yambda-discovery-ranking-v01 / sol-medium-0-153-4-basic-r04-20260924T221413Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-25T00:53:46.565Z 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 · 2026-09-25T00:53:47.954Z ## 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 · 2026-09-25T00:53:47.954Z 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 · 2026-09-25T00:53:47.954Z 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 · 2026-09-25T00:53:47.954Z 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 2026-09-25 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": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "medium", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "medium", "summary": "auto" } ## Step 6 · user · 2026-09-25T00:53:47.973Z # 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 · 2026-09-25T00:53:50.091Z ### Thinking unavailable — no text recorded I’ll first profile the event volumes, candidate encoding, and validation label coverage. Then I’ll build a leakage-safe ranker, score it publicly, iterate on the highest-impact signals, and refit the selected recipe on train+validation for the final artifact. I’ll keep a valid submission on disk early. ### exec · 2026-09-25T00:53:52.797Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /task/data && sed -n '1,200p' /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:53:52.865Z Script completed Wall time 0.1 seconds Output: total 340M -r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet -r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet -r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet # 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 """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), } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c61c291c87d288406ad054e1fe2e", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 176, "reasoning_output_tokens": 22, "total_tokens": 14513 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 176, "reasoning_output_tokens": 22, "total_tokens": 14513 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 176, "reasoning_output_tokens": 22, "total_tokens": 14513 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b5f75979-1faa-482e-93c4-6818d1a54f08", "process_id": "96923", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && sed -n '1,200p' /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,200p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "stderr": "", "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6740 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n" } } ## Step 8 · agent · 2026-09-25T00:53:54.141Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:53:59.173Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, numpy as np, glob, os\\nfor f in glob.glob('/task/data/*.parquet'):\\n d=pd.read_parquet(f)\\n print(os.path.basename(f),len(d),d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\\nfor stage in ['validation','test']:\\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]),'same items catalog?',len(z['item_ids']))\\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:53:59.234Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ad90a2ac-f4aa-4d95-a4a0-c805d89e68b8", "process_id": "70728", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, numpy as np, glob, os\nfor f in glob.glob('/task/data/*.parquet'):\n d=pd.read_parquet(f)\n print(os.path.basename(f),len(d),d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\nfor stage in ['validation','test']:\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]),'same items catalog?',len(z['item_ids']))\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, numpy as np, glob, os\nfor f in glob.glob('/task/data/*.parquet'):\n d=pd.read_parquet(f)\n print(os.path.basename(f),len(d),d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\nfor stage in ['validation','test']:\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]),'same items catalog?',len(z['item_ids']))\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3591 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6211aa887d2838a3d9bcdfa7342", "usage": { "input_tokens": 17428, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 310, "reasoning_output_tokens": 22, "total_tokens": 17738 }, "turn_token_usage": { "input_tokens": 31765, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 486, "reasoning_output_tokens": 44, "total_tokens": 32251 }, "thread_token_usage": { "input_tokens": 31765, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 486, "reasoning_output_tokens": 44, "total_tokens": 32251 } } ## Step 9 · agent · 2026-09-25T00:54:00.948Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:02.289Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; ls -la /app; find /opt -maxdepth 3 -type f -name python 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:54:02.381Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 total 12 drwxr-xr-x 3 agent agent 4096 Sep 25 00:50 . drwxr-xr-x 1 root root 4096 Sep 25 00:53 .. drwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4df7e45-bdc0-429f-8832-2f82612584bb", "process_id": "75171", "command": [ "/bin/sh", "-lc", "which -a python python3; ls -la /app; find /opt -maxdepth 3 -type f -name python 2>/dev/null | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3; ls -la /app; find /opt -maxdepth 3 -type f -name python 2>/dev/null | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3510 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c627861c87d29071215c14feb38a", "usage": { "input_tokens": 17794, "cached_input_tokens": 17280, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 23, "total_tokens": 17898 }, "turn_token_usage": { "input_tokens": 49559, "cached_input_tokens": 39552, "cache_write_input_tokens": 