokens": 0, "output_tokens": 11547, "reasoning_output_tokens": 4363, "total_tokens": 693374 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-88d6b630-949f-4551-aab1-fa2ce079a967", "process_id": "40698", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/cf_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/cf_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\n", "stderr": "", "aggregated_output": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\n", "exit_code": 0, "duration": { "secs": 75, "nanos": 386833496 }, "formatted_output": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\n" } } ## Step 37 · agent · 2026-09-23T21:52:34.084Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:36.008Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_cf.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:52:38.622Z Script completed Wall time 2.6 seconds Output: { "model": "/app/solution/validation_cf.parquet", "scores": { "quality_ndcg@10": 0.038246, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a20270087d2948dd15ad5aedaf9", "usage": { "input_tokens": 36564, "cached_input_tokens": 27648, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 13, "total_tokens": 36647 }, "turn_token_usage": { "input_tokens": 718391, "cached_input_tokens": 663936, "cache_write_input_tokens": 0, "output_tokens": 11630, "reasoning_output_tokens": 4376, "total_tokens": 730021 }, "thread_token_usage": { "input_tokens": 718391, "cached_input_tokens": 663936, "cache_write_input_tokens": 0, "output_tokens": 11630, "reasoning_output_tokens": 4376, "total_tokens": 730021 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b98b693-e540-4047-85a5-fcbf596fbe6f", "process_id": "12868", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_cf.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_cf.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038246,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038246,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 431893859 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.038246,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 38 · agent · 2026-09-23T21:52:49.270Z ### Thinking unavailable — no text recorded The corrected blend now reaches public NDCG@10 = 0.038246. The gain comes from debiased neighbor affinity: raw neighbor voting over-recommends universally popular tracks, while a modest item-frequency correction lets personalization complement (rather than duplicate) seven-day momentum. I’m testing one final latent-factor variant before freezing the architecture and refitting through the validation cutoff. ### exec · 2026-09-23T21:53:23.408Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/svd_validate.py\\n+exec(open('/app/solution/cf_validate.py').read().split('R=build_matrix()')[0])\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+R=build_matrix(); df=np.asarray((R>0).sum(axis=0)).ravel()\\n+idf=np.log1p(10000/np.maximum(df,1))**.5\\n+A=R@sparse.diags(idf)\\n+rn=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel()); A=sparse.diags(1/np.maximum(rn,1e-8))@A\\n+print('fitting svd',flush=True)\\n+svd=TruncatedSVD(n_components=128,n_iter=4,random_state=2026)\\n+U=svd.fit_transform(A); V=svd.components_; print('explained',svd.explained_variance_ratio_.sum(),flush=True)\\n+with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\\n+f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\\n+gold=truth(); urow={int(u):r for r,u in enumerate(eu)}; gold={(u,i):g for (u,i),g in gold.items() if u in urow and i in imap.index and ((ep[urow[u],int(imap[i])//8]>>(int(imap[i])%8))&1)}\\n+discounts=1/np.log2(np.arange(2,12)); idcg={}\\n+for (u,i),g in gold.items(): idcg.setdefault(u,[]).append(g)\\n+idcg={u:np.sum((2**np.sort(gs)[::-1][:10]-1)*discounts[:min(10,len(gs))]) for u,gs in idcg.items()}\\n+configs=[(b,d) for d in (0,.25,.5,.75,1) for b in (.25,.5,1,2,4,8,16)]\\n+outs={c:np.empty((len(eu),100),np.int32) for c in configs}\\n+bs=128\\n+for st in range(0,len(eu),bs):\\n+ en=min(len(eu),st+bs); rows=(eu[st:en].astype(np.int64)//100-1).astype(int); pred=U[rows]@V\\n+ pred=np.maximum(pred,0)\\n+ for beta,d in configs:\\n+ # idf correction/decorrection exponent plus popularity blend\\n+ sc=pred/np.maximum(idf[None,:],1e-5)**d+beta*globrank[None,:]\\n+ for j in range(en-st):\\n+ eligible=np.unpackbits(ep[st+j],count=nitems,bitorder='little').astype(bool); sc[j,~eligible]=-1e20\\n+ ix=np.argpartition(sc[j],-100)[-100:]; ix=ix[np.argsort(sc[j,ix])[::-1]]; outs[(beta,d)][st+j]=ix\\n+ print(st,en,flush=True)\\n+vals={}\\n+for cfg,out in outs.items():\\n+ s=0\\n+ for r,u0 in enumerate(eu):\\n+ u=int(u0)\\n+ if u not in idcg: continue\\n+ gains=np.array([gold.get((u,int(items[i])),0) for i in out[r,:10]])\\n+ s+=np.sum((2**gains-1)*discounts)/idcg[u]\\n+ vals[cfg]=s/len(idcg); print(cfg,vals[cfg])\\n+best=max(vals,key=vals.get); print('BEST',best,vals[best]); out=outs[best]\\n+pd.DataFrame({'uid':np.repeat(eu,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(eu))}).to_parquet(O/'validation_svd.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:53:23.520Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-291eba40-bae8-4729-a9ab-9863f42c812f", "changes": { "/app/solution/svd_validate.py": { "type": "add", "content": "exec(open('/app/solution/cf_validate.py').read().split('R=build_matrix()')[0])\nfrom sklearn.decomposition import TruncatedSVD\n\nR=build_matrix(); df=np.asarray((R>0).sum(axis=0)).ravel()\nidf=np.log1p(10000/np.maximum(df,1))**.5\nA=R@sparse.diags(idf)\nrn=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel()); A=sparse.diags(1/np.maximum(rn,1e-8))@A\nprint('fitting svd',flush=True)\nsvd=TruncatedSVD(n_components=128,n_iter=4,random_state=2026)\nU=svd.fit_transform(A); V=svd.components_; print('explained',svd.explained_variance_ratio_.sum(),flush=True)\nwith np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\nf=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\ngold=truth(); urow={int(u):r for r,u in enumerate(eu)}; gold={(u,i):g for (u,i),g in gold.items() if u in urow and i in imap.index and ((ep[urow[u],int(imap[i])//8]>>(int(imap[i])%8))&1)}\ndiscounts=1/np.log2(np.arange(2,12)); idcg={}\nfor (u,i),g in gold.items(): idcg.setdefault(u,[]).append(g)\nidcg={u:np.sum((2**np.sort(gs)[::-1][:10]-1)*discounts[:min(10,len(gs))]) for u,gs in idcg.items()}\nconfigs=[(b,d) for d in (0,.25,.5,.75,1) for b in (.25,.5,1,2,4,8,16)]\nouts={c:np.empty((len(eu),100),np.int32) for c in configs}\nbs=128\nfor st in range(0,len(eu),bs):\n en=min(len(eu),st+bs); rows=(eu[st:en].astype(np.int64)//100-1).astype(int); pred=U[rows]@V\n pred=np.maximum(pred,0)\n for beta,d in configs:\n # idf correction/decorrection exponent plus popularity blend\n sc=pred/np.maximum(idf[None,:],1e-5)**d+beta*globrank[None,:]\n for j in range(en-st):\n eligible=np.unpackbits(ep[st+j],count=nitems,bitorder='little').astype(bool); sc[j,~eligible]=-1e20\n ix=np.argpartition(sc[j],-100)[-100:]; ix=ix[np.argsort(sc[j,ix])[::-1]]; outs[(beta,d)][st+j]=ix\n print(st,en,flush=True)\nvals={}\nfor cfg,out in outs.items():\n s=0\n for r,u0 in enumerate(eu):\n u=int(u0)\n if u not in idcg: continue\n gains=np.array([gold.get((u,int(items[i])),0) for i in out[r,:10]])\n s+=np.sum((2**gains-1)*discounts)/idcg[u]\n vals[cfg]=s/len(idcg); print(cfg,vals[cfg])\nbest=max(vals,key=vals.get); print('BEST',best,vals[best]); out=outs[best]\npd.DataFrame({'uid':np.repeat(eu,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(eu))}).to_parquet(O/'validation_svd.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/svd_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a26d44087d28c2e808fbf481afa", "usage": { "input_tokens": 36716, "cached_input_tokens": 35712, "cache_write_input_tokens": 0, "output_tokens": 1350, "reasoning_output_tokens": 279, "total_tokens": 38066 }, "turn_token_usage": { "input_tokens": 755107, "cached_input_tokens": 699648, "cache_write_input_tokens": 0, "output_tokens": 12980, "reasoning_output_tokens": 4655, "total_tokens": 768087 }, "thread_token_usage": { "input_tokens": 755107, "cached_input_tokens": 699648, "cache_write_input_tokens": 0, "output_tokens": 12980, "reasoning_output_tokens": 4655, "total_tokens": 768087 } } ## Step 39 · agent · 2026-09-23T21:53:25.894Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:53:27.597Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/svd_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:53:57.786Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"20b99b","wall_time_seconds":30.001458095,"session_id":39989,"original_token_count":47,"output":"fitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a53c20487d2983d91accd7329d3", "usage": { "input_tokens": 38090, "cached_input_tokens": 36480, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 22, "total_tokens": 38178 }, "turn_token_usage": { "input_tokens": 793197, "cached_input_tokens": 736128, "cache_write_input_tokens": 0, "output_tokens": 13068, "reasoning_output_tokens": 4677, "total_tokens": 806265 }, "thread_token_usage": { "input_tokens": 793197, "cached_input_tokens": 736128, "cache_write_input_tokens": 0, "output_tokens": 13068, "reasoning_output_tokens": 4677, "total_tokens": 806265 } } ## Step 40 · agent · 2026-09-23T21:54:00.110Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:01.390Z { "code": "const r = await tools.write_stdin({\"session_id\":39989,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:54:31.398Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"50fcb5","wall_time_seconds":30.000610709,"session_id":39989,"original_token_count":100,"output":"2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a7600f487d2831794d643b9e22d", "usage": { "input_tokens": 38333, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 38387 }, "turn_token_usage": { "input_tokens": 831530, "cached_input_tokens": 774016, "cache_write_input_tokens": 0, "output_tokens": 13122, "reasoning_output_tokens": 4683, "total_tokens": 844652 }, "thread_token_usage": { "input_tokens": 831530, "cached_input_tokens": 774016, "cache_write_input_tokens": 0, "output_tokens": 13122, "reasoning_output_tokens": 4683, "total_tokens": 844652 } } ## Step 41 · agent · 2026-09-23T21:54:33.714Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:34.983Z { "code": "const r = await tools.write_stdin({\"session_id\":39989,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:54:41.381Z Script completed Wall time 6.4 seconds Output: {"chunk_id":"972ab2","wall_time_seconds":6.366636103,"exit_code":0,"original_token_count":296,"output":"7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44a979c9087d2a34cb29936cebbca", "usage": { "input_tokens": 38684, "cached_input_tokens": 38144, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 16, "total_tokens": 38748 }, "turn_token_usage": { "input_tokens": 870214, "cached_input_tokens": 812160, "cache_write_input_tokens": 0, "output_tokens": 13186, "reasoning_output_tokens": 4699, "total_tokens": 883400 }, "thread_token_usage": { "input_tokens": 870214, "cached_input_tokens": 812160, "cache_write_input_tokens": 0, "output_tokens": 13186, "reasoning_output_tokens": 4699, "total_tokens": 883400 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4da6802-4f07-4caf-a46a-c735ae13d78d", "process_id": "39989", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/svd_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/svd_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n", "stderr": "", "aggregated_output": "fitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n", "exit_code": 0, "duration": { "secs": 73, "nanos": 592333024 }, "formatted_output": "fitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n" } } ## Step 42 · agent · 2026-09-23T21:54:48.450Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:59.153Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/fuse_validate.py\\n+from pathlib import Path\\n+import pandas as pd\\n+import numpy as np\\n+O=Path('/app/solution')\\n+a=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\\n+for w in (.15,.3,.5,.75,1,1.5,2,3):\\n+ rows=[]\\n+ for u,x in a.groupby('uid',sort=False):\\n+ ar=dict(zip(x.item_id,x['rank'])); y=b[b.uid==u]; br=dict(zip(y.item_id,y['rank'])); z=g[g.uid==u]; gr=dict(zip(z.item_id,z['rank']))\\n+ union=set(ar)|set(br)|set(gr)\\n+ # rank-based fusion; missing candidates receive a conservative tail rank\\n+ score={i:-ar.get(i,130)-w*br.get(i,130)-.15*gr.get(i,130) for i in union}\\n+ pick=sorted(union,key=score.get,reverse=True)[:100]\\n+ rows.extend((u,i,r) for r,i in enumerate(pick,1))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_fuse_{w}.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:54:59.197Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-92700d46-1674-4714-9f51-3333e6f6eb9b", "changes": { "/app/solution/fuse_validate.py": { "type": "add", "content": "from pathlib import Path\nimport pandas as pd\nimport numpy as np\nO=Path('/app/solution')\na=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\nfor w in (.15,.3,.5,.75,1,1.5,2,3):\n rows=[]\n for u,x in a.groupby('uid',sort=False):\n ar=dict(zip(x.item_id,x['rank'])); y=b[b.uid==u]; br=dict(zip(y.item_id,y['rank'])); z=g[g.uid==u]; gr=dict(zip(z.item_id,z['rank']))\n union=set(ar)|set(br)|set(gr)\n # rank-based fusion; missing candidates receive a conservative tail rank\n score={i:-ar.get(i,130)-w*br.get(i,130)-.15*gr.get(i,130) for i in union}\n pick=sorted(union,key=score.get,reverse=True)[:100]\n rows.extend((u,i,r) for r,i in enumerate(pick,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_fuse_{w}.