0, "output_tokens": 590, "reasoning_output_tokens": 67, "total_tokens": 50149 }, "thread_token_usage": { "input_tokens": 49559, "cached_input_tokens": 39552, "cache_write_input_tokens": 0, "output_tokens": 590, "reasoning_output_tokens": 67, "total_tokens": 50149 } } ## Step 10 · agent · 2026-09-25T00:54:03.659Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:05.034Z { "code": "const r = await tools.exec_command({cmd:\"find / -type d -name 'pandas' 2>/dev/null | head -20; env | sort | sed -n '1,120p'; ls -la /task\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:54:05.439Z Script completed Wall time 0.4 seconds Output: /opt/task-venv/lib/python3.12/site-packages/pandas CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_MANAGED_BY_NPM=1 CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex CODEX_SESSION_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0 CODEX_THREAD_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=c4f7b664e68f LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin NVM_CD_FLAGS= NVM_DIR=/home/agent/.nvm NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node PAGER=cat PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/home/agent/.nvm/versions/node/v22.23.3/bin/codex total 36 dr-xr-xr-x 1 root root 4096 Sep 25 00:50 . drwxr-xr-x 1 root root 4096 Sep 25 00:53 .. dr-xr-xr-x 1 root root 4096 Sep 25 00:47 data -r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c62aabf087d2b8b9276ecc83d645", "usage": { "input_tokens": 18011, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 12, "total_tokens": 18109 }, "turn_token_usage": { "input_tokens": 67570, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 688, "reasoning_output_tokens": 79, "total_tokens": 68258 }, "thread_token_usage": { "input_tokens": 67570, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 688, "reasoning_output_tokens": 79, "total_tokens": 68258 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-53ad0c3d-2721-4832-b876-1f451559b80b", "process_id": "13393", "command": [ "/bin/sh", "-lc", "find / -type d -name 'pandas' 2>/dev/null | head -20; env | sort | sed -n '1,120p'; ls -la /task" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d -name 'pandas' 2>/dev/null | head -20; env | sort | sed -n '1,120p'; ls -la /task" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCODEX_THREAD_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4f7b664e68f\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCODEX_THREAD_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4f7b664e68f\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 236971675 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCODEX_THREAD_ID=01a0d60d-d7ae-7d51-a878-21e6460c44b0\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4f7b664e68f\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n" } } ## Step 11 · agent · 2026-09-25T00:54:06.734Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:11.576Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, glob, os\\nfor f in glob.glob('/task/data/*.parquet'):\\n d=pd.read_parquet(f)\\n print(os.path.basename(f),len(d), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\\nfor stage in ['validation','test']:\\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]))\\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:54:15.261Z Script completed Wall time 3.7 seconds Output: val_likes.parquet 37795 {'uid': (np.uint32(100), np.uint32(999900), 4114), 'timestamp': (np.uint32(24788815), np.uint32(25393390), 19790), 'item_id': (np.uint32(209), np.uint32(9390461), 22902)} candidate_catalog.parquet 30000 {'item_id': (np.uint32(206), np.uint32(9390294), 30000)} val_listens.parquet 1501697 {'uid': (np.uint32(100), np.uint32(1000000), 8196), 'timestamp': (np.uint32(24788600), np.uint32(25393395), 119851), 'item_id': (np.uint32(50), np.uint32(9390541), 191747)} train_unlikes.parquet 292665 {'uid': (np.uint32(100), np.uint32(1000000), 6197), 'timestamp': (np.uint32(250), np.uint32(24786570), 87635), 'item_id': (np.uint32(26), np.uint32(9390601), 113072)} train_likes.parquet 814831 {'uid': (np.uint32(100), np.uint32(1000000), 8144), 'timestamp': (np.uint32(80), np.uint32(24786790), 562774), 'item_id': (np.uint32(26), np.uint32(9390522), 172303)} train_listens.parquet 43484159 {'uid': (np.uint32(100), np.uint32(1000000), 9238), 'timestamp': (np.uint32(0), np.uint32(24786795), 4806896), 'item_id': (np.uint32(22), np.uint32(9390623), 845395)} validation_preference_state.parquet 469124 {'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814)} test_preference_state.parquet 484776 {'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863)} train_undislikes.parquet 19960 {'uid': (np.uint32(100), np.uint32(999900), 2769), 'timestamp': (np.uint32(15950), np.uint32(24785395), 6956), 'item_id': (np.uint32(178), np.uint32(9390240), 14725)} train_dislikes.parquet 101075 {'uid': (np.uint32(100), np.uint32(1000000), 5732), 'timestamp': (np.uint32(75), np.uint32(24786580), 67779), 'item_id': (np.uint32(105), np.uint32(9390541), 50876)} val_unlikes.parquet 9346 {'uid': (np.uint32(1100), np.uint32(998800), 1313), 'timestamp': (np.uint32(24789005), np.uint32(25393385), 2941), 'item_id': (np.uint32(1928), np.uint32(9390163), 8080)} target_users.parquet 8456 {'uid': (np.uint32(100), np.uint32(1000000), 8456)} val_undislikes.parquet 606 {'uid': (np.uint32(900), np.uint32(998800), 205), 'timestamp': (np.uint32(24810715), np.uint32(25393035), 242), 'item_id': (np.uint32(15149), np.uint32(9385279), 579)} val_dislikes.parquet 3563 {'uid': (np.uint32(1600), np.uint32(998800), 955), 'timestamp': (np.uint32(24789920), np.uint32(25393280), 2386), 'item_id': (np.uint32(651), np.uint32(9388630), 3135)} validation (8456,) (30000,) (8456, 3750) eligible [24657. 26724.2 29242. 29979. 30000. ] test (8456,) (30000,) (8456, 3750) eligible [24552. 26672.7 29220. 29975. 30000. ] 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 Index(['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'], dtype='object') ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c62da85487d294e277a4edd24091", "usage": { "input_tokens": 18747, "cached_input_tokens": 17664, "cache_write_input_tokens": 0, "output_tokens": 292, "reasoning_output_tokens": 14, "total_tokens": 19039 }, "turn_token_usage": { "input_tokens": 86317, "cached_input_tokens": 71424, "cache_write_input_tokens": 0, "output_tokens": 980, "reasoning_output_tokens": 93, "total_tokens": 87297 }, "thread_token_usage": { "input_tokens": 86317, "cached_input_tokens": 71424, "cache_write_input_tokens": 0, "output_tokens": 980, "reasoning_output_tokens": 93, "total_tokens": 87297 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8a546161-8f3c-4979-91a2-38600223a9e3", "process_id": "45358", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, glob, os\nfor f in glob.glob('/task/data/*.parquet'):\n d=pd.read_parquet(f)\n print(os.path.basename(f),len(d), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\nfor stage in ['validation','test']:\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]))\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, glob, os\nfor f in glob.glob('/task/data/*.parquet'):\n d=pd.read_parquet(f)\n print(os.path.basename(f),len(d), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d if c in ['uid','item_id','timestamp']})\nfor stage in ['validation','test']:\n z=np.load(f'/task/data/{stage}_eligible_candidates.npz')\n counts=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1)\n print(stage, z['uids'].shape,z['item_ids'].shape,z['packed_eligible'].shape, 'eligible',np.percentile(counts,[0,1,50,99,100]))\nprint(pd.read_parquet('/task/data/candidate_catalog.parquet').head(),pd.read_parquet('/task/data/candidate_catalog.parquet').columns)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "val_likes.parquet 37795 {'uid': (np.uint32(100), np.uint32(999900), 4114), 'timestamp': (np.uint32(24788815), np.uint32(25393390), 19790), 'item_id': (np.uint32(209), np.uint32(9390461), 22902)}\ncandidate_catalog.parquet 30000 {'item_id': (np.uint32(206), np.uint32(9390294), 30000)}\nval_listens.parquet 1501697 {'uid': (np.uint32(100), np.uint32(1000000), 8196), 'timestamp': (np.uint32(24788600), np.uint32(25393395), 119851), 'item_id': (np.uint32(50), np.uint32(9390541), 191747)}\ntrain_unlikes.parquet 292665 {'uid': (np.uint32(100), np.uint32(1000000), 6197), 'timestamp': (np.uint32(250), np.uint32(24786570), 87635), 'item_id': (np.uint32(26), np.uint32(9390601), 113072)}\ntrain_likes.parquet 814831 {'uid': (np.uint32(100), np.uint32(1000000), 8144), 'timestamp': (np.uint32(80), np.uint32(24786790), 562774), 'item_id': (np.uint32(26), np.uint32(9390522), 172303)}\ntrain_listens.parquet 43484159 {'uid': (np.uint32(100), np.uint32(1000000), 9238), 'timestamp': (np.uint32(0), np.uint32(24786795), 4806896), 'item_id': (np.uint32(22), np.uint32(9390623), 845395)}\nvalidation_preference_state.parquet 469124 {'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814)}\ntest_preference_state.parquet 484776 {'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863)}\ntrain_undislikes.parquet 19960 {'uid': (np.uint32(100), np.uint32(999900), 2769), 'timestamp': (np.uint32(15950), np.uint32(24785395), 6956), 'item_id': (np.uint32(178), np.uint32(9390240), 14725)}\ntrain_dislikes.parquet 101075 {'uid': (np.uint32(100), np.uint32(1000000), 5732), 'timestamp': (np.uint32(75), np.uint32(24786580), 67779), 'item_id': (np.uint32(105), np.uint32(9390541), 50876)}\nval_unlikes.parquet 9346 {'uid': (np.uint32(1100), np.uint32(998800), 1313), 'timestamp': (np.uint32(24789005), np.uint32(25393385), 2941), 'item_id': (np.uint32(1928), np.uint32(9390163), 8080)}\ntarget_users.parquet 8456 {'uid': (np.uint32(100), np.uint32(1000000), 8456)}\nval_undislikes.parquet 606 {'uid': (np.uint32(900), np.uint32(998800), 205), 'timestamp': (np.uint32(24810715), np.uint32(25393035), 242), 'item_id': (np.uint32(15149), np.uint32(9385279), 579)}\nval_dislikes.parquet 3563 {'uid': (np.uint32(1600), np.uint32(998800), 955), 'timestamp': (np.uint32(24789920), np.uint32(25393280), 2386), 'item_id': (np.uint32(651), np.uint32(9388630), 3135)}\nvalidation (8456,) (30000,) (8456, 3750) eligible [24657. 26724.2 29242. 29979. 30000. ]\ntest (8456,) (30000,) (8456, 3750) eligible [24552. 26672.7 29220. 29975. 30000. ]\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 Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n", "stderr": "", "aggregated_output": "val_likes.parquet 37795 {'uid': (np.uint32(100), np.uint32(999900), 4114), 'timestamp': (np.uint32(24788815), np.uint32(25393390), 19790), 'item_id': (np.uint32(209), np.uint32(9390461), 22902)}\ncandidate_catalog.parquet 30000 {'item_id': (np.uint32(206), np.uint32(9390294), 30000)}\nval_listens.parquet 1501697 {'uid': (np.uint32(100), np.uint32(1000000), 8196), 'timestamp': (np.uint32(24788600), np.uint32(25393395), 119851), 'item_id': (np.uint32(50), np.uint32(9390541), 191747)}\ntrain_unlikes.parquet 292665 {'uid': (np.uint32(100), np.uint32(1000000), 6197), 'timestamp': (np.uint32(250), np.uint32(24786570), 87635), 'item_id': (np.uint32(26), np.uint32(9390601), 113072)}\ntrain_likes.parquet 814831 {'uid': (np.uint32(100), np.uint32(1000000), 8144), 'timestamp': (np.uint32(80), np.uint32(24786790), 562774), 'item_id': (np.uint32(26), np.uint32(9390522), 172303)}\ntrain_listens.parquet 43484159 {'uid': (np.uint32(100), np.uint32(1000000), 9238), 'timestamp': (np.uint32(0), np.uint32(24786795), 4806896), 'item_id': (np.uint32(22), np.uint32(9390623), 845395)}\nvalidation_preference_state.parquet 