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/fuse_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44aa1937087d290726e95969d6795", "usage": { "input_tokens": 39508, "cached_input_tokens": 36352, "cache_write_input_tokens": 0, "output_tokens": 520, "reasoning_output_tokens": 175, "total_tokens": 40028 }, "turn_token_usage": { "input_tokens": 909722, "cached_input_tokens": 848512, "cache_write_input_tokens": 0, "output_tokens": 13706, "reasoning_output_tokens": 4874, "total_tokens": 923428 }, "thread_token_usage": { "input_tokens": 909722, "cached_input_tokens": 848512, "cache_write_input_tokens": 0, "output_tokens": 13706, "reasoning_output_tokens": 4874, "total_tokens": 923428 } } ## Step 43 · agent · 2026-09-23T21:55:02.230Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:55:10.334Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/fuse_validate.py\\n@@\\n-a=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\\n+a=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\\n+bg={u:x for u,x in b.groupby('uid',sort=False)}; gg={u:x for u,x in g.groupby('uid',sort=False)}\\n@@\\n- ar=dict(zip(x.item_id,x['rank'])); y=b[b.uid==u]; br=dict(zip(y.item_id,y['rank'])); z=g[g.uid==u]; gr=dict(zip(z.item_id,z['rank']))\\n+ ar=dict(zip(x.item_id,x['rank'])); y=bg[u]; br=dict(zip(y.item_id,y['rank'])); z=gg[u]; gr=dict(zip(z.item_id,z['rank']))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:55:10.402Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7a77b87d-8d1e-44df-bc1e-d439a8fad278", "changes": { "/app/solution/fuse_validate.py": { "type": "update", "unified_diff": "@@ -5,2 +5,3 @@\n a=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\n+bg={u:x for u,x in b.groupby('uid',sort=False)}; gg={u:x for u,x in g.groupby('uid',sort=False)}\n for w in (.15,.3,.5,.75,1,1.5,2,3):\n@@ -8,3 +9,3 @@\n for u,x in a.groupby('uid',sort=False):\n- ar=dict(zip(x.item_id,x['rank'])); y=b[b.uid==u]; br=dict(zip(y.item_id,y['rank'])); z=g[g.uid==u]; gr=dict(zip(z.item_id,z['rank']))\n+ ar=dict(zip(x.item_id,x['rank'])); y=bg[u]; br=dict(zip(y.item_id,y['rank'])); z=gg[u]; gr=dict(zip(z.item_id,z['rank']))\n union=set(ar)|set(br)|set(gr)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/fuse_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44ab36fd887d28cf0379dedc4cb74", "usage": { "input_tokens": 40052, "cached_input_tokens": 38528, "cache_write_input_tokens": 0, "output_tokens": 285, "reasoning_output_tokens": 23, "total_tokens": 40337 }, "turn_token_usage": { "input_tokens": 949774, "cached_input_tokens": 887040, "cache_write_input_tokens": 0, "output_tokens": 13991, "reasoning_output_tokens": 4897, "total_tokens": 963765 }, "thread_token_usage": { "input_tokens": 949774, "cached_input_tokens": 887040, "cache_write_input_tokens": 0, "output_tokens": 13991, "reasoning_output_tokens": 4897, "total_tokens": 963765 } } ### exec · 2026-09-23T21:55:15.263Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:55:45.431Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"ffc792","wall_time_seconds":30.001031134,"session_id":84075,"original_token_count":53,"output":"validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038386,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.03901,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.039374,\nvalidation_fuse_0.75.parquet "} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44abebca887d2a8a6ded9815380f9", "usage": { "input_tokens": 40361, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 0, "total_tokens": 40474 }, "turn_token_usage": { "input_tokens": 990135, "cached_input_tokens": 926336, "cache_write_input_tokens": 0, "output_tokens": 14104, "reasoning_output_tokens": 4897, "total_tokens": 1004239 }, "thread_token_usage": { "input_tokens": 990135, "cached_input_tokens": 926336, "cache_write_input_tokens": 0, "output_tokens": 14104, "reasoning_output_tokens": 4897, "total_tokens": 1004239 } } ### exec · 2026-09-23T21:55:48.113Z { "code": "const r = await tools.write_stdin({\"session_id\":84075,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:55:57.922Z Script completed Wall time 9.8 seconds Output: {"chunk_id":"78145b","wall_time_seconds":9.799973756,"exit_code":0,"original_token_count":68,"output":" \"quality_ndcg@10\": 0.040009,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.039652,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.039725,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.0394,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.039527,\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44ae1b34c87d2a888bfc68cf4731b", "usage": { "input_tokens": 40610, "cached_input_tokens": 40192, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 40656 }, "turn_token_usage": { "input_tokens": 1030745, "cached_input_tokens": 966528, "cache_write_input_tokens": 0, "output_tokens": 14150, "reasoning_output_tokens": 4897, "total_tokens": 1044895 }, "thread_token_usage": { "input_tokens": 1030745, "cached_input_tokens": 966528, "cache_write_input_tokens": 0, "output_tokens": 14150, "reasoning_output_tokens": 4897, "total_tokens": 1044895 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2b806dd0-7944-412b-a74e-ea05f2f71acc", "process_id": "84075", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038386,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.03901,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.039374,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.040009,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.039652,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.039725,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.0394,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.039527,\n", "stderr": "", "aggregated_output": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038386,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.03901,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.039374,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.040009,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.039652,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.039725,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.0394,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.039527,\n", "exit_code": 0, "duration": { "secs": 42, "nanos": 490562512 }, "formatted_output": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038386,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.03901,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.039374,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.040009,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.039652,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.039725,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.0394,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.039527,\n" } } ## Step 44 · agent · 2026-09-23T21:56:01.994Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:56:05.993Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor stem in ['train_listens','val_listens','train_likes','val_likes']:\\n x=pd.read_parquet(D/(stem+'.parquet'),columns=['timestamp'])\\n print(stem,x.timestamp.min(),x.timestamp.max())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:56:07.247Z Script completed Wall time 1.2 seconds Output: train_listens 0 24786795 val_listens 24788600 25393395 train_likes 80 24786790 val_likes 24788815 25393390 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44aee255087d28785cb6e29ea09f0", "usage": { "input_tokens": 40816, "cached_input_tokens": 40448, "cache_write_input_tokens": 0, "output_tokens": 216, "reasoning_output_tokens": 82, "total_tokens": 41032 }, "turn_token_usage": { "input_tokens": 1071561, "cached_input_tokens": 1006976, "cache_write_input_tokens": 0, "output_tokens": 14366, "reasoning_output_tokens": 4979, "total_tokens": 1085927 }, "thread_token_usage": { "input_tokens": 1071561, "cached_input_tokens": 1006976, "cache_write_input_tokens": 0, "output_tokens": 14366, "reasoning_output_tokens": 4979, "total_tokens": 1085927 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5d082f0-dba6-4a7b-aa67-38454441d998", "process_id": "57096", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\nD=Path('/task/data')\nfor stem in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/(stem+'.parquet'),columns=['timestamp'])\n print(stem,x.timestamp.min(),x.timestamp.max())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\nD=Path('/task/data')\nfor stem in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/(stem+'.parquet'),columns=['timestamp'])\n print(stem,x.timestamp.min(),x.timestamp.max())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens 0 24786795\nval_listens 24788600 25393395\ntrain_likes 80 24786790\nval_likes 24788815 25393390\n", "stderr": "", "aggregated_output": "train_listens 0 24786795\nval_listens 24788600 25393395\ntrain_likes 80 24786790\nval_likes 24788815 25393390\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 59967484 }, "formatted_output": "train_listens 0 24786795\nval_listens 24788600 25393395\ntrain_likes 80 24786790\nval_likes 24788815 25393390\n" } } ## Step 45 · agent · 2026-09-23T21:56:21.259Z ### Thinking unavailable — no text recorded ## Step 46 · agent · 2026-09-23T21:56:24.075Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:56:31.700Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data'; s=pd.read_parquet(D+'/validation_preference_state.parquet'); z=np.load(D+'/validation_eligible_candidates.npz'); ui=pd.Series(np.arange(len(z['uids'])),index=z['uids']); ii=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids']);\\nr=ui.reindex(s.uid).to_numpy(); c=ii.reindex(s.item_id).to_numpy(); ok=~np.isnan(r)&~np.isnan(c); el=np.zeros(len(s),bool); el[ok]=((z['packed_eligible'][r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1).astype(bool)\\nprint(s.groupby('state').size()); print(pd.crosstab(s.state,el,normalize='index'))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:56:32.718Z Script completed Wall time 1.0 seconds Output: state disliked 50689 liked 418435 dtype: int64 col_0 False state disliked 1.0 liked 1.0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44af78e5887d286e35b6e354f6e04", "usage": { "input_tokens": 41099, "cached_input_tokens": 40576, "cache_write_input_tokens": 0, "output_tokens": 724, "reasoning_output_tokens": 476, "total_tokens": 41823 }, "turn_token_usage": { "input_tokens": 1112660, "cached_input_tokens": 1047552, "cache_write_input_tokens": 0, "output_tokens": 15090, "reasoning_output_tokens": 5455, "total_tokens": 1127750 }, "thread_token_usage": { "input_tokens": 1112660, "cached_input_tokens": 1047552, "cache_write_input_tokens": 0, "output_tokens": 15090, "reasoning_output_tokens": 5455, "total_tokens": 1127750 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-21dcc27a-95a6-43d5-a782-63b5e9c5e244", "process_id": "3767", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; s=pd.read_parquet(D+'/validation_preference_state.parquet'); z=np.load(D+'/validation_eligible_candidates.npz'); ui=pd.Series(np.arange(len(z['uids'])),index=z['uids']); ii=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids']);\nr=ui.reindex(s.uid).to_numpy(); c=ii.reindex(s.item_id).to_numpy(); ok=~np.isnan(r)&~np.isnan(c); el=np.zeros(len(s),bool); el[ok]=((z['packed_eligible'][r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1).astype(bool)\nprint(s.groupby('state').size()); print(pd.crosstab(s.state,el,normalize='index'))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; s=pd.read_parquet(D+'/validation_preference_state.parquet'); z=np.load(D+'/validation_eligible_candidates.npz'); ui=pd.Series(np.arange(len(z['uids'])),index=z['uids']); ii=pd.Series(np.arange(len(z['item_ids'])),index=z['item_ids']);\nr=ui.reindex(s.uid).to_numpy(); c=ii.reindex(s.item_id).to_numpy(); ok=~np.isnan(r)&~np.isnan(c); el=np.zeros(len(s),bool); el[ok]=((z['packed_eligible'][r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1).astype(bool)\nprint(s.groupby('state').size()); print(pd.crosstab(s.state,el,normalize='index'))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "state\ndisliked 50689\nliked 418435\ndtype: int64\ncol_0 False\nstate \ndisliked 1.0\nliked 1.0\n", "stderr": "", "aggregated_output": "state\ndisliked 50689\nliked 418435\ndtype: int64\ncol_0 False\nstate \ndisliked 1.0\nliked 1.0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 831340395 }, "formatted_output": "state\ndisliked 50689\nliked 418435\ndtype: int64\ncol_0 False\nstate \ndisliked 1.0\nliked 1.0\n" } } ## Step 47 · agent · 2026-09-23T21:56:39.404Z ### Thinking unavailable — no text recorded Rank fusion improves the public score again to 0.040009 by combining the complementary neighborhood and 128-factor latent models. Active explicit likes/dislikes are entirely removed by platform eligibility at this cutoff, so explicit state is already enforced through the supplied masks; I’m now refitting both models on train+validation and using validation-window organic-like momentum for the hidden horizon. ### exec · 2026-09-23T21:57:41.578Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build the final discovery ranking from train+validation history.\\n+\\n+The model combines frequency-debiased user-neighbor CF, TF-IDF PureSVD, and\\n+seven-day organic-like momentum, then applies the test eligibility mask.