469124 {'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814)}\ntest_preference_state.parquet 484776 {'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863)}\ntrain_undislikes.parquet 19960 {'uid': (np.uint32(100), np.uint32(999900), 2769), 'timestamp': (np.uint32(15950), np.uint32(24785395), 6956), 'item_id': (np.uint32(178), np.uint32(9390240), 14725)}\ntrain_dislikes.parquet 101075 {'uid': (np.uint32(100), np.uint32(1000000), 5732), 'timestamp': (np.uint32(75), np.uint32(24786580), 67779), 'item_id': (np.uint32(105), np.uint32(9390541), 50876)}\nval_unlikes.parquet 9346 {'uid': (np.uint32(1100), np.uint32(998800), 1313), 'timestamp': (np.uint32(24789005), np.uint32(25393385), 2941), 'item_id': (np.uint32(1928), np.uint32(9390163), 8080)}\ntarget_users.parquet 8456 {'uid': (np.uint32(100), np.uint32(1000000), 8456)}\nval_undislikes.parquet 606 {'uid': (np.uint32(900), np.uint32(998800), 205), 'timestamp': (np.uint32(24810715), np.uint32(25393035), 242), 'item_id': (np.uint32(15149), np.uint32(9385279), 579)}\nval_dislikes.parquet 3563 {'uid': (np.uint32(1600), np.uint32(998800), 955), 'timestamp': (np.uint32(24789920), np.uint32(25393280), 2386), 'item_id': (np.uint32(651), np.uint32(9388630), 3135)}\nvalidation (8456,) (30000,) (8456, 3750) eligible [24657. 26724.2 29242. 29979. 30000. ]\ntest (8456,) (30000,) (8456, 3750) eligible [24552. 26672.7 29220. 29975. 30000. ]\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 Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 516387767 }, "formatted_output": "val_likes.parquet 37795 {'uid': (np.uint32(100), np.uint32(999900), 4114), 'timestamp': (np.uint32(24788815), np.uint32(25393390), 19790), 'item_id': (np.uint32(209), np.uint32(9390461), 22902)}\ncandidate_catalog.parquet 30000 {'item_id': (np.uint32(206), np.uint32(9390294), 30000)}\nval_listens.parquet 1501697 {'uid': (np.uint32(100), np.uint32(1000000), 8196), 'timestamp': (np.uint32(24788600), np.uint32(25393395), 119851), 'item_id': (np.uint32(50), np.uint32(9390541), 191747)}\ntrain_unlikes.parquet 292665 {'uid': (np.uint32(100), np.uint32(1000000), 6197), 'timestamp': (np.uint32(250), np.uint32(24786570), 87635), 'item_id': (np.uint32(26), np.uint32(9390601), 113072)}\ntrain_likes.parquet 814831 {'uid': (np.uint32(100), np.uint32(1000000), 8144), 'timestamp': (np.uint32(80), np.uint32(24786790), 562774), 'item_id': (np.uint32(26), np.uint32(9390522), 172303)}\ntrain_listens.parquet 43484159 {'uid': (np.uint32(100), np.uint32(1000000), 9238), 'timestamp': (np.uint32(0), np.uint32(24786795), 4806896), 'item_id': (np.uint32(22), np.uint32(9390623), 845395)}\nvalidation_preference_state.parquet 469124 {'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814)}\ntest_preference_state.parquet 484776 {'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863)}\ntrain_undislikes.parquet 19960 {'uid': (np.uint32(100), np.uint32(999900), 2769), 'timestamp': (np.uint32(15950), np.uint32(24785395), 6956), 'item_id': (np.uint32(178), np.uint32(9390240), 14725)}\ntrain_dislikes.parquet 101075 {'uid': (np.uint32(100), np.uint32(1000000), 5732), 'timestamp': (np.uint32(75), np.uint32(24786580), 67779), 'item_id': (np.uint32(105), np.uint32(9390541), 50876)}\nval_unlikes.parquet 9346 {'uid': (np.uint32(1100), np.uint32(998800), 1313), 'timestamp': (np.uint32(24789005), np.uint32(25393385), 2941), 'item_id': (np.uint32(1928), np.uint32(9390163), 8080)}\ntarget_users.parquet 8456 {'uid': (np.uint32(100), np.uint32(1000000), 8456)}\nval_undislikes.parquet 606 {'uid': (np.uint32(900), np.uint32(998800), 205), 'timestamp': (np.uint32(24810715), np.uint32(25393035), 242), 'item_id': (np.uint32(15149), np.uint32(9385279), 579)}\nval_dislikes.parquet 3563 {'uid': (np.uint32(1600), np.uint32(998800), 955), 'timestamp': (np.uint32(24789920), np.uint32(25393280), 2386), 'item_id': (np.uint32(651), np.uint32(9388630), 3135)}\nvalidation (8456,) (30000,) (8456, 3750) eligible [24657. 26724.2 29242. 29979. 30000. ]\ntest (8456,) (30000,) (8456, 3750) eligible [24552. 26672.7 29220. 29975. 30000. ]\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 Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n" } } ## Step 12 · agent · 2026-09-25T00:54:18.616Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:19.271Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:54:19.321Z Script completed Wall time 0.0 seconds Output: #!/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() ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9e99ad35-0eca-4aea-bc0a-f09f109bf8a2", "process_id": "43991", "command": [ "/bin/sh", "-lc", "sed -n '1,260p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1,260p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3931 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c637746c87d295874732ff99da96", "usage": { "input_tokens": 20209, "cached_input_tokens": 17792, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 87, "total_tokens": 20358 }, "turn_token_usage": { "input_tokens": 106526, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 1129, "reasoning_output_tokens": 180, "total_tokens": 107655 }, "thread_token_usage": { "input_tokens": 106526, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 1129, "reasoning_output_tokens": 180, "total_tokens": 107655 } } ## Step 13 · agent · 2026-09-25T00:54:27.714Z ### Thinking unavailable — no text recorded The catalog is broad (30,000 tracks) and eligibility removes only a modest user-specific slice, so global popularity is a useful fallback but unlikely to be sufficient. I’m testing it first to establish a floor, while checking whether the cutoff candidates exclude already-consumed tracks—which determines whether repeat-affinity or collaborative discovery should dominate. ### exec · 2026-09-25T00:54:37.053Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build leakage-safe validation and final music discovery rankings.