\\n+\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\\n+CUTOFF=25393400\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); nitems=len(items)\\n+imap=pd.Series(np.arange(nitems,dtype=np.int32),index=items)\\n+\\n+def add_listens(stem, rr, cc, vv):\\n+ pf=pq.ParquetFile(D/f'{stem}_listens.parquet')\\n+ for b in pf.iter_batches(batch_size=1_100_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\\n+ x=b.to_pandas(); x=x[x.played_ratio_pct>=50]\\n+ col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\\n+ row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32)\\n+ age=(CUTOFF-x.timestamp.to_numpy())/86400\\n+ val=((.35+.65*np.exp(-age/90))*np.where(x.played_ratio_pct.to_numpy()>=80,1,.35)*np.where(x.is_organic.to_numpy()==1,1,.6)).astype(np.float32)\\n+ key=row.astype(np.int64)*nitems+col; uq,inv=np.unique(key,return_inverse=True)\\n+ rr.append((uq//nitems).astype(np.int32)); cc.append((uq%nitems).astype(np.int32)); vv.append(np.bincount(inv,weights=val).astype(np.float32))\\n+\\n+def build_matrix():\\n+ cache=O/'R_final.npz'\\n+ if cache.exists(): return sparse.load_npz(cache)\\n+ rr=[];cc=[];vv=[]\\n+ for stem in ('train','val'): add_listens(stem,rr,cc,vv)\\n+ for stem in ('train','val'):\\n+ x=pd.read_parquet(D/f'{stem}_likes.parquet')\\n+ col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\\n+ row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32); age=(CUTOFF-x.timestamp.to_numpy())/86400\\n+ val=(2*(.35+.65*np.exp(-age/120))*np.where(x.is_organic.to_numpy()==1,1,.7)).astype(np.float32)\\n+ rr.append(row);cc.append(col);vv.append(val)\\n+ R=sparse.coo_matrix((np.concatenate(vv),(np.concatenate(rr),np.concatenate(cc))),shape=(10000,nitems)).tocsr(); R.sum_duplicates(); R.data=np.log1p(R.data)\\n+ sparse.save_npz(cache,R,compressed=False); return R\\n+\\n+def momentum():\\n+ x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\\n+ vc=x.item_id.value_counts(); raw=np.zeros(nitems,np.float32); p=imap.reindex(vc.index).to_numpy(); ok=~np.isnan(p); raw[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok])\\n+ rank=np.empty(nitems,np.float32); rank[np.lexsort((-np.arange(nitems),raw))]=np.arange(nitems,dtype=np.float32)/nitems\\n+ return rank\\n+\\n+def eligible_top(sc, packed, n=100):\\n+ mask=np.unpackbits(packed,count=nitems,bitorder='little').astype(bool); sc[~mask]=-1e20\\n+ ix=np.argpartition(sc,-n)[-n:]; return ix[np.argsort(sc[ix])[::-1]]\\n+\\n+R=build_matrix(); df=np.asarray((R>0).sum(axis=0)).ravel(); trend=momentum()\\n+with np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].copy(); test_items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\\n+assert np.array_equal(items,test_items)\\n+rows=(uids.astype(np.int64)//100-1).astype(int)\\n+\\n+# Neighborhood representation and latent representation.\\n+norm=np.sqrt(np.asarray(R.multiply(R).sum(axis=1)).ravel()); P=sparse.diags(1/np.maximum(norm,1e-8))@R\\n+idf=np.log1p(10000/np.maximum(df,1))**.5; A=R@sparse.diags(idf)\\n+an=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel()); A=sparse.diags(1/np.maximum(an,1e-8))@A\\n+svd=TruncatedSVD(n_components=128,n_iter=4,random_state=2026); U=svd.fit_transform(A); V=svd.components_\\n+\\n+cf_top=np.empty((len(uids),100),np.int32); svd_top=np.empty_like(cf_top); global_top=np.empty_like(cf_top)\\n+bs=96\\n+for st in range(0,len(uids),bs):\\n+ en=min(len(uids),st+bs); ur=rows[st:en]\\n+ sim=(P[ur]@P.T).toarray(); sim[np.arange(en-st),ur]=0\\n+ ix=np.argpartition(sim,-50,axis=1)[:,-50:]; near=np.zeros_like(sim); np.put_along_axis(near,ix,np.take_along_axis(sim,ix,axis=1),axis=1)\\n+ pred=np.asarray(near@R); pred=np.log1p(5*pred/np.maximum(near.sum(axis=1,keepdims=True),1e-8))/np.maximum(df[None,:],1)**.4+16*trend[None,:]\\n+ latent=np.maximum(U[ur]@V,0)+4*trend[None,:]\\n+ for j in range(en-st):\\n+ cf_top[st+j]=eligible_top(pred[j],packed[st+j]); svd_top[st+j]=eligible_top(latent[j],packed[st+j]); global_top[st+j]=eligible_top(trend.copy(),packed[st+j])\\n+ print(f'ranked {en}/{len(uids)}',flush=True)\\n+\\n+# Validation-selected rank fusion: CF + .75*SVD + .15*global.\\n+out=np.empty_like(cf_top)\\n+for r in range(len(uids)):\\n+ ar={int(i):q for q,i in enumerate(cf_top[r],1)}; br={int(i):q for q,i in enumerate(svd_top[r],1)}; gr={int(i):q for q,i in enumerate(global_top[r],1)}\\n+ union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,130)-.75*br.get(i,130)-.15*gr.get(i,130) for i in union}\\n+ out[r]=sorted(union,key=score.get,reverse=True)[:100]\\n+submission=pd.DataFrame({'uid':np.repeat(uids,100).astype(np.int64),'item_id':items[out.ravel()].astype(np.int64),'rank':np.tile(np.arange(1,101),len(uids)).astype(np.int64)})\\n+submission.to_parquet(O/'submission.parquet',index=False)\\n+print('wrote',O/'submission.parquet',len(submission))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:57:41.677Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-01928513-d7fa-4247-a372-257519271479", "changes": { "/app/solution/build_submission.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build the final discovery ranking from train+validation history.\n\nThe model combines frequency-debiased user-neighbor CF, TF-IDF PureSVD, and\nseven-day organic-like momentum, then applies the test eligibility mask.\n\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nCUTOFF=25393400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); nitems=len(items)\nimap=pd.Series(np.arange(nitems,dtype=np.int32),index=items)\n\ndef add_listens(stem, rr, cc, vv):\n pf=pq.ParquetFile(D/f'{stem}_listens.parquet')\n for b in pf.iter_batches(batch_size=1_100_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); x=x[x.played_ratio_pct>=50]\n col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\n row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32)\n age=(CUTOFF-x.timestamp.to_numpy())/86400\n val=((.35+.65*np.exp(-age/90))*np.where(x.played_ratio_pct.to_numpy()>=80,1,.35)*np.where(x.is_organic.to_numpy()==1,1,.6)).astype(np.float32)\n key=row.astype(np.int64)*nitems+col; uq,inv=np.unique(key,return_inverse=True)\n rr.append((uq//nitems).astype(np.int32)); cc.append((uq%nitems).astype(np.int32)); vv.append(np.bincount(inv,weights=val).astype(np.float32))\n\ndef build_matrix():\n cache=O/'R_final.npz'\n if cache.exists(): return sparse.load_npz(cache)\n rr=[];cc=[];vv=[]\n for stem in ('train','val'): add_listens(stem,rr,cc,vv)\n for stem in ('train','val'):\n x=pd.read_parquet(D/f'{stem}_likes.parquet')\n col=imap.reindex(x.item_id).to_numpy(); ok=~np.isnan(col); x=x.loc[ok]; col=col[ok].astype(np.int32)\n row=(x.uid.to_numpy(dtype=np.int64)//100-1).astype(np.int32); age=(CUTOFF-x.timestamp.to_numpy())/86400\n val=(2*(.35+.65*np.exp(-age/120))*np.where(x.is_organic.to_numpy()==1,1,.7)).astype(np.float32)\n rr.append(row);cc.append(col);vv.append(val)\n R=sparse.coo_matrix((np.concatenate(vv),(np.concatenate(rr),np.concatenate(cc))),shape=(10000,nitems)).tocsr(); R.sum_duplicates(); R.data=np.log1p(R.data)\n sparse.save_npz(cache,R,compressed=False); return R\n\ndef momentum():\n x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\n vc=x.item_id.value_counts(); raw=np.zeros(nitems,np.float32); p=imap.reindex(vc.index).to_numpy(); ok=~np.isnan(p); raw[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok])\n rank=np.empty(nitems,np.float32); rank[np.lexsort((-np.arange(nitems),raw))]=np.arange(nitems,dtype=np.float32)/nitems\n return rank\n\ndef eligible_top(sc, packed, n=100):\n mask=np.unpackbits(packed,count=nitems,bitorder='little').astype(bool); sc[~mask]=-1e20\n ix=np.argpartition(sc,-n)[-n:]; return ix[np.argsort(sc[ix])[::-1]]\n\nR=build_matrix(); df=np.asarray((R>0).sum(axis=0)).ravel(); trend=momentum()\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].copy(); test_items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nassert np.array_equal(items,test_items)\nrows=(uids.astype(np.int64)//100-1).astype(int)\n\n# Neighborhood representation and latent representation.\nnorm=np.sqrt(np.asarray(R.multiply(R).sum(axis=1)).ravel()); P=sparse.diags(1/np.maximum(norm,1e-8))@R\nidf=np.log1p(10000/np.maximum(df,1))**.5; A=R@sparse.diags(idf)\nan=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel()); A=sparse.diags(1/np.maximum(an,1e-8))@A\nsvd=TruncatedSVD(n_components=128,n_iter=4,random_state=2026); U=svd.fit_transform(A); V=svd.components_\n\ncf_top=np.empty((len(uids),100),np.int32); svd_top=np.empty_like(cf_top); global_top=np.empty_like(cf_top)\nbs=96\nfor st in range(0,len(uids),bs):\n en=min(len(uids),st+bs); ur=rows[st:en]\n sim=(P[ur]@P.T).toarray(); sim[np.arange(en-st),ur]=0\n ix=np.argpartition(sim,-50,axis=1)[:,-50:]; near=np.zeros_like(sim); np.put_along_axis(near,ix,np.take_along_axis(sim,ix,axis=1),axis=1)\n pred=np.asarray(near@R); pred=np.log1p(5*pred/np.maximum(near.sum(axis=1,keepdims=True),1e-8))/np.maximum(df[None,:],1)**.4+16*trend[None,:]\n latent=np.maximum(U[ur]@V,0)+4*trend[None,:]\n for j in range(en-st):\n cf_top[st+j]=eligible_top(pred[j],packed[st+j]); svd_top[st+j]=eligible_top(latent[j],packed[st+j]); global_top[st+j]=eligible_top(trend.copy(),packed[st+j])\n print(f'ranked {en}/{len(uids)}',flush=True)\n\n# Validation-selected rank fusion: CF + .75*SVD + .15*global.\nout=np.empty_like(cf_top)\nfor r in range(len(uids)):\n ar={int(i):q for q,i in enumerate(cf_top[r],1)}; br={int(i):q for q,i in enumerate(svd_top[r],1)}; gr={int(i):q for q,i in enumerate(global_top[r],1)}\n union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,130)-.75*br.get(i,130)-.15*gr.get(i,130) for i in union}\n out[r]=sorted(union,key=score.get,reverse=True)[:100]\nsubmission=pd.DataFrame({'uid':np.repeat(uids,100).astype(np.int64),'item_id':items[out.ravel()].astype(np.int64),'rank':np.tile(np.arange(1,101),len(uids)).astype(np.int64)})\nsubmission.to_parquet(O/'submission.parquet',index=False)\nprint('wrote',O/'submission.parquet',len(submission))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_submission.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44b10f40087d2b8d73eb50ebb53e0", "usage": { "input_tokens": 41889, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 2060, "reasoning_output_tokens": 112, "total_tokens": 43949 }, "turn_token_usage": { "input_tokens": 1154549, "cached_input_tokens": 1087488, "cache_write_input_tokens": 0, "output_tokens": 17150, "reasoning_output_tokens": 5567, "total_tokens": 1171699 }, "thread_token_usage": { "input_tokens": 1154549, "cached_input_tokens": 1087488, "cache_write_input_tokens": 0, "output_tokens": 17150, "reasoning_output_tokens": 5567, "total_tokens": 1171699 } } ## Step 48 · agent · 2026-09-23T21:57:47.055Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:57:58.451Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/write_fallback.py\\n+from pathlib import Path\\n+import numpy as np, pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); pos=pd.Series(np.arange(len(items)),index=items)\\n+x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=25393400-7*86400)]; vc=x.item_id.value_counts()\\n+score=np.zeros(len(items)); p=pos.reindex(vc.index).to_numpy(); ok=~np.isnan(p); score[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok]); order=np.lexsort((np.arange(len(items)), -score))\\n+with np.load(D/'test_eligible_candidates.npz') as z: u=z['uids'].copy(); packed=z['packed_eligible'].copy(); assert np.array_equal(items,z['item_ids'])\\n+out=np.empty((len(u),100),int)\\n+for r in range(len(u)):\\n+ m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); out[r]=order[m[order]][:100]\\n+pd.DataFrame({'uid':np.repeat(u,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(u))}).to_parquet(O/'submission.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:57:58.549Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e21aa06a-e130-4c7e-9771-c382a94d65ea", "changes": { "/app/solution/write_fallback.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); pos=pd.Series(np.arange(len(items)),index=items)\nx=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=25393400-7*86400)]; vc=x.item_id.value_counts()\nscore=np.zeros(len(items)); p=pos.reindex(vc.index).to_numpy(); ok=~np.isnan(p); score[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok]); order=np.lexsort((np.arange(len(items)), -score))\nwith np.load(D/'test_eligible_candidates.npz') as z: u=z['uids'].copy(); packed=z['packed_eligible'].copy(); assert np.array_equal(items,z['item_ids'])\nout=np.empty((len(u),100),int)\nfor r in range(len(u)):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); out[r]=order[m[order]][:100]\npd.DataFrame({'uid':np.repeat(u,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(u))}).to_parquet(O/'submission.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/write_fallback.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44b56766487d2a3e379665cd2e46a", "usage": { "input_tokens": 43973, "cached_input_tokens": 41728, "cache_write_input_tokens": 0, "output_tokens": 471, "reasoning_output_tokens": 106, "total_tokens": 44444 }, "turn_token_usage": { "input_tokens": 1198522, "cached_input_tokens": 1129216, "cache_write_input_tokens": 0, "output_tokens": 17621, "reasoning_output_tokens": 5673, "total_tokens": 1216143 }, "thread_token_usage": { "input_tokens": 1198522, "cached_input_tokens": 1129216, "cache_write_input_tokens": 0, "output_tokens": 17621, "reasoning_output_tokens": 5673, "total_tokens": 1216143 } } ### exec · 2026-09-23T21:58:03.531Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/write_fallback.py && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python -u /app/solution/build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:58:33.707Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"865744","wall_time_seconds":30.001593289,"session_id":18115,"original_token_count":110,"output":"-rw-r--r-- 1 agent agent 530K Sep 23 21:58 /app/solution/submission.parquet\nranked 96/8456\nranked 192/8456\nranked 288/8456\nranked 384/8456\nranked 480/8456\nranked 576/8456\nranked 672/8456\nranked 768/8456\nranked 864/8456\nranked 960/8456\nranked 1056/8456\nranked 1152/8456\nranked 1248/8456\nranked 1344/8456\nranked 1440/8456\nranked 1536/8456\nranked 1632/8456\nranked 1728/8456\nranked 1824/8456\nranked 1920/8456\nranked 2016/8456\nranked 2112/8456\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44b66d40887d2a08ea2c5c2a8824a", "usage": { "input_tokens": 44468, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 0, "total_tokens": 44560 }, "turn_token_usage": { "input_tokens": 1242990, "cached_input_tokens": 1172992, "cache_write_input_tokens": 0, "output_tokens": 17713, "reasoning_output_tokens": 5673, "total_tokens": 1260703 }, "thread_token_usage": { "input_tokens": 1242990, "cached_input_tokens": 1172992, "cache_write_input_tokens": 0, "output_tokens": 17713, "reasoning_output_tokens": 5673, "total_tokens": 1260703 } } ## Step 49 · agent · 2026-09-23T21:58:36.185Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:58:37.306Z { "code": "const r = await tools.write_stdin({\"session_id\":18115,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T21:59:01.432Z Script completed Wall time 24.1 seconds Output: {"chunk_id":"2f45f5","wall_time_seconds":24.115762111,"exit_code":0,"original_token_count":297,"output":"ranked 2208/8456\nranked 2304/8456\nranked 2400/8456\nranked 2496/8456\nranked 2592/8456\nranked 2688/8456\nranked 2784/8456\nranked 2880/8456\nranked 2976/8456\nranked 3072/8456\nranked 3168/8456\nranked 3264/8456\nranked 3360/8456\nranked 3456/8456\nranked 3552/8456\nranked 3648/8456\nranked 3744/8456\nranked 3840/8456\nranked 3936/8456\nranked 4032/8456\nranked 4128/8456\nranked 4224/8456\nranked 4320/8456\nranked 4416/8456\nranked 4512/8456\nranked 4608/8456\nranked 4704/8456\nranked 4800/8456\nranked 4896/8456\nranked 4992/8456\nranked 5088/8456\nranked 5184/8456\nranked 5280/8456\nranked 5376/8456\nranked 5472/8456\nranked 5568/8456\nranked 5664/8456\nranked 5760/8456\nranked 5856/8456\nranked 5952/8456\nranked 6048/8456\nranked 6144/8456\nranked 6240/8456\nranked 6336/8456\nranked 6432/8456\nranked 6528/8456\nranked 6624/8456\nranked 6720/8456\nranked 6816/8456\nranked 6912/8456\nranked 7008/8456\nranked 7104/8456\nranked 7200/8456\nranked 7296/8456\nranked 7392/8456\nranked 7488/8456\nranked 7584/8456\nranked 7680/8456\nranked 7776/8456\nranked 7872/8456\nranked 7968/8456\nranked 8064/8456\nranked 8160/8456\nranked 8256/8456\nranked 8352/8456\nranked 8448/8456\nranked 8456/8456\nwrote /app/solution/submission.parquet 845600\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44b89f3b087d28fc6652c130f430d", "usage": { "input_tokens": 44832, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 26, "total_tokens": 44906 }, "turn_token_usage": { "input_tokens": 1287822, "cached_input_tokens": 1217280, "cache_write_input_tokens": 0, "output_tokens": 17787, "reasoning_output_tokens": 5699, "total_tokens": 1305609 }, "thread_token_usage": { "input_tokens": 1287822, "cached_input_tokens": 1217280, "cache_write_input_tokens": 0, "output_tokens": 17787, "reasoning_output_tokens": 5699, "total_tokens": 1305609 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-29b73383-87d5-47e9-914e-022f7ed93547", "process_id": "18115", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/write_fallback.py && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python -u /app/solution/build_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/write_fallback.py && ls -lh /app/solution/submission.parquet && /opt/task-venv/bin/python -u /app/solution/build_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 530K Sep 23 21:58 /app/solution/submission.parquet\nranked 96/8456\nranked 192/8456\nranked 288/8456\nranked 384/8456\nranked 480/8456\nranked 576/8456\nranked 672/8456\nranked 768/8456\nranked 864/8456\nranked 960/8456\nranked 1056/8456\nranked 1152/8456\nranked 1248/8456\nranked 1344/8456\nranked 1440/8456\nranked 1536/8456\nranked 1632/8456\nranked 1728/8456\nranked 1824/8456\nranked 1920/8456\nranked 2016/8456\nranked 2112/8456\nranked 2208/8456\nranked 2304/8456\nranked 2400/8456\nranked 2496/8456\nranked 2592/8456\nranked 2688/8456\nranked 2784/8456\nranked 2880/8456\nranked 2976/8456\nranked 3072/8456\nranked 3168/8456\nranked 3264/8456\nranked 3360/8456\nranked 3456/8456\nranked 3552/8456\nranked 3648/8456\nranked 3744/8456\nranked 3840/8456\nranked 3936/8456\nranked 4032/8456\nranked 4128/8456\nranked 4224/8456\nranked 4320/8456\nranked 4416/8456\nranked 4512/8456\nranked 4608/8456\nranked 4704/8456\nranked 4800/8456\nranked 4896/8456\nranked 4992/8456\nranked 5088/8456\nranked 5184/8456\nranked 5280/8456\nranked 5376/8456\nranked 5472/8456\nranked 5568/8456\nranked 5664/8456\nranked 5760/8456\nranked 5856/8456\nranked 5952/8456\nranked 6048/8456\nranked 6144/8456\nranked 6240/8456\nranked 6336/8456\nranked 6432/8456\nranked 6528/8456\nranked 6624/8456\nranked 6720/8456\nranked 6816/8456\nranked 6912/8456\nranked 7008/8456\nranked 7104/8456\nranked 7200/8456\nranked 7296/8456\nranked 7392/8456\nranked 7488/8456\nranked 7584/8456\nranked 7680/8456\nranked 7776/8456\nranked 7872/8456\nranked 7968/8456\nranked 8064/8456\nranked 8160/8456\nranked 8256/8456\nranked 8352/8456\nranked 8448/8456\nranked 8456/8456\nwrote /app/solution/submission.parquet 845600\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 530K Sep 23 21:58 /app/solution/submission.parquet\nranked 96/8456\nranked 192/8456\nranked 288/8456\nranked 384/8456\nranked 480/8456\nranked 576/8456\nranked 672/8456\nranked 768/8456\nranked 864/8456\nranked 960/8456\nranked 1056/8456\nranked 1152/8456\nranked 1248/8456\nranked 1344/8456\nranked 1440/8456\nranked 1536/8456\nranked 1632/8456\nranked 1728/8456\nranked 1824/8456\nranked 1920/8456\nranked 2016/8456\nranked 2112/8456\nranked 2208/8456\nranked 2304/8456\nranked 2400/8456\nranked 2496/8456\nranked 2592/8456\nranked 2688/8456\nranked 2784/8456\nranked 2880/8456\nranked 2976/8456\nranked 3072/8456\nranked 3168/8456\nranked 3264/8456\nranked 3360/8456\nranked 3456/8456\nranked 3552/8456\nranked 3648/8456\nranked 3744/8456\nranked 3840/8456\nranked 3936/8456\nranked 4032/8456\nranked 4128/8456\nranked 4224/8456\nranked 4320/8456\nranked 4416/8456\nranked 4512/8456\nranked 4608/8456\nranked 4704/8456\nranked 4800/8456\nranked 4896/8456\nranked 4992/8456\nranked 5088/8456\nranked 5184/8456\nranked 5280/8456\nranked 5376/8456\nranked 5472/8456\nranked 5568/8456\nranked 5664/8456\nranked 5760/8456\nranked 5856/8456\nranked 5952/8456\nranked 6048/8456\nranked 6144/8456\nranked 6240/8456\nranked 6336/8456\nranked 6432/8456\nranked 6528/8456\nranked 6624/8456\nranked 6720/8456\nranked 6816/8456\nranked 6912/8456\nranked 7008/8456\nranked 7104/8456\nranked 7200/8456\nranked 7296/8456\nranked 7392/8456\nranked 7488/8456\nranked 7584/8456\nranked 7680/8456\nranked 7776/8456\nranked 7872/8456\nranked 7968/8456\nranked 8064/8456\nranked 8160/8456\nranked 8256/8456\nranked 8352/8456\nranked 8448/8456\nranked 8456/8456\nwrote /app/solution/submission.parquet 845600\n", "exit_code": 0, "duration": { "secs": 57, "nanos": 733920324 }, "formatted_output": "-rw-r--r-- 1 agent agent 530K Sep 23 21:58 /app/solution/submission.parquet\nranked 96/8456\nranked 192/8456\nranked 288/8456\nranked 384/8456\nranked 480/8456\nranked 576/8456\nranked 672/8456\nranked 768/8456\nranked 864/8456\nranked 960/8456\nranked 1056/8456\nranked 1152/8456\nranked 1248/8456\nranked 1344/8456\nranked 1440/8456\nranked 1536/8456\nranked 1632/8456\nranked 1728/8456\nranked 1824/8456\nranked 1920/8456\nranked 2016/8456\nranked 2112/8456\nranked 2208/8456\nranked 2304/8456\nranked 2400/8456\nranked 2496/8456\nranked 2592/8456\nranked 2688/8456\nranked 2784/8456\nranked 2880/8456\nranked 2976/8456\nranked 3072/8456\nranked 3168/8456\nranked 3264/8456\nranked 3360/8456\nranked 3456/8456\nranked 3552/8456\nranked 3648/8456\nranked 3744/8456\nranked 3840/8456\nranked 3936/8456\nranked 4032/8456\nranked 4128/8456\nranked 4224/8456\nranked 4320/8456\nranked 4416/8456\nranked 4512/8456\nranked 4608/8456\nranked 4704/8456\nranked 4800/8456\nranked 4896/8456\nranked 4992/8456\nranked 5088/8456\nranked 5184/8456\nranked 5280/8456\nranked 5376/8456\nranked 5472/8456\nranked 5568/8456\nranked 5664/8456\nranked 5760/8456\nranked 5856/8456\nranked 5952/8456\nranked 6048/8456\nranked 6144/8456\nranked 6240/8456\nranked 6336/8456\nranked 6432/8456\nranked 6528/8456\nranked 6624/8456\nranked 6720/8456\nranked 6816/8456\nranked 6912/8456\nranked 7008/8456\nranked 7104/8456\nranked 7200/8456\nranked 7296/8456\nranked 7392/8456\nranked 7488/8456\nranked 7584/8456\nranked 7680/8456\nranked 7776/8456\nranked 7872/8456\nranked 7968/8456\nranked 8064/8456\nranked 8160/8456\nranked 8256/8456\nranked 8352/8456\nranked 8448/8456\nranked 8456/8456\nwrote /app/solution/submission.parquet 845600\n" } } ## Step 50 · agent · 2026-09-23T21:59:04.582Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:59:14.991Z { "code": "const r = await tools.exec_command({\"cmd\":\"du -sh /app/solution; ls -lh /app/solution | sort -k5 -h | tail -15; /opt/task-venv/bin/python - <<'PY'\\nimport sys, numpy as np, pandas as pd\\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\\nD='/task/data'; p='/app/solution/submission.parquet'; x=pd.read_parquet(p); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\\nwith np.load(D+'/test_eligible_candidates.npz') as z:\\n ur={int(u):r for r,u in enumerate(z['uids'])}; ic={int(i):c for c,i in enumerate(z['item_ids'])}; packed=z['packed_eligible']\\n ok=True\\n for u,i in y[['uid','item_id']].itertuples(index=False,name=None):\\n r=ur[int(u)]; c=ic[int(i)]; ok &= bool((packed[r,c//8]>>(c%8))&1)\\nprint({'rows':len(y),'users':y.uid.nunique(),'all_eligible':ok,'duplicates':int(y.duplicated(['uid','item_id']).sum()),'bytes':__import__('os').path.getsize(p)})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-09-23T21:59:17.045Z Script completed Wall time 2.0 seconds Output: 109M /app/solution -rw-r--r-- 1 agent agent 694K Sep 23 21:45 val_blend7.parquet -rw-r--r-- 1 agent agent 708K Sep 23 21:45 val_catalog.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:52 validation_cf.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:54 validation_svd.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_0.15.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_0.3.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_0.5.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_0.75.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_1.5.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_1.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_2.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:55 validation_fuse_3.parquet -rw-r--r-- 1 agent agent 1.1M Sep 23 21:59 submission.parquet -rw-r--r-- 1 agent agent 44M Sep 23 21:49 R_train.npz -rw-r--r-- 1 agent agent 45M Sep 23 21:58 R_final.npz {'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicates': 0, 'bytes': 1068374} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44ba5b82c87d293450e890afdfaef", "usage": { "input_tokens": 45580, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 384, "reasoning_output_tokens": 49, "total_tokens": 45964 }, "turn_token_usage": { "input_tokens": 1333402, "cached_input_tokens": 1258240, "cache_write_input_tokens": 0, "output_tokens": 18171, "reasoning_output_tokens": 5748, "total_tokens": 1351573 }, "thread_token_usage": { "input_tokens": 1333402, "cached_input_tokens": 1258240, "cache_write_input_tokens": 0, "output_tokens": 18171, "reasoning_output_tokens": 5748, "total_tokens": 1351573 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-63fcada5-1c27-4c99-bd16-8dbfeb88480e", "process_id": "32296", "command": [ "/bin/sh", "-lc", "du -sh /app/solution; ls -lh /app/solution | sort -k5 -h | tail -15; /opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD='/task/data'; p='/app/solution/submission.parquet'; x=pd.read_parquet(p); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\nwith np.load(D+'/test_eligible_candidates.npz') as z:\n ur={int(u):r for r,u in enumerate(z['uids'])}; ic={int(i):c for c,i in enumerate(z['item_ids'])}; packed=z['packed_eligible']\n ok=True\n for u,i in y[['uid','item_id']].itertuples(index=False,name=None):\n r=ur[int(u)]; c=ic[int(i)]; ok &= bool((packed[r,c//8]>>(c%8))&1)\nprint({'rows':len(y),'users':y.uid.nunique(),'all_eligible':ok,'duplicates':int(y.duplicated(['uid','item_id']).sum()),'bytes':__import__('os').path.getsize(p)})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "du -sh /app/solution; ls -lh /app/solution | sort -k5 -h | tail -15; /opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD='/task/data'; p='/app/solution/submission.parquet'; x=pd.read_parquet(p); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); y=validate_ranking(x,targets)\nwith np.load(D+'/test_eligible_candidates.npz') as z:\n ur={int(u):r for r,u in enumerate(z['uids'])}; ic={int(i):c for c,i in enumerate(z['item_ids'])}; packed=z['packed_eligible']\n ok=True\n for u,i in y[['uid','item_id']].itertuples(index=False,name=None):\n r=ur[int(u)]; c=ic[int(i)]; ok &= bool((packed[r,c//8]>>(c%8))&1)\nprint({'rows':len(y),'users':y.uid.nunique(),'all_eligible':ok,'duplicates':int(y.duplicated(['uid','item_id']).sum()),'bytes':__import__('os').path.getsize(p)})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicates': 0, 'bytes': 1068374}\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicates': 0, 'bytes': 1068374}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 857585115 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicates': 0, 'bytes': 1068374}\n" } } ## Step 51 · agent · 2026-09-23T21:59:35.686Z ### Thinking unavailable — no text recorded The final refit is complete and the submission passes all structural checks: 845,600 rows, exactly 100 unique test-eligible tracks for each of 8,456 users. I’ve preserved it while doing a last low-risk check of the momentum horizon; I’ll only replace the artifact if validation demonstrates a genuine improvement. ### exec · 2026-09-23T21:59:50.029Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_trend.py\\n+from pathlib import Path\\n+import numpy as np,pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution'); cutoff=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\\n+x=pd.read_parquet(D/'train_likes.parquet'); x=x[x.is_organic==1]\\n+with np.load(D/'validation_eligible_candidates.npz') as z: u=z['uids'].copy(); packed=z['packed_eligible'].copy()\\n+for days in (1,2,3,4,5,7,10,14,21):\\n+ vc=x[x.timestamp>=cutoff-days*86400].item_id.value_counts(); a=np.zeros(len(items)); p=idx.reindex(vc.index).to_numpy();ok=~np.isnan(p);a[p[ok].astype(int)]=vc.to_numpy()[ok]\\n+ order=np.lexsort((np.arange(len(items)),-a)); out=np.empty((len(u),100),int)\\n+ for r in range(len(u)):\\n+ m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);out[r]=order[m[order]][:100]\\n+ pd.DataFrame({'uid':np.repeat(u,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(u))}).to_parquet(O/f'trend{days}.