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+\\n+\\n+def load_candidates(stage):\\n+ z = np.load(DATA / f'{stage}_eligible_candidates.npz')\\n+ uids = z['uids'].astype(np.int64)\\n+ items = z['item_ids'].astype(np.int64)\\n+ eligible = np.unpackbits(z['packed_eligible'], axis=1, count=len(items), bitorder='little').astype(bool)\\n+ return uids, items, eligible\\n+\\n+\\n+def popularity(stage, path):\\n+ uids, items, eligible = load_candidates(stage)\\n+ catalog = pd.read_parquet(DATA / 'candidate_catalog.parquet').set_index('item_id')\\n+ score = np.log1p(catalog.loc[items, 'organic_positive_listens'].to_numpy())\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ ix = np.flatnonzero(eligible[r])\\n+ top = ix[np.argsort(-score[ix], kind='stable')[:100]]\\n+ rows.append(pd.DataFrame({'uid': uid, 'item_id': items[top], 'rank': np.arange(1,101)}))\\n+ pd.concat(rows, ignore_index=True).to_parquet(path, index=False)\\n+\\n+\\n+if __name__ == '__main__':\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); a=ap.parse_args()\\n+ if a.baseline:\\n+ popularity('validation', OUT/'validation_pop.parquet')\\n+ popularity('test', OUT/'submission.parquet')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:54:37.106Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-dad9b4e6-8da1-453b-8766-f56fa181ff1a", "changes": { "/app/solution/build_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build leakage-safe validation and final music discovery rankings.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/task/data')\nOUT = Path('/app/solution')\n\n\ndef load_candidates(stage):\n z = np.load(DATA / f'{stage}_eligible_candidates.npz')\n uids = z['uids'].astype(np.int64)\n items = z['item_ids'].astype(np.int64)\n eligible = np.unpackbits(z['packed_eligible'], axis=1, count=len(items), bitorder='little').astype(bool)\n return uids, items, eligible\n\n\ndef popularity(stage, path):\n uids, items, eligible = load_candidates(stage)\n catalog = pd.read_parquet(DATA / 'candidate_catalog.parquet').set_index('item_id')\n score = np.log1p(catalog.loc[items, 'organic_positive_listens'].to_numpy())\n rows = []\n for r, uid in enumerate(uids):\n ix = np.flatnonzero(eligible[r])\n top = ix[np.argsort(-score[ix], kind='stable')[:100]]\n rows.append(pd.DataFrame({'uid': uid, 'item_id': items[top], 'rank': np.arange(1,101)}))\n pd.concat(rows, ignore_index=True).to_parquet(path, index=False)\n\n\nif __name__ == '__main__':\n ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); a=ap.parse_args()\n if a.baseline:\n popularity('validation', OUT/'validation_pop.parquet')\n popularity('test', OUT/'submission.parquet')\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c63b9fc887d2acb4226cefcf3fa0", "usage": { "input_tokens": 21682, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 928, "reasoning_output_tokens": 378, "total_tokens": 22610 }, "turn_token_usage": { "input_tokens": 128208, "cached_input_tokens": 109184, "cache_write_input_tokens": 0, "output_tokens": 2057, "reasoning_output_tokens": 558, "total_tokens": 130265 }, "thread_token_usage": { "input_tokens": 128208, "cached_input_tokens": 109184, "cache_write_input_tokens": 0, "output_tokens": 2057, "reasoning_output_tokens": 558, "total_tokens": 130265 } } ## Step 14 · agent · 2026-09-25T00:54:38.343Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:39.790Z { "code": "const r = await tools.exec_command({cmd:\"ls -l solution && /opt/task-venv/bin/python solution/build_ranker.py --baseline && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:55:09.957Z Script completed Wall time 30.2 seconds Output: total 4 -rw-r--r-- 1 agent agent 1377 Sep 25 00:54 build_ranker.py ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c64d6dfc87d2bb1eff6cbd3d1e79", "usage": { "input_tokens": 22634, "cached_input_tokens": 21504, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 13, "total_tokens": 22735 }, "turn_token_usage": { "input_tokens": 150842, "cached_input_tokens": 130688, "cache_write_input_tokens": 0, "output_tokens": 2158, "reasoning_output_tokens": 571, "total_tokens": 153000 }, "thread_token_usage": { "input_tokens": 150842, "cached_input_tokens": 130688, "cache_write_input_tokens": 0, "output_tokens": 2158, "reasoning_output_tokens": 571, "total_tokens": 153000 } } ## Step 15 · agent · 2026-09-25T00:55:12.268Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:13.398Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution; /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:55:16.091Z Script completed Wall time 2.7 seconds Output: total 688K -rw-r--r-- 1 agent agent 1.4K Sep 25 00:54 build_ranker.py -rw-r--r-- 1 agent agent 682K Sep 25 00:54 validation_pop.parquet { "model": "solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.010577, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c66e504487d2aabf450f6983f405", "usage": { "input_tokens": 22787, "cached_input_tokens": 22400, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 34, "total_tokens": 22893 }, "turn_token_usage": { "input_tokens": 173629, "cached_input_tokens": 153088, "cache_write_input_tokens": 0, "output_tokens": 2264, "reasoning_output_tokens": 605, "total_tokens": 175893 }, "thread_token_usage": { "input_tokens": 173629, "cached_input_tokens": 153088, "cache_write_input_tokens": 0, "output_tokens": 2264, "reasoning_output_tokens": 605, "total_tokens": 175893 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-87def208-cece-4257-a782-47323ca385db", "process_id": "58737", "command": [ "/bin/sh", "-lc", "ls -lh solution; /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution; /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 525373054 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b47d47c7-63cd-40ac-b748-da9910c641df", "process_id": "29003", "command": [ "/bin/sh", "-lc", "ls -l solution && /opt/task-venv/bin/python solution/build_ranker.py --baseline && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -l solution && /opt/task-venv/bin/python solution/build_ranker.py --baseline && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 37, "nanos": 140996886 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 16 · agent · 2026-09-25T00:55:22.174Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:34.814Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,time\\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\\nui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(it)}\\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); truth['eligible']=[mask[ui[int(a)],ii[int(b)]] for a,b in truth[['uid','item_id']].itertuples(index=False)]\\nprint('val strong',len(L),'likes',len(K),'truth',len(truth),'eligible',truth.eligible.sum())\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print('truth pop percentiles',truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\\n# train pairs for future truth\\npairs=set(zip(truth[truth.eligible].uid.astype(int),truth[truth.eligible].item_id.astype(int)))\\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]\\ntr['future_rel']=[(int(a),int(b)) in pairs for a,b in tr[['uid','item_id']].itertuples(index=False)]\\nprint('train catalog events',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0], 'future events prior',tr.future_rel.sum(),'future pairs prior',tr[tr.future_rel][['uid','item_id']].drop_duplicates().shape[0])\\n# eligibility among random unique train pairs\\nq=tr[['uid','item_id']].drop_duplicates(); q=q.sample(min(200000,len(q)),random_state=1); print('prior pair eligible',np.mean([mask[ui[int(a)],ii[int(b)]] for a,b in q.itertuples(index=False)]))\\nprint(tr[tr.future_rel].groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:56:04.980Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c675290c87d2aec64eb9a997ee95", "usage": { "input_tokens": 23017, "cached_input_tokens": 22656, "cache_write_input_tokens": 0, "output_tokens": 904, "reasoning_output_tokens": 172, "total_tokens": 23921 }, "turn_token_usage": { "input_tokens": 196646, "cached_input_tokens": 175744, "cache_write_input_tokens": 0, "output_tokens": 3168, "reasoning_output_tokens": 777, "total_tokens": 199814 }, "thread_token_usage": { "input_tokens": 196646, "cached_input_tokens": 175744, "cache_write_input_tokens": 0, "output_tokens": 3168, "reasoning_output_tokens": 777, "total_tokens": 199814 } } ## Step 17 · agent · 2026-09-25T00:56:06.417Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9be3e867-c868-4af3-8b36-1f954ff56246", "process_id": "92633", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(it)}\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); truth['eligible']=[mask[ui[int(a)],ii[int(b)]] for a,b in truth[['uid','item_id']].itertuples(index=False)]\nprint('val strong',len(L),'likes',len(K),'truth',len(truth),'eligible',truth.eligible.sum())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print('truth pop percentiles',truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\n# train pairs for future truth\npairs=set(zip(truth[truth.eligible].uid.astype(int),truth[truth.eligible].item_id.astype(int)))\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]\ntr['future_rel']=[(int(a),int(b)) in pairs for a,b in tr[['uid','item_id']].itertuples(index=False)]\nprint('train catalog events',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0], 'future events prior',tr.future_rel.sum(),'future pairs prior',tr[tr.future_rel][['uid','item_id']].drop_duplicates().shape[0])\n# eligibility among random unique train pairs\nq=tr[['uid','item_id']].drop_duplicates(); q=q.sample(min(200000,len(q)),random_state=1); print('prior pair eligible',np.mean([mask[ui[int(a)],ii[int(b)]] for a,b in q.itertuples(index=False)]))\nprint(tr[tr.future_rel].groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(it)}\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); truth['eligible']=[mask[ui[int(a)],ii[int(b)]] for a,b in truth[['uid','item_id']].itertuples(index=False)]\nprint('val strong',len(L),'likes',len(K),'truth',len(truth),'eligible',truth.eligible.sum())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print('truth pop percentiles',truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\n# train pairs for future truth\npairs=set(zip(truth[truth.eligible].uid.astype(int),truth[truth.eligible].item_id.astype(int)))\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]\ntr['future_rel']=[(int(a),int(b)) in pairs for a,b in tr[['uid','item_id']].itertuples(index=False)]\nprint('train catalog events',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0], 'future events prior',tr.future_rel.sum(),'future pairs prior',tr[tr.future_rel][['uid','item_id']].drop_duplicates().shape[0])\n# eligibility among random unique train pairs\nq=tr[['uid','item_id']].drop_duplicates(); q=q.sample(min(200000,len(q)),random_state=1); print('prior pair eligible',np.mean([mask[ui[int(a)],ii[int(b)]] for a,b in q.itertuples(index=False)]))\nprint(tr[tr.future_rel].groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "val strong 170791 likes 8528 truth 175729 eligible 37769\ntruth pop percentiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\ntrain catalog events 31062540 pairs 7868664 future events prior 0 future pairs prior 0\nprior pair eligible 0.0\n n last org ratio\ncount 0.0 0.0 0.0 0.0\nmean NaN NaN NaN NaN\nstd NaN NaN NaN NaN\nmin NaN NaN NaN NaN\n25% NaN NaN NaN NaN\n50% NaN NaN NaN NaN\n75% NaN NaN NaN NaN\nmax NaN NaN NaN