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:59:50.118Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e126a529-14dd-434f-81b7-a26957cce26f", "changes": { "/app/solution/tune_trend.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data'); O=Path('/app/solution'); cutoff=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); idx=pd.Series(np.arange(len(items)),index=items)\nx=pd.read_parquet(D/'train_likes.parquet'); x=x[x.is_organic==1]\nwith np.load(D/'validation_eligible_candidates.npz') as z: u=z['uids'].copy(); packed=z['packed_eligible'].copy()\nfor days in (1,2,3,4,5,7,10,14,21):\n vc=x[x.timestamp>=cutoff-days*86400].item_id.value_counts(); a=np.zeros(len(items)); p=idx.reindex(vc.index).to_numpy();ok=~np.isnan(p);a[p[ok].astype(int)]=vc.to_numpy()[ok]\n order=np.lexsort((np.arange(len(items)),-a)); out=np.empty((len(u),100),int)\n for r in range(len(u)):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);out[r]=order[m[order]][:100]\n pd.DataFrame({'uid':np.repeat(u,100),'item_id':items[out.ravel()],'rank':np.tile(np.arange(1,101),len(u))}).to_parquet(O/f'trend{days}.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_trend.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44bb5501887d2972c567cbb219c02", "usage": { "input_tokens": 46464, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 963, "reasoning_output_tokens": 511, "total_tokens": 47427 }, "turn_token_usage": { "input_tokens": 1379866, "cached_input_tokens": 1303680, "cache_write_input_tokens": 0, "output_tokens": 19134, "reasoning_output_tokens": 6259, "total_tokens": 1399000 }, "thread_token_usage": { "input_tokens": 1379866, "cached_input_tokens": 1303680, "cache_write_input_tokens": 0, "output_tokens": 19134, "reasoning_output_tokens": 6259, "total_tokens": 1399000 } } ### exec · 2026-09-23T21:59:55.402Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/tune_trend.py && for f in /app/solution/trend*.parquet; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:00:24.599Z Script completed Wall time 29.2 seconds Output: {"chunk_id":"057694","wall_time_seconds":29.007885151,"exit_code":0,"original_token_count":109,"output":"trend1.parquet \"quality_ndcg@10\": 0.016286,\ntrend10.parquet \"quality_ndcg@10\": 0.029425,\ntrend14.parquet \"quality_ndcg@10\": 0.026726,\ntrend2.parquet \"quality_ndcg@10\": 0.027126,\ntrend21.parquet \"quality_ndcg@10\": 0.021959,\ntrend3.parquet \"quality_ndcg@10\": 0.030503,\ntrend4.parquet \"quality_ndcg@10\": 0.031101,\ntrend5.parquet \"quality_ndcg@10\": 0.028051,\ntrend7.parquet \"quality_ndcg@10\": 0.029297,\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44bd65e0887d2bb50a6bc782c6f0d", "usage": { "input_tokens": 47451, "cached_input_tokens": 46336, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 0, "total_tokens": 47565 }, "turn_token_usage": { "input_tokens": 1427317, "cached_input_tokens": 1350016, "cache_write_input_tokens": 0, "output_tokens": 19248, "reasoning_output_tokens": 6259, "total_tokens": 1446565 }, "thread_token_usage": { "input_tokens": 1427317, "cached_input_tokens": 1350016, "cache_write_input_tokens": 0, "output_tokens": 19248, "reasoning_output_tokens": 6259, "total_tokens": 1446565 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7c6a987f-53ae-44dd-9b3a-4295b8d01342", "process_id": "27505", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/tune_trend.py && for f in /app/solution/trend*.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/tune_trend.py && for f in /app/solution/trend*.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trend1.parquet \"quality_ndcg@10\": 0.016286,\ntrend10.parquet \"quality_ndcg@10\": 0.029425,\ntrend14.parquet \"quality_ndcg@10\": 0.026726,\ntrend2.parquet \"quality_ndcg@10\": 0.027126,\ntrend21.parquet \"quality_ndcg@10\": 0.021959,\ntrend3.parquet \"quality_ndcg@10\": 0.030503,\ntrend4.parquet \"quality_ndcg@10\": 0.031101,\ntrend5.parquet \"quality_ndcg@10\": 0.028051,\ntrend7.parquet \"quality_ndcg@10\": 0.029297,\n", "stderr": "", "aggregated_output": "trend1.parquet \"quality_ndcg@10\": 0.016286,\ntrend10.parquet \"quality_ndcg@10\": 0.029425,\ntrend14.parquet \"quality_ndcg@10\": 0.026726,\ntrend2.parquet \"quality_ndcg@10\": 0.027126,\ntrend21.parquet \"quality_ndcg@10\": 0.021959,\ntrend3.parquet \"quality_ndcg@10\": 0.030503,\ntrend4.parquet \"quality_ndcg@10\": 0.031101,\ntrend5.parquet \"quality_ndcg@10\": 0.028051,\ntrend7.parquet \"quality_ndcg@10\": 0.029297,\n", "exit_code": 0, "duration": { "secs": 29, "nanos": 7837100 }, "formatted_output": "trend1.parquet \"quality_ndcg@10\": 0.016286,\ntrend10.parquet \"quality_ndcg@10\": 0.029425,\ntrend14.parquet \"quality_ndcg@10\": 0.026726,\ntrend2.parquet \"quality_ndcg@10\": 0.027126,\ntrend21.parquet \"quality_ndcg@10\": 0.021959,\ntrend3.parquet \"quality_ndcg@10\": 0.030503,\ntrend4.parquet \"quality_ndcg@10\": 0.031101,\ntrend5.parquet \"quality_ndcg@10\": 0.028051,\ntrend7.parquet \"quality_ndcg@10\": 0.029297,\n" } } ## Step 52 · agent · 2026-09-23T22:00:33.032Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:47.750Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/cf_validate.py\\n@@\\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy()\\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\\n+vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok])\\n*** Update File: /app/solution/svd_validate.py\\n@@\\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\\n+vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok]); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\\n*** Update File: /app/solution/build_submission.py\\n@@\\n- x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\\n+ x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-4*86400)]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:00:47.864Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c6ba3194-a4e4-4c0b-822a-bda3f896355a", "changes": { "/app/solution/cf_validate.py": { "type": "update", "unified_diff": "@@ -50,3 +50,4 @@\n # best global trend from development: 7-day organic likes\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy()\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\n+vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok])\n # Percentile with the catalog popularity order as the deterministic tie-break.\n", "move_path": null }, "/app/solution/svd_validate.py": { "type": "update", "unified_diff": "@@ -11,3 +11,4 @@\n with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\n-f=pd.read_parquet(O/'pop_features.parquet'); glob=f.like7.to_numpy(); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\n+vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok]); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\n gold=truth(); urow={int(u):r for r,u in enumerate(eu)}; gold={(u,i):g for (u,i),g in gold.items() if u in urow and i in imap.index and ((ep[urow[u],int(imap[i])//8]>>(int(imap[i])%8))&1)}\n", "move_path": null }, "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -44,3 +44,3 @@\n def momentum():\n- x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\n+ x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-4*86400)]\n vc=x.item_id.value_counts(); raw=np.zeros(nitems,np.float32); p=imap.reindex(vc.index).to_numpy(); ok=~np.isnan(p); raw[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok])\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/cf_validate.py\nM /app/solution/svd_validate.py\nM /app/solution/build_submission.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44bf8ca3c87d29a238e049464e6fc", "usage": { "input_tokens": 47790, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 655, "reasoning_output_tokens": 186, "total_tokens": 48445 }, "turn_token_usage": { "input_tokens": 1475107, "cached_input_tokens": 1397248, "cache_write_input_tokens": 0, "output_tokens": 19903, "reasoning_output_tokens": 6445, "total_tokens": 1495010 }, "thread_token_usage": { "input_tokens": 1475107, "cached_input_tokens": 1397248, "cache_write_input_tokens": 0, "output_tokens": 19903, "reasoning_output_tokens": 6445, "total_tokens": 1495010 } } ## Step 53 · agent · 2026-09-23T22:00:51.158Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:54.772Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/cf_validate.py >/app/solution/cf4.log && /opt/task-venv/bin/python /app/solution/svd_validate.py >/app/solution/svd4.log && tail -22 /app/solution/cf4.log && tail -37 /app/solution/svd4.log\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:01:24.938Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"ed3deb","wall_time_seconds":30.001039095,"session_id":24887,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44c10184887d2b00f16249489ec5f", "usage": { "input_tokens": 48469, "cached_input_tokens": 47616, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 28, "total_tokens": 48618 }, "turn_token_usage": { "input_tokens": 1523576, "cached_input_tokens": 1444864, "cache_write_input_tokens": 0, "output_tokens": 20052, "reasoning_output_tokens": 6473, "total_tokens": 1543628 }, "thread_token_usage": { "input_tokens": 1523576, "cached_input_tokens": 1444864, "cache_write_input_tokens": 0, "output_tokens": 20052, "reasoning_output_tokens": 6473, "total_tokens": 1543628 } } ## Step 54 · agent · 2026-09-23T22:01:29.783Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:01:30.986Z { "code": "const r = await tools.write_stdin({\"session_id\":24887,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:02:00.996Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"629a90","wall_time_seconds":30.000999629,"session_id":24887,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44c3537f487d2b6e8d9264555fd0e", "usage": { "input_tokens": 48675, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 50, "total_tokens": 48773 }, "turn_token_usage": { "input_tokens": 1572251, "cached_input_tokens": 1493120, "cache_write_input_tokens": 0, "output_tokens": 20150, "reasoning_output_tokens": 6523, "total_tokens": 1592401 }, "thread_token_usage": { "input_tokens": 1572251, "cached_input_tokens": 1493120, "cache_write_input_tokens": 0, "output_tokens": 20150, "reasoning_output_tokens": 6523, "total_tokens": 1592401 } } ## Step 55 · agent · 2026-09-23T22:02:04.559Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:02:05.749Z { "code": "const r = await tools.write_stdin({\"session_id\":24887,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:02:35.758Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"931c05","wall_time_seconds":30.001048505,"session_id":24887,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44c5930c087d29b77f1d80856eba5", "usage": { "input_tokens": 48830, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 48884 }, "turn_token_usage": { "input_tokens": 1621081, "cached_input_tokens": 1541632, "cache_write_input_tokens": 0, "output_tokens": 20204, "reasoning_output_tokens": 6529, "total_tokens": 1641285 }, "thread_token_usage": { "input_tokens": 1621081, "cached_input_tokens": 1541632, "cache_write_input_tokens": 0, "output_tokens": 20204, "reasoning_output_tokens": 6529, "total_tokens": 1641285 } } ## Step 56 · agent · 2026-09-23T22:02:37.685Z ### Thinking unavailable — no text recorded A four-day organic-like horizon validates better than seven days (0.031101 vs. 0.029297 as a standalone ranker). I’m re-running the two personalized blends with that horizon; the current fully valid submission remains safely in place until the revised model is confirmed. ### exec · 2026-09-23T22:02:41.134Z { "code": "const r = await tools.write_stdin({\"session_id\":24887,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:03:11.188Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"697a6c","wall_time_seconds":30.000947672,"session_id":24887,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44c7c000487d28df14b77a2850ae5", "usage": { "input_tokens": 48941, "cached_input_tokens": 48640, "cache_write_input_tokens": 0, "output_tokens": 130, "reasoning_output_tokens": 18, "total_tokens": 49071 }, "turn_token_usage": { "input_tokens": 1670022, "cached_input_tokens": 1590272, "cache_write_input_tokens": 0, "output_tokens": 20334, "reasoning_output_tokens": 6547, "total_tokens": 1690356 }, "thread_token_usage": { "input_tokens": 1670022, "cached_input_tokens": 1590272, "cache_write_input_tokens": 0, "output_tokens": 20334, "reasoning_output_tokens": 6547, "total_tokens": 1690356 } } ## Step 57 · agent · 2026-09-23T22:03:14.473Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:15.689Z { "code": "const r = await tools.write_stdin({\"session_id\":24887,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:03:26.233Z Script completed Wall time 10.5 seconds Output: {"chunk_id":"753b45","wall_time_seconds":10.534386307,"exit_code":0,"original_token_count":454,"output":"(50, 0.5, 0) 0.020017314303662135\n(50, 1, 0) 0.020277158003543277\n(50, 2, 0) 0.020615154179439776\n(50, 4, 0) 0.02107384597596884\n(50, 8, 0) 0.021895545394742217\n(50, 16, 0) 0.023291612726087447\n(50, 32, 0) 0.024083239870662743\n(50, 0.5, 0.2) 0.022116432578382323\n(50, 1, 0.2) 0.023398568031248276\n(50, 2, 0.2) 0.02531359544636224\n(50, 4, 0.2) 0.026849208237250025\n(50, 8, 0.2) 0.028499370182623447\n(50, 16, 0.2) 0.03046526215244903\n(50, 32, 0.2) 0.03237481618772807\n(50, 0.5, 0.4) 0.022559938268212055\n(50, 1, 0.4) 0.026627936044015657\n(50, 2, 0.4) 0.030339991574234233\n(50, 4, 0.4) 0.03340201747772281\n(50, 8, 0.4) 0.03459402684437292\n(50, 16, 0.4) 0.03587458277662601\n(50, 32, 0.4) 0.03702735115656276\nBEST (50, 32, 0.4)\n8448 8456\n(0.25, 0) 0.030017036608101278\n(0.5, 0) 0.030820979198195934\n(1, 0) 0.03179264624677531\n(2, 0) 0.032667505589132804\n(4, 0) 0.03396166374940465\n(8, 0) 0.035307869211717614\n(16, 0) 0.03516765851330217\n(0.25, 0.25) 0.02910960657984848\n(0.5, 0.25) 0.03009407155014966\n(1, 0.25) 0.03066655137462279\n(2, 0.25) 0.03279251694441676\n(4, 0.25) 0.033975460063026666\n(8, 0.25) 0.03551308037956917\n(16, 0.25) 0.03496693135083901\n(0.25, 0.5) 0.028359000201925832\n(0.5, 0.5) 0.029353908980331474\n(1, 0.5) 0.030323671233626094\n(2, 0.5) 0.032542279059786\n(4, 0.5) 0.03415804356320297\n(8, 0.5) 0.03517688444114323\n(16, 0.5) 0.034607183054833325\n(0.25, 0.75) 0.027873913119346717\n(0.5, 0.75) 0.028528426901012188\n(1, 0.75) 0.02996285416186545\n(2, 0.75) 0.031891026390676196\n(4, 0.75) 0.034270065635306206\n(8, 0.75) 0.03497730500628756\n(16, 0.75) 0.034461685956120446\n(0.25, 1) 0.02732873279058705\n(0.5, 1) 0.02777231773008886\n(1, 1) 0.029347860007638258\n(2, 1) 0.03211699709176452\n(4, 1) 0.034568533474469094\n(8, 1) 0.03477417633837973\n(16, 1) 0.034360440944749215\nBEST (8, 0.25) 0.03551308037956917\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44c9f6edc87d29e0db9f3880dfcf1", "usage": { "input_tokens": 49129, "cached_input_tokens": 48768, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 9, "total_tokens": 49186 }, "turn_token_usage": { "input_tokens": 1719151, "cached_input_tokens": 1639040, "cache_write_input_tokens": 0, "output_tokens": 20391, "reasoning_output_tokens": 6556, "total_tokens": 1739542 }, "thread_token_usage": { "input_tokens": 1719151, "cached_input_tokens": 1639040, "cache_write_input_tokens": 0, "output_tokens": 20391, "reasoning_output_tokens": 6556, "total_tokens": 1739542 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1c0e7c05-0111-4363-8e64-2d34e6eea076", "process_id": "24887", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/cf_validate.py >/app/solution/cf4.log && /opt/task-venv/bin/python /app/solution/svd_validate.py >/app/solution/svd4.log && tail -22 /app/solution/cf4.log && tail -37 /app/solution/svd4.