NaN\n", "stderr": "", "aggregated_output": "val strong 170791 likes 8528 truth 175729 eligible 37769\ntruth pop percentiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\ntrain catalog events 31062540 pairs 7868664 future events prior 0 future pairs prior 0\nprior pair eligible 0.0\n n last org ratio\ncount 0.0 0.0 0.0 0.0\nmean NaN NaN NaN NaN\nstd NaN NaN NaN NaN\nmin NaN NaN NaN NaN\n25% NaN NaN NaN NaN\n50% NaN NaN NaN NaN\n75% NaN NaN NaN NaN\nmax NaN NaN NaN NaN\n", "exit_code": 0, "duration": { "secs": 31, "nanos": 936867299 }, "formatted_output": "val strong 170791 likes 8528 truth 175729 eligible 37769\ntruth pop percentiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\ntrain catalog events 31062540 pairs 7868664 future events prior 0 future pairs prior 0\nprior pair eligible 0.0\n n last org ratio\ncount 0.0 0.0 0.0 0.0\nmean NaN NaN NaN NaN\nstd NaN NaN NaN NaN\nmin NaN NaN NaN NaN\n25% NaN NaN NaN NaN\n50% NaN NaN NaN NaN\n75% NaN NaN NaN NaN\nmax NaN NaN NaN NaN\n" } } ### exec · 2026-09-25T00:56:07.285Z { "code": "const r = await tools.exec_command({cmd:\"ps aux | grep '[p]ython' | head\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:56:07.342Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: ps: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7c88f61e-dd5a-4559-80ca-bd62987189e9", "process_id": "14219", "command": [ "/bin/sh", "-lc", "ps aux | grep '[p]ython' | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps aux | grep '[p]ython' | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3540 }, "formatted_output": "/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6a537c487d2aa5253cdd15d0f52", "usage": { "input_tokens": 23944, "cached_input_tokens": 22784, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 22, "total_tokens": 24024 }, "turn_token_usage": { "input_tokens": 220590, "cached_input_tokens": 198528, "cache_write_input_tokens": 0, "output_tokens": 3248, "reasoning_output_tokens": 799, "total_tokens": 223838 }, "thread_token_usage": { "input_tokens": 220590, "cached_input_tokens": 198528, "cache_write_input_tokens": 0, "output_tokens": 3248, "reasoning_output_tokens": 799, "total_tokens": 223838 } } ## Step 18 · agent · 2026-09-25T00:56:10.076Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:56:21.649Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np,time\\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\\nui=pd.Series(np.arange(len(u)),index=u); ii=pd.Series(np.arange(len(it)),index=it)\\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); rr=ui.loc[truth.uid].values; cc=ii.loc[truth.item_id].values; truth['eligible']=mask[rr,cc]\\nprint('val',len(L),len(K),len(truth),truth.eligible.sum(),flush=True)\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print(truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]),flush=True)\\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(tr),flush=True); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]; print('filtered',len(tr),flush=True)\\nrel=truth[truth.eligible][['uid','item_id']].assign(rel=1); m=tr.merge(rel,on=['uid','item_id'],how='inner'); print('future prior',len(m),m[['uid','item_id']].drop_duplicates().shape[0],flush=True)\\nq=tr[['uid','item_id']].drop_duplicates().sample(200000,random_state=1); print('prior eligible',mask[ui.loc[q.uid].values,ii.loc[q.item_id].values].mean(),flush=True)\\nprint(m.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe(),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:56:22.831Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"6da8e7","wall_time_seconds":1.001079448,"session_id":15820,"original_token_count":52,"output":"val 170791 8528 175729 37769\r\n0.00 1.0\r\n0.10 428.0\r\n0.25 1733.0\r\n0.50 5622.0\r\n0.75 13374.0\r\n0.90 21680.2\r\n0.99 29101.0\r\n1.00 30000.0\r\nName: popularity_rank, dtype: float64\r\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6a7a66c87d2b34d28e6154d60b1", "usage": { "input_tokens": 24058, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 736, "reasoning_output_tokens": 84, "total_tokens": 24794 }, "turn_token_usage": { "input_tokens": 244648, "cached_input_tokens": 222336, "cache_write_input_tokens": 0, "output_tokens": 3984, "reasoning_output_tokens": 883, "total_tokens": 248632 }, "thread_token_usage": { "input_tokens": 244648, "cached_input_tokens": 222336, "cache_write_input_tokens": 0, "output_tokens": 3984, "reasoning_output_tokens": 883, "total_tokens": 248632 } } ## Step 19 · agent · 2026-09-25T00:56:23.896Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:56:24.615Z { "code": "const r = await tools.write_stdin({session_id:15820,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:56:29.211Z Script completed Wall time 4.6 seconds Output: read 43484159 filtered 31062540 future prior 0 0 prior eligible 0.0 n last org ratio count 0.0 0.0 0.0 0.0 mean NaN NaN NaN NaN std NaN NaN NaN NaN min NaN NaN NaN NaN 25% NaN NaN NaN NaN 50% NaN NaN NaN NaN 75% NaN NaN NaN NaN max NaN NaN NaN NaN ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6b70a0c87d280a05bae8b6a6e74", "usage": { "input_tokens": 24964, "cached_input_tokens": 23936, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 25020 }, "turn_token_usage": { "input_tokens": 269612, "cached_input_tokens": 246272, "cache_write_input_tokens": 0, "output_tokens": 4040, "reasoning_output_tokens": 892, "total_tokens": 273652 }, "thread_token_usage": { "input_tokens": 269612, "cached_input_tokens": 246272, "cache_write_input_tokens": 0, "output_tokens": 4040, "reasoning_output_tokens": 892, "total_tokens": 273652 