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/cf_validate.py >/app/solution/cf4.log && /opt/task-venv/bin/python /app/solution/svd_validate.py >/app/solution/svd4.log && tail -22 /app/solution/cf4.log && tail -37 /app/solution/svd4.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(50, 0.5, 0) 0.020017314303662135\n(50, 1, 0) 0.020277158003543277\n(50, 2, 0) 0.020615154179439776\n(50, 4, 0) 0.02107384597596884\n(50, 8, 0) 0.021895545394742217\n(50, 16, 0) 0.023291612726087447\n(50, 32, 0) 0.024083239870662743\n(50, 0.5, 0.2) 0.022116432578382323\n(50, 1, 0.2) 0.023398568031248276\n(50, 2, 0.2) 0.02531359544636224\n(50, 4, 0.2) 0.026849208237250025\n(50, 8, 0.2) 0.028499370182623447\n(50, 16, 0.2) 0.03046526215244903\n(50, 32, 0.2) 0.03237481618772807\n(50, 0.5, 0.4) 0.022559938268212055\n(50, 1, 0.4) 0.026627936044015657\n(50, 2, 0.4) 0.030339991574234233\n(50, 4, 0.4) 0.03340201747772281\n(50, 8, 0.4) 0.03459402684437292\n(50, 16, 0.4) 0.03587458277662601\n(50, 32, 0.4) 0.03702735115656276\nBEST (50, 32, 0.4)\n8448 8456\n(0.25, 0) 0.030017036608101278\n(0.5, 0) 0.030820979198195934\n(1, 0) 0.03179264624677531\n(2, 0) 0.032667505589132804\n(4, 0) 0.03396166374940465\n(8, 0) 0.035307869211717614\n(16, 0) 0.03516765851330217\n(0.25, 0.25) 0.02910960657984848\n(0.5, 0.25) 0.03009407155014966\n(1, 0.25) 0.03066655137462279\n(2, 0.25) 0.03279251694441676\n(4, 0.25) 0.033975460063026666\n(8, 0.25) 0.03551308037956917\n(16, 0.25) 0.03496693135083901\n(0.25, 0.5) 0.028359000201925832\n(0.5, 0.5) 0.029353908980331474\n(1, 0.5) 0.030323671233626094\n(2, 0.5) 0.032542279059786\n(4, 0.5) 0.03415804356320297\n(8, 0.5) 0.03517688444114323\n(16, 0.5) 0.034607183054833325\n(0.25, 0.75) 0.027873913119346717\n(0.5, 0.75) 0.028528426901012188\n(1, 0.75) 0.02996285416186545\n(2, 0.75) 0.031891026390676196\n(4, 0.75) 0.034270065635306206\n(8, 0.75) 0.03497730500628756\n(16, 0.75) 0.034461685956120446\n(0.25, 1) 0.02732873279058705\n(0.5, 1) 0.02777231773008886\n(1, 1) 0.029347860007638258\n(2, 1) 0.03211699709176452\n(4, 1) 0.034568533474469094\n(8, 1) 0.03477417633837973\n(16, 1) 0.034360440944749215\nBEST (8, 0.25) 0.03551308037956917\n", "stderr": "", "aggregated_output": "(50, 0.5, 0) 0.020017314303662135\n(50, 1, 0) 0.020277158003543277\n(50, 2, 0) 0.020615154179439776\n(50, 4, 0) 0.02107384597596884\n(50, 8, 0) 0.021895545394742217\n(50, 16, 0) 0.023291612726087447\n(50, 32, 0) 0.024083239870662743\n(50, 0.5, 0.2) 0.022116432578382323\n(50, 1, 0.2) 0.023398568031248276\n(50, 2, 0.2) 0.02531359544636224\n(50, 4, 0.2) 0.026849208237250025\n(50, 8, 0.2) 0.028499370182623447\n(50, 16, 0.2) 0.03046526215244903\n(50, 32, 0.2) 0.03237481618772807\n(50, 0.5, 0.4) 0.022559938268212055\n(50, 1, 0.4) 0.026627936044015657\n(50, 2, 0.4) 0.030339991574234233\n(50, 4, 0.4) 0.03340201747772281\n(50, 8, 0.4) 0.03459402684437292\n(50, 16, 0.4) 0.03587458277662601\n(50, 32, 0.4) 0.03702735115656276\nBEST (50, 32, 0.4)\n8448 8456\n(0.25, 0) 0.030017036608101278\n(0.5, 0) 0.030820979198195934\n(1, 0) 0.03179264624677531\n(2, 0) 0.032667505589132804\n(4, 0) 0.03396166374940465\n(8, 0) 0.035307869211717614\n(16, 0) 0.03516765851330217\n(0.25, 0.25) 0.02910960657984848\n(0.5, 0.25) 0.03009407155014966\n(1, 0.25) 0.03066655137462279\n(2, 0.25) 0.03279251694441676\n(4, 0.25) 0.033975460063026666\n(8, 0.25) 0.03551308037956917\n(16, 0.25) 0.03496693135083901\n(0.25, 0.5) 0.028359000201925832\n(0.5, 0.5) 0.029353908980331474\n(1, 0.5) 0.030323671233626094\n(2, 0.5) 0.032542279059786\n(4, 0.5) 0.03415804356320297\n(8, 0.5) 0.03517688444114323\n(16, 0.5) 0.034607183054833325\n(0.25, 0.75) 0.027873913119346717\n(0.5, 0.75) 0.028528426901012188\n(1, 0.75) 0.02996285416186545\n(2, 0.75) 0.031891026390676196\n(4, 0.75) 0.034270065635306206\n(8, 0.75) 0.03497730500628756\n(16, 0.75) 0.034461685956120446\n(0.25, 1) 0.02732873279058705\n(0.5, 1) 0.02777231773008886\n(1, 1) 0.029347860007638258\n(2, 1) 0.03211699709176452\n(4, 1) 0.034568533474469094\n(8, 1) 0.03477417633837973\n(16, 1) 0.034360440944749215\nBEST (8, 0.25) 0.03551308037956917\n", "exit_code": 0, "duration": { "secs": 151, "nanos": 294093772 }, "formatted_output": "(50, 0.5, 0) 0.020017314303662135\n(50, 1, 0) 0.020277158003543277\n(50, 2, 0) 0.020615154179439776\n(50, 4, 0) 0.02107384597596884\n(50, 8, 0) 0.021895545394742217\n(50, 16, 0) 0.023291612726087447\n(50, 32, 0) 0.024083239870662743\n(50, 0.5, 0.2) 0.022116432578382323\n(50, 1, 0.2) 0.023398568031248276\n(50, 2, 0.2) 0.02531359544636224\n(50, 4, 0.2) 0.026849208237250025\n(50, 8, 0.2) 0.028499370182623447\n(50, 16, 0.2) 0.03046526215244903\n(50, 32, 0.2) 0.03237481618772807\n(50, 0.5, 0.4) 0.022559938268212055\n(50, 1, 0.4) 0.026627936044015657\n(50, 2, 0.4) 0.030339991574234233\n(50, 4, 0.4) 0.03340201747772281\n(50, 8, 0.4) 0.03459402684437292\n(50, 16, 0.4) 0.03587458277662601\n(50, 32, 0.4) 0.03702735115656276\nBEST (50, 32, 0.4)\n8448 8456\n(0.25, 0) 0.030017036608101278\n(0.5, 0) 0.030820979198195934\n(1, 0) 0.03179264624677531\n(2, 0) 0.032667505589132804\n(4, 0) 0.03396166374940465\n(8, 0) 0.035307869211717614\n(16, 0) 0.03516765851330217\n(0.25, 0.25) 0.02910960657984848\n(0.5, 0.25) 0.03009407155014966\n(1, 0.25) 0.03066655137462279\n(2, 0.25) 0.03279251694441676\n(4, 0.25) 0.033975460063026666\n(8, 0.25) 0.03551308037956917\n(16, 0.25) 0.03496693135083901\n(0.25, 0.5) 0.028359000201925832\n(0.5, 0.5) 0.029353908980331474\n(1, 0.5) 0.030323671233626094\n(2, 0.5) 0.032542279059786\n(4, 0.5) 0.03415804356320297\n(8, 0.5) 0.03517688444114323\n(16, 0.5) 0.034607183054833325\n(0.25, 0.75) 0.027873913119346717\n(0.5, 0.75) 0.028528426901012188\n(1, 0.75) 0.02996285416186545\n(2, 0.75) 0.031891026390676196\n(4, 0.75) 0.034270065635306206\n(8, 0.75) 0.03497730500628756\n(16, 0.75) 0.034461685956120446\n(0.25, 1) 0.02732873279058705\n(0.5, 1) 0.02777231773008886\n(1, 1) 0.029347860007638258\n(2, 1) 0.03211699709176452\n(4, 1) 0.034568533474469094\n(8, 1) 0.03477417633837973\n(16, 1) 0.034360440944749215\nBEST (8, 0.25) 0.03551308037956917\n" } } ## Step 58 · agent · 2026-09-23T22:03:36.848Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:41.543Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_submission.py\\n@@\\n- x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-4*86400)]\\n+ x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:03:41.723Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b22c7c5f-08a5-47ae-adae-6f037ca1bcfa", "changes": { "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -44,3 +44,3 @@\n def momentum():\n- x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-4*86400)]\n+ x=pd.read_parquet(D/'val_likes.parquet'); x=x[(x.is_organic==1)&(x.timestamp>=CUTOFF-7*86400)]\n vc=x.item_id.value_counts(); raw=np.zeros(nitems,np.float32); p=imap.reindex(vc.index).to_numpy(); ok=~np.isnan(p); raw[p[ok].astype(int)]=np.log1p(vc.to_numpy()[ok])\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_submission.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44cae722887d2bd1f77ce50edffc9", "usage": { "input_tokens": 50333, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 391, "reasoning_output_tokens": 265, "total_tokens": 50724 }, "turn_token_usage": { "input_tokens": 1769484, "cached_input_tokens": 1687936, "cache_write_input_tokens": 0, "output_tokens": 20782, "reasoning_output_tokens": 6821, "total_tokens": 1790266 }, "thread_token_usage": { "input_tokens": 1769484, "cached_input_tokens": 1687936, "cache_write_input_tokens": 0, "output_tokens": 20782, "reasoning_output_tokens": 6821, "total_tokens": 1790266 } } ## Step 59 · agent · 2026-09-23T22:03:47.790Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:51.192Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2500});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:04:21.385Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"c1c877","wall_time_seconds":30.000636317,"session_id":17900,"original_token_count":54,"output":"validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038507,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.038651,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.038894,\nvalidation_fuse_0.75.parquet "} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44cbe8ec087d2b5f033639bad9308", "usage": { "input_tokens": 50748, "cached_input_tokens": 50176, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 18, "total_tokens": 50881 }, "turn_token_usage": { "input_tokens": 1820232, "cached_input_tokens": 1738112, "cache_write_input_tokens": 0, "output_tokens": 20915, "reasoning_output_tokens": 6839, "total_tokens": 1841147 }, "thread_token_usage": { "input_tokens": 1820232, "cached_input_tokens": 1738112, "cache_write_input_tokens": 0, "output_tokens": 20915, "reasoning_output_tokens": 6839, "total_tokens": 1841147 } } ### exec · 2026-09-23T22:04:26.090Z { "code": "const r = await tools.write_stdin({\"session_id\":17900,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2500});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:04:34.035Z Script completed Wall time 7.9 seconds Output: {"chunk_id":"c324a2","wall_time_seconds":7.936203261,"exit_code":0,"original_token_count":68,"output":" \"quality_ndcg@10\": 0.038959,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.038209,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.038811,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.038046,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.037466,\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44ce59c9887d288d0db7c378fe1fc", "usage": { "input_tokens": 51019, "cached_input_tokens": 50560, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 51065 }, "turn_token_usage": { "input_tokens": 1871251, "cached_input_tokens": 1788672, "cache_write_input_tokens": 0, "output_tokens": 20961, "reasoning_output_tokens": 6839, "total_tokens": 1892212 }, "thread_token_usage": { "input_tokens": 1871251, "cached_input_tokens": 1788672, "cache_write_input_tokens": 0, "output_tokens": 20961, "reasoning_output_tokens": 6839, "total_tokens": 1892212 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ff09bc1-89c7-463f-9609-f2c8a376b2b3", "process_id": "17900", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/fuse_validate.py && for f in /app/solution/validation_fuse*; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038507,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.038651,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.038894,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.038959,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.038209,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.038811,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.038046,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.037466,\n", "stderr": "", "aggregated_output": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038507,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.038651,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.038894,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.038959,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.038209,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.038811,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.038046,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.037466,\n", "exit_code": 0, "duration": { "secs": 42, "nanos": 649222314 }, "formatted_output": "validation_fuse_0.15.parquet \"quality_ndcg@10\": 0.038507,\nvalidation_fuse_0.3.parquet \"quality_ndcg@10\": 0.038651,\nvalidation_fuse_0.5.parquet \"quality_ndcg@10\": 0.038894,\nvalidation_fuse_0.75.parquet \"quality_ndcg@10\": 0.038959,\nvalidation_fuse_1.5.parquet \"quality_ndcg@10\": 0.038209,\nvalidation_fuse_1.parquet \"quality_ndcg@10\": 0.038811,\nvalidation_fuse_2.parquet \"quality_ndcg@10\": 0.038046,\nvalidation_fuse_3.parquet \"quality_ndcg@10\": 0.037466,\n" } } ## Step 60 · agent · 2026-09-23T22:04:40.504Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:04:52.995Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+`submission.parquet` is the final test-cutoff ranking. It contains 845,600\\n+rows (100 tracks for each of 8,456 users), with integer `uid`, `item_id`, and\\n+`rank` columns. Every row was checked against `test_eligible_candidates.npz`.\\n+\\n+## Model\\n+\\n+The final model is a validation-selected rank fusion of:\\n+\\n+- 50-neighbor implicit collaborative filtering, using recency-, completion-,\\n+ organic-source-, and explicit-like-weighted histories, with an item-frequency\\n+ correction;\\n+- a 128-component TF-IDF-weighted PureSVD model;\\n+- seven-day organic-like momentum, with training popularity as the tie-break.\\n+\\n+The interaction model is refit on `train_* + val_*`. Test preference state is\\n+enforced by the supplied cutoff-correct eligibility mask (all active explicit\\n+state pairs are ineligible). The selected fusion scored **0.040009 public\\n+NDCG@10**, versus 0.029297 for the strongest standalone trend baseline and\\n+0.010710 for raw catalog popularity.\\n+\\n+## Rebuild\\n+\\n+Run:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_submission.py\\n+```\\n+\\n+The script writes only under `/app/solution/`, caches the final sparse matrix\\n+there, fixes the SVD random seed, applies the test eligibility bits before\\n+top-k selection, and overwrites `submission.parquet` only after ranking all\\n+users.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:04:53.108Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c302e246-5811-43b1-81c2-fdb985d81d75", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Music-discovery ranker\n\n`submission.parquet` is the final test-cutoff ranking. It contains 845,600\nrows (100 tracks for each of 8,456 users), with integer `uid`, `item_id`, and\n`rank` columns. Every row was checked against `test_eligible_candidates.npz`.\n\n## Model\n\nThe final model is a validation-selected rank fusion of:\n\n- 50-neighbor implicit collaborative filtering, using recency-, completion-,\n organic-source-, and explicit-like-weighted histories, with an item-frequency\n correction;\n- a 128-component TF-IDF-weighted PureSVD model;\n- seven-day organic-like momentum, with training popularity as the tie-break.\n\nThe interaction model is refit on `train_* + val_*`. Test preference state is\nenforced by the supplied cutoff-correct eligibility mask (all active explicit\nstate pairs are ineligible). The selected fusion scored **0.040009 public\nNDCG@10**, versus 0.029297 for the strongest standalone trend baseline and\n0.010710 for raw catalog popularity.\n\n## Rebuild\n\nRun:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_submission.py\n```\n\nThe script writes only under `/app/solution/`, caches the final sparse matrix\nthere, fixes the SVD random seed, applies the test eligibility bits before\ntop-k selection, and overwrites `submission.parquet` only after ranking all\nusers.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44cf2387c87d2a5b8b6ee7eabf5a1", "usage": { "input_tokens": 51226, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 524, "reasoning_output_tokens": 125, "total_tokens": 51750 }, "turn_token_usage": { "input_tokens": 1922477, "cached_input_tokens": 1839488, "cache_write_input_tokens": 0, "output_tokens": 21485, "reasoning_output_tokens": 6964, "total_tokens": 1943962 }, "thread_token_usage": { "input_tokens": 1922477, "cached_input_tokens": 1839488, "cache_write_input_tokens": 0, "output_tokens": 21485, "reasoning_output_tokens": 6964, "total_tokens": 1943962 } } ## Step 61 · agent · 2026-09-23T22:05:06.923Z ### Thinking unavailable — no text recorded The four-day signal was stronger alone but weaker once combined with personalized models; its best fused result was 0.038959, below the preserved seven-day fusion at 0.040009. I’ve therefore retained the latter and made the rebuild script reproduce it. I’m running one final fusion-weight sweep on the selected model outputs, with the submission still untouched. ### exec · 2026-09-23T22:05:16.887Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/cf_validate.py\\n@@\\n-xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-7*86400)]\\n*** Update File: /app/solution/svd_validate.py\\n@@\\n-xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-7*86400)]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:05:16.950Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-092f605c-e807-4868-a5a9-ed294dee0880", "changes": { "/app/solution/svd_validate.py": { "type": "update", "unified_diff": "@@ -11,3 +11,3 @@\n with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ei=z['item_ids'].copy(); ep=z['packed_eligible'].copy()\n-xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-7*86400)]\n vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok]); globrank=np.empty(nitems,np.float32); globrank[np.lexsort((-np.arange(nitems),glob))]=np.arange(nitems,dtype=np.float32)/nitems\n", "move_path": null }, "/app/solution/cf_validate.py": { "type": "update", "unified_diff": "@@ -50,3 +50,3 @@\n # best global trend from development: 7-day organic likes\n-xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-4*86400)]\n+xtrend=pd.read_parquet(D/'train_likes.parquet'); xtrend=xtrend[(xtrend.is_organic==1)&(xtrend.timestamp>=CUTOFF-7*86400)]\n vc=xtrend.item_id.value_counts(); glob=np.zeros(nitems); gp=imap.reindex(vc.index).to_numpy(); gok=~np.isnan(gp); glob[gp[gok].astype(int)]=np.log1p(vc.to_numpy()[gok])\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/cf_validate.py\nM /app/solution/svd_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44d05679c87d28364fe3bb757b830", "usage": { "input_tokens": 51774, "cached_input_tokens": 51072, "cache_write_input_tokens": 0, "output_tokens": 651, "reasoning_output_tokens": 336, "total_tokens": 52425 }, "turn_token_usage": { "input_tokens": 1974251, "cached_input_tokens": 1890560, "cache_write_input_tokens": 0, "output_tokens": 22136, "reasoning_output_tokens": 7300, "total_tokens": 1996387 }, "thread_token_usage": { "input_tokens": 1974251, "cached_input_tokens": 1890560, "cache_write_input_tokens": 0, "output_tokens": 22136, "reasoning_output_tokens": 7300, "total_tokens": 1996387 } } ## Step 62 · agent · 2026-09-23T22:05:20.597Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:05:22.901Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/cf_validate.py && /opt/task-venv/bin/python /app/solution/svd_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:05:53.086Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"97544b","wall_time_seconds":30.001062052,"session_id":83732,"original_token_count":187,"output":"R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44d1d458c87d2b626dc0f2c7be26b", "usage": { "input_tokens": 52449, "cached_input_tokens": 51584, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 17, "total_tokens": 52547 }, "turn_token_usage": { "input_tokens": 2026700, "cached_input_tokens": 1942144, "cache_write_input_tokens": 0, "output_tokens": 22234, "reasoning_output_tokens": 7317, "total_tokens": 2048934 }, "thread_token_usage": { "input_tokens": 2026700, "cached_input_tokens": 1942144, "cache_write_input_tokens": 0, "output_tokens": 22234, "reasoning_output_tokens": 7317, "total_tokens": 2048934 } } ## Step 63 · agent · 2026-09-23T22:05:54.812Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:05:56.057Z { "code": "const r = await tools.write_stdin({\"session_id\":83732,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:06:26.066Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"ff5336","wall_time_seconds":30.00160493,"session_id":83732,"original_token_count":226,"output":"score batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44d41491487d28a823e727c37f5cc", "usage": { "input_tokens": 52902, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 8, "total_tokens": 52958 }, "turn_token_usage": { "input_tokens": 2079602, "cached_input_tokens": 1994368, "cache_write_input_tokens": 0, "output_tokens": 22290, "reasoning_output_tokens": 7325, "total_tokens": 2101892 }, "thread_token_usage": { "input_tokens": 2079602, "cached_input_tokens": 1994368, "cache_write_input_tokens": 0, "output_tokens": 22290, "reasoning_output_tokens": 7325, "total_tokens": 2101892 } } ### exec · 2026-09-23T22:06:29.023Z { "code": "const r = await tools.write_stdin({\"session_id\":83732,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1500});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:06:59.032Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"c8519a","wall_time_seconds":30.00078874,"session_id":83732,"original_token_count":278,"output":"score batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\nfitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44d6241bc87d2908db3bbaf27d0b8", "usage": { "input_tokens": 53383, "cached_input_tokens": 52736, "cache_write_input_tokens": 0, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 53429 }, "turn_token_usage": { "input_tokens": 2132985, "cached_input_tokens": 2047104, "cache_write_input_tokens": 0, "output_tokens": 22336, "reasoning_output_tokens": 7325, "total_tokens": 2155321 }, "thread_token_usage": { "input_tokens": 2132985, "cached_input_tokens": 2047104, "cache_write_input_tokens": 0, "output_tokens": 22336, "reasoning_output_tokens": 7325, "total_tokens": 2155321 } } ## Step 64 · agent · 2026-09-23T22:07:01.143Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:02.393Z { "code": "const r = await tools.write_stdin({\"session_id\":83732,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:07:32.485Z Script completed Wall time 30.1 seconds Output: {"chunk_id":"58a028","wall_time_seconds":30.001113414,"session_id":83732,"original_token_count":102,"output":"640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44d835a6487d29843902139b0fe33", "usage": { "input_tokens": 54094, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 54148 }, "turn_token_usage": { "input_tokens": 2187079, "cached_input_tokens": 2100352, "cache_write_input_tokens": 0, "output_tokens": 22390, "reasoning_output_tokens": 7331, "total_tokens": 2209469 }, "thread_token_usage": { "input_tokens": 2187079, "cached_input_tokens": 2100352, "cache_write_input_tokens": 0, "output_tokens": 22390, "reasoning_output_tokens": 7331, "total_tokens": 2209469 } } ## Step 65 · agent · 2026-09-23T22:07:34.159Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:35.465Z { "code": "const r = await tools.write_stdin({\"session_id\":83732,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:07:50.824Z Script completed Wall time 15.4 seconds Output: {"chunk_id":"ca3ff1","wall_time_seconds":15.34999577,"exit_code":0,"original_token_count":321,"output":"5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44da4b69087d296da09df5e5a902e", "usage": { "input_tokens": 54445, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 54499 }, "turn_token_usage": { "input_tokens": 2241524, "cached_input_tokens": 2154240, "cache_write_input_tokens": 0, "output_tokens": 22444, "reasoning_output_tokens": 7337, "total_tokens": 2263968 }, "thread_token_usage": { "input_tokens": 2241524, "cached_input_tokens": 2154240, "cache_write_input_tokens": 0, "output_tokens": 22444, "reasoning_output_tokens": 7337, "total_tokens": 2263968 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bb51e11b-687a-4286-9bb4-8cfcad0e0adf", "process_id": "83732", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/cf_validate.py && /opt/task-venv/bin/python /app/solution/svd_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/cf_validate.py && /opt/task-venv/bin/python /app/solution/svd_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\nfitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n", "stderr": "", "aggregated_output": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\nfitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n", "exit_code": 0, "duration": { "secs": 147, "nanos": 738068114 }, "formatted_output": "R (10000, 30000) 5687487\nscore batch 0 96\nscore batch 96 192\nscore batch 192 288\nscore batch 288 384\nscore batch 384 480\nscore batch 480 576\nscore batch 576 672\nscore batch 672 768\nscore batch 768 864\nscore batch 864 960\nscore batch 960 1056\nscore batch 1056 1152\nscore batch 1152 1248\nscore batch 1248 1344\nscore batch 1344 1440\nscore batch 1440 1536\nscore batch 1536 1632\nscore batch 1632 1728\nscore batch 1728 1824\nscore batch 1824 1920\nscore batch 1920 2016\nscore batch 2016 2112\nscore batch 2112 2208\nscore batch 2208 2304\nscore batch 2304 2400\nscore batch 2400 2496\nscore batch 2496 2592\nscore batch 2592 2688\nscore batch 2688 2784\nscore batch 2784 2880\nscore batch 2880 2976\nscore batch 2976 3072\nscore batch 3072 3168\nscore batch 3168 3264\nscore batch 3264 3360\nscore batch 3360 3456\nscore batch 3456 3552\nscore batch 3552 3648\nscore batch 3648 3744\nscore batch 3744 3840\nscore batch 3840 3936\nscore batch 3936 4032\nscore batch 4032 4128\nscore batch 4128 4224\nscore batch 4224 4320\nscore batch 4320 4416\nscore batch 4416 4512\nscore batch 4512 4608\nscore batch 4608 4704\nscore batch 4704 4800\nscore batch 4800 4896\nscore batch 4896 4992\nscore batch 4992 5088\nscore batch 5088 5184\nscore batch 5184 5280\nscore batch 5280 5376\nscore batch 5376 5472\nscore batch 5472 5568\nscore batch 5568 5664\nscore batch 5664 5760\nscore batch 5760 5856\nscore batch 5856 5952\nscore batch 5952 6048\nscore batch 6048 6144\nscore batch 6144 6240\nscore batch 6240 6336\nscore batch 6336 6432\nscore batch 6432 6528\nscore batch 6528 6624\nscore batch 6624 6720\nscore batch 6720 6816\nscore batch 6816 6912\nscore batch 6912 7008\nscore batch 7008 7104\nscore batch 7104 7200\nscore batch 7200 7296\nscore batch 7296 7392\nscore batch 7392 7488\nscore batch 7488 7584\nscore batch 7584 7680\nscore batch 7680 7776\nscore batch 7776 7872\nscore batch 7872 7968\nscore batch 7968 8064\nscore batch 8064 8160\nscore batch 8160 8256\nscore batch 8256 8352\nscore batch 8352 8448\nscore batch 8448 8456\n(50, 0.5, 0) 0.019864947447216463\n(50, 1, 0) 0.020433783132538793\n(50, 2, 0) 0.020766059020969722\n(50, 4, 0) 0.021356396031187903\n(50, 8, 0) 0.02188845498803884\n(50, 16, 0) 0.023177817210558175\n(50, 32, 0) 0.024982455331175234\n(50, 0.5, 0.2) 0.02189448492027235\n(50, 1, 0.2) 0.024337053197515307\n(50, 2, 0.2) 0.02574748520537792\n(50, 4, 0.2) 0.027025076926534965\n(50, 8, 0.2) 0.02996617514898292\n(50, 16, 0.2) 0.0325398275107521\n(50, 32, 0.2) 0.033999931988691494\n(50, 0.5, 0.4) 0.023285395071747234\n(50, 1, 0.4) 0.027370445737669787\n(50, 2, 0.4) 0.031849209584776265\n(50, 4, 0.4) 0.03581237098007778\n(50, 8, 0.4) 0.036933436368416034\n(50, 16, 0.4) 0.038245807603044156\n(50, 32, 0.4) 0.03707326800340807\nBEST (50, 16, 0.4)\nfitting svd\nexplained 0.20060369961259972\n0 128\n128 256\n256 384\n384 512\n512 640\n640 768\n768 896\n896 1024\n1024 1152\n1152 1280\n1280 1408\n1408 1536\n1536 1664\n1664 1792\n1792 1920\n1920 2048\n2048 2176\n2176 2304\n2304 2432\n2432 2560\n2560 2688\n2688 2816\n2816 2944\n2944 3072\n3072 3200\n3200 3328\n3328 3456\n3456 3584\n3584 3712\n3712 3840\n3840 3968\n3968 4096\n4096 4224\n4224 4352\n4352 4480\n4480 4608\n4608 4736\n4736 4864\n4864 4992\n4992 5120\n5120 5248\n5248 5376\n5376 5504\n5504 5632\n5632 5760\n5760 5888\n5888 6016\n6016 6144\n6144 6272\n6272 6400\n6400 6528\n6528 6656\n6656 6784\n6784 6912\n6912 7040\n7040 7168\n7168 7296\n7296 7424\n7424 7552\n7552 7680\n7680 7808\n7808 7936\n7936 8064\n8064 8192\n8192 8320\n8320 8448\n8448 8456\n(0.25, 0) 0.02981963216624465\n(0.5, 0) 0.03073045630206507\n(1, 0) 0.03338509310877335\n(2, 0) 0.03508009225392361\n(4, 0) 0.03716977080511784\n(8, 0) 0.0359337156883778\n(16, 0) 0.03517463827950864\n(0.25, 0.25) 0.029433420267078902\n(0.5, 0.25) 0.030377409216367075\n(1, 0.25) 0.03293188601108482\n(2, 0.25) 0.034764176467558666\n(4, 0.25) 0.0370869254675664\n(8, 0.25) 0.03575816988741424\n(16, 0.25) 0.0348759604687817\n(0.25, 0.5) 0.02824017929507411\n(0.5, 0.5) 0.02992949597962021\n(1, 0.5) 0.032524123799449786\n(2, 0.5) 0.03491625417957597\n(4, 0.5) 0.03687164301733166\n(8, 0.5) 0.03530296978151344\n(16, 0.5) 0.034494820569227445\n(0.25, 0.75) 0.02771895032538095\n(0.5, 0.75) 0.029482822958099154\n(1, 0.75) 0.03190131173645258\n(2, 0.75) 0.03474809565056204\n(4, 0.75) 0.03621383537731388\n(8, 0.75) 0.035308428552190955\n(16, 0.75) 0.03409345400855306\n(0.25, 1) 0.027329793281049772\n(0.5, 1) 0.029388134306296304\n(1, 1) 0.031881082573745965\n(2, 1) 0.034948145776828685\n(4, 1) 0.03580717495268575\n(8, 1) 0.035332955138800644\n(16, 1) 0.03383064411794568\nBEST (4, 0) 0.03716977080511784\n" } } ## Step 66 · agent · 2026-09-23T22:08:02.344Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:08:20.261Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_fusion.py\\n+from pathlib import Path\\n+import sys\\n+import numpy as np, pandas as pd\\n+sys.path.insert(0,'/task')\\n+from evaluate_public import validation_truth\\n+from ranking_evaluation import CandidateSet, ndcg_at_k\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+a=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\\n+def ranks(x): return {int(u):dict(zip(y.item_id.astype(int),y['rank'].astype(int))) for u,y in x.groupby('uid',sort=False)}\\n+A=ranks(a);B=ranks(b);G=ranks(g); elig=CandidateSet(D/'validation_eligible_candidates.npz'); truth=validation_truth(elig,set(A))\\n+best=(-1,None)\\n+for miss in (110,120,130,150,180):\\n+ for w in (.3,.5,.65,.75,.85,1,1.2):\\n+ for wg in (0,.05,.1,.15,.25,.4):\\n+ vals=[]\\n+ for u,gold in truth.items():\\n+ ar=A[u];br=B[u];gr=G[u]; union=set(ar)|set(br)|set(gr)\\n+ top=sorted(union,key=lambda i:-ar.get(i,miss)-w*br.get(i,miss)-wg*gr.get(i,miss),reverse=True)[:10]\\n+ vals.append(ndcg_at_k(top,gold,10))\\n+ v=float(np.mean(vals))\\n+ if v>best[0]: best=(v,(miss,w,wg))\\n+print('BEST',best)\\n+miss,w,wg=best[1]; rows=[]\\n+for u,ar in A.items():\\n+ br=B[u];gr=G[u];union=set(ar)|set(br)|set(gr);top=sorted(union,key=lambda i:-ar.get(i,miss)-w*br.get(i,miss)-wg*gr.get(i,miss),reverse=True)[:100]\\n+ rows.extend((u,i,r) for r,i in enumerate(top,1))\\n+pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_best.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:08:20.376Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0363e7eb-0f16-48e1-ac31-606ab12223e3", "changes": { "/app/solution/tune_fusion.py": { "type": "add", "content": "from pathlib import Path\nimport sys\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nD=Path('/task/data'); O=Path('/app/solution')\na=pd.read_parquet(O/'validation_cf.parquet'); b=pd.read_parquet(O/'validation_svd.parquet'); g=pd.read_parquet(O/'val_like7.parquet')\ndef ranks(x): return {int(u):dict(zip(y.item_id.astype(int),y['rank'].astype(int))) for u,y in x.groupby('uid',sort=False)}\nA=ranks(a);B=ranks(b);G=ranks(g); elig=CandidateSet(D/'validation_eligible_candidates.npz'); truth=validation_truth(elig,set(A))\nbest=(-1,None)\nfor miss in (110,120,130,150,180):\n for w in (.3,.5,.65,.75,.85,1,1.2):\n for wg in (0,.05,.1,.15,.25,.4):\n vals=[]\n for u,gold in truth.items():\n ar=A[u];br=B[u];gr=G[u]; union=set(ar)|set(br)|set(gr)\n top=sorted(union,key=lambda i:-ar.get(i,miss)-w*br.get(i,miss)-wg*gr.get(i,miss),reverse=True)[:10]\n vals.append(ndcg_at_k(top,gold,10))\n v=float(np.mean(vals))\n if v>best[0]: best=(v,(miss,w,wg))\nprint('BEST',best)\nmiss,w,wg=best[1]; rows=[]\nfor u,ar in A.items():\n br=B[u];gr=G[u];union=set(ar)|set(br)|set(gr);top=sorted(union,key=lambda i:-ar.get(i,miss)-w*br.get(i,miss)-wg*gr.get(i,miss),reverse=True)[:100]\n rows.extend((u,i,r) for r,i in enumerate(top,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_best.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_fusion.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44db71aac87d2af2b678d8c6f7c34", "usage": { "input_tokens": 55320, "cached_input_tokens": 54272, "cache_write_input_tokens": 0, "output_tokens": 834, "reasoning_output_tokens": 264, "total_tokens": 56154 }, "turn_token_usage": { "input_tokens": 2296844, "cached_input_tokens": 2208512, "cache_write_input_tokens": 0, "output_tokens": 23278, "reasoning_output_tokens": 7601, "total_tokens": 2320122 }, "thread_token_usage": { "input_tokens": 2296844, "cached_input_tokens": 2208512, "cache_write_input_tokens": 0, "output_tokens": 23278, "reasoning_output_tokens": 7601, "total_tokens": 2320122 } } ### exec · 2026-09-23T22:08:24.656Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/tune_fusion.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:08:54.822Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"c65e3c","wall_time_seconds":30.000998184,"session_id":37023,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44dd4b22887d2bb562c3593b0f745", "usage": { "input_tokens": 56178, "cached_input_tokens": 55168, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 0, "total_tokens": 56265 }, "turn_token_usage": { "input_tokens": 2353022, "cached_input_tokens": 2263680, "cache_write_input_tokens": 0, "output_tokens": 23365, "reasoning_output_tokens": 7601, "total_tokens": 2376387 }, "thread_token_usage": { "input_tokens": 2353022, "cached_input_tokens": 2263680, "cache_write_input_tokens": 0, "output_tokens": 23365, "reasoning_output_tokens": 7601, "total_tokens": 2376387 } } ## Step 67 · agent · 2026-09-23T22:08:57.183Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:08:58.422Z { "code": "const r = await tools.write_stdin({\"session_id\":37023,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:09:28.430Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"9db79b","wall_time_seconds":30.000858695,"session_id":37023,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44df70eec87d29e8cafd04ce29c7a", "usage": { "input_tokens": 56324, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 26, "total_tokens": 56398 }, "turn_token_usage": { "input_tokens": 2409346, "cached_input_tokens": 2319616, "cache_write_input_tokens": 0, "output_tokens": 23439, "reasoning_output_tokens": 7627, "total_tokens": 2432785 }, "thread_token_usage": { "input_tokens": 2409346, "cached_input_tokens": 2319616, "cache_write_input_tokens": 0, "output_tokens": 23439, "reasoning_output_tokens": 7627, "total_tokens": 2432785 } } ## Step 68 · agent · 2026-09-23T22:09:30.803Z ### Thinking unavailable — no text recorded The selected seven-day models have been restored exactly. I’m evaluating fusion weights directly against the public labels (without touching the test artifact); this is the last model-selection step, followed only by a final structural verification. ### exec · 2026-09-23T22:09:33.774Z { "code": "const r = await tools.write_stdin({\"session_id\":37023,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-23T22:09:47.699Z Script completed Wall time 13.9 seconds Output: {"chunk_id":"372c4f","wall_time_seconds":13.914305729,"exit_code":0,"original_token_count":45,"output":"BEST (0.04002329934569416, (110, 0.75, 0.1))\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040023,\n \"quality_users\": 4263\n }\n}\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44e18ace887d28be7759c359b1ce6", "usage": { "input_tokens": 56456, "cached_input_tokens": 56192, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 25, "total_tokens": 56579 }, "turn_token_usage": { "input_tokens": 2465802, "cached_input_tokens": 2375808, "cache_write_input_tokens": 0, "output_tokens": 23562, "reasoning_output_tokens": 7652, "total_tokens": 2489364 }, "thread_token_usage": { "input_tokens": 2465802, "cached_input_tokens": 2375808, "cache_write_input_tokens": 0, "output_tokens": 23562, "reasoning_output_tokens": 7652, "total_tokens": 2489364 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ce6f43a-1bc4-406a-b486-bb1d194a59f0", "process_id": "37023", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/tune_fusion.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/tune_fusion.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "BEST (0.04002329934569416, (110, 0.75, 0.1))\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040023,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "BEST (0.04002329934569416, (110, 0.75, 0.1))\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040023,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 82, "nanos": 876815557 }, "formatted_output": "BEST (0.04002329934569416, (110, 0.75, 0.1))\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040023,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 69 · agent · 2026-09-23T22:09:51.781Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:58.853Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_submission.py\\n@@\\n-# Validation-selected rank fusion: CF + .75*SVD + .15*global.\\n+# Validation-selected rank fusion: CF + .75*SVD + .10*global.\\n@@\\n- union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,130)-.75*br.get(i,130)-.15*gr.get(i,130) for i in union}\\n+ union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,110)-.75*br.get(i,110)-.10*gr.get(i,110) for i in union}\\n*** Update File: /app/solution/README.md\\n@@\\n-state pairs are ineligible). The selected fusion scored **0.040009 public\\n+state pairs are ineligible). The selected fusion scored **0.040023 public\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:09:58.992Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b08c08bf-7ae4-4e91-8f12-9e38382784ed", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -18,3 +18,3 @@\n enforced by the supplied cutoff-correct eligibility mask (all active explicit\n-state pairs are ineligible). The selected fusion scored **0.040009 public\n+state pairs are ineligible). The selected fusion scored **0.040023 public\n NDCG@10**, versus 0.029297 for the strongest standalone trend baseline and\n", "move_path": null }, "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -76,3 +76,3 @@\n \n-# Validation-selected rank fusion: CF + .75*SVD + .15*global.\n+# Validation-selected rank fusion: CF + .75*SVD + .10*global.\n out=np.empty_like(cf_top)\n@@ -80,3 +80,3 @@\n ar={int(i):q for q,i in enumerate(cf_top[r],1)}; br={int(i):q for q,i in enumerate(svd_top[r],1)}; gr={int(i):q for q,i in enumerate(global_top[r],1)}\n- union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,130)-.75*br.get(i,130)-.15*gr.get(i,130) for i in union}\n+ union=set(ar)|set(br)|set(gr); score={i:-ar.get(i,110)-.75*br.get(i,110)-.10*gr.get(i,110) for i in union}\n out[r]=sorted(union,key=score.get,reverse=True)[:100]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_submission.py\nM /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44e2bdf7c87d2aa0f99135e7be7ee", "usage": { "input_tokens": 56714, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 294, "reasoning_output_tokens": 64, "total_tokens": 57008 }, "turn_token_usage": { "input_tokens": 2522516, "cached_input_tokens": 2432128, "cache_write_input_tokens": 0, "output_tokens": 23856, "reasoning_output_tokens": 7716, "total_tokens": 2546372 }, "thread_token_usage": { "input_tokens": 2522516, "cached_input_tokens": 2432128, "cache_write_input_tokens": 0, "output_tokens": 23856, "reasoning_output_tokens": 7716, "total_tokens": 2546372 } } ## Step 70 · agent · 2026-09-23T22:10:02.740Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:10:04.467Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n" } ### Tool output · Timestamp not recorded No response recorded. ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "session_id": "01a0d036-320f-7770-b6ce-97d2499a8eb7", "root_turn_id": "01a0d036-321d-7f32-9e56-12ef187c1f5d", "response_id": "resp_0873099e47eab4cc016ab44e3744b887d2aa089094636da9fe", "usage": { "input_tokens": 57032, "cached_input_tokens": 56576, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 14, "total_tokens": 57111 }, "turn_token_usage": { "input_tokens": 2579548, "cached_input_tokens": 2488704, "cache_write_input_tokens": 0, "output_tokens": 23935, "reasoning_output_tokens": 7730, "total_tokens": 2603483 }, "thread_token_usage": { "input_tokens": 2579548, "cached_input_tokens": 2488704, "cache_write_input_tokens": 0, "output_tokens": 23935, "reasoning_output_tokens": 7730, "total_tokens": 2603483 } }