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-490df38b-b621-4a87-9879-4621023fe5c3", "process_id": "15820", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nui=pd.Series(np.arange(len(u)),index=u); ii=pd.Series(np.arange(len(it)),index=it)\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); rr=ui.loc[truth.uid].values; cc=ii.loc[truth.item_id].values; truth['eligible']=mask[rr,cc]\nprint('val',len(L),len(K),len(truth),truth.eligible.sum(),flush=True)\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print(truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]),flush=True)\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(tr),flush=True); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]; print('filtered',len(tr),flush=True)\nrel=truth[truth.eligible][['uid','item_id']].assign(rel=1); m=tr.merge(rel,on=['uid','item_id'],how='inner'); print('future prior',len(m),m[['uid','item_id']].drop_duplicates().shape[0],flush=True)\nq=tr[['uid','item_id']].drop_duplicates().sample(200000,random_state=1); print('prior eligible',mask[ui.loc[q.uid].values,ii.loc[q.item_id].values].mean(),flush=True)\nprint(m.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe(),flush=True)\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'; z=np.load(D+'/validation_eligible_candidates.npz'); u=z['uids'].astype('int64'); it=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nui=pd.Series(np.arange(len(u)),index=u); ii=pd.Series(np.arange(len(it)),index=it)\nL=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); L=L[(L.is_organic==1)&(L.played_ratio_pct>=80)&L.uid.isin(u)&L.item_id.isin(it)][['uid','item_id']].drop_duplicates()\nK=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic']); K=K[(K.is_organic==1)&K.uid.isin(u)&K.item_id.isin(it)][['uid','item_id']].drop_duplicates(); truth=pd.concat([L,K]).drop_duplicates(); rr=ui.loc[truth.uid].values; cc=ii.loc[truth.item_id].values; truth['eligible']=mask[rr,cc]\nprint('val',len(L),len(K),len(truth),truth.eligible.sum(),flush=True)\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); truth=truth.merge(cat[['item_id','popularity_rank']],on='item_id'); print(truth[truth.eligible].popularity_rank.quantile([0,.1,.25,.5,.75,.9,.99,1]),flush=True)\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(tr),flush=True); tr=tr[tr.uid.isin(u)&tr.item_id.isin(it)]; print('filtered',len(tr),flush=True)\nrel=truth[truth.eligible][['uid','item_id']].assign(rel=1); m=tr.merge(rel,on=['uid','item_id'],how='inner'); print('future prior',len(m),m[['uid','item_id']].drop_duplicates().shape[0],flush=True)\nq=tr[['uid','item_id']].drop_duplicates().sample(200000,random_state=1); print('prior eligible',mask[ui.loc[q.uid].values,ii.loc[q.item_id].values].mean(),flush=True)\nprint(m.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','mean'),ratio=('played_ratio_pct','mean')).describe(),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "val 170791 8528 175729 37769\r\n0.00 1.0\r\n0.10 428.0\r\n0.25 1733.0\r\n0.50 5622.0\r\n0.75 13374.0\r\n0.90 21680.2\r\n0.99 29101.0\r\n1.00 30000.0\r\nName: popularity_rank, dtype: float64\r\nread 43484159\r\nfiltered 31062540\r\nfuture prior 0 0\r\nprior eligible 0.0\r\n n last org ratio\r\ncount 0.0 0.0 0.0 0.0\r\nmean NaN NaN NaN NaN\r\nstd NaN NaN NaN NaN\r\nmin NaN NaN NaN NaN\r\n25% NaN NaN NaN NaN\r\n50% NaN NaN NaN NaN\r\n75% NaN NaN NaN NaN\r\nmax NaN NaN NaN NaN\r\n", "stderr": "", "aggregated_output": "val 170791 8528 175729 37769\r\n0.00 1.0\r\n0.10 428.0\r\n0.25 1733.0\r\n0.50 5622.0\r\n0.75 13374.0\r\n0.90 21680.2\r\n0.99 29101.0\r\n1.00 30000.0\r\nName: popularity_rank, dtype: float64\r\nread 43484159\r\nfiltered 31062540\r\nfuture prior 0 0\r\nprior eligible 0.0\r\n n last org ratio\r\ncount 0.0 0.0 0.0 0.0\r\nmean NaN NaN NaN NaN\r\nstd NaN NaN NaN NaN\r\nmin NaN NaN NaN NaN\r\n25% NaN NaN NaN NaN\r\n50% NaN NaN NaN NaN\r\n75% NaN NaN NaN NaN\r\nmax NaN NaN NaN NaN\r\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 379999771 }, "formatted_output": "val 170791 8528 175729 37769\r\n0.00 1.0\r\n0.10 428.0\r\n0.25 1733.0\r\n0.50 5622.0\r\n0.75 13374.0\r\n0.90 21680.2\r\n0.99 29101.0\r\n1.00 30000.0\r\nName: popularity_rank, dtype: float64\r\nread 43484159\r\nfiltered 31062540\r\nfuture prior 0 0\r\nprior eligible 0.0\r\n n last org ratio\r\ncount 0.0 0.0 0.0 0.0\r\nmean NaN NaN NaN NaN\r\nstd NaN NaN NaN NaN\r\nmin NaN NaN NaN NaN\r\n25% NaN NaN NaN NaN\r\n50% NaN NaN NaN NaN\r\n75% NaN NaN NaN NaN\r\nmax NaN NaN NaN NaN\r\n" } } ## Step 20 · agent · 2026-09-25T00:56:40.672Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-09-25T00:56:52.412Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-09-25T00:56:57.891Z ### Thinking unavailable — no text recorded Eligibility is fully novelty-based: every catalog track previously played by a user is excluded, and none of the validation positives are repeats. That rules out memorization and confirms the core model should learn taste from cross-user co-consumption. I’m building an implicit-feedback latent model with organic/completion weighting and popularity normalization, then I’ll tune only the score blend on the public window. ### exec · 2026-09-25T00:57:26.141Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n import numpy as np\\n import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n@@\\n def popularity(stage, path):\\n@@\\n pd.concat(rows, ignore_index=True).to_parquet(path, index=False)\\n+\\n+\\n+def interaction_matrix(history, uids, items):\\n+ \\\"\\\"\\\"Weighted implicit matrix. Rows are target users, columns catalog tracks.\\\"\\\"\\\"\\n+ uid_index = pd.Series(np.arange(len(uids), dtype=np.int32), index=uids)\\n+ item_index = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\\n+ mats = []\\n+ for split in history:\\n+ d = pd.read_parquet(DATA / f'{split}_listens.parquet',\\n+ columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+ d = d[(d.played_ratio_pct >= 50) & d.uid.isin(uids) & d.item_id.isin(items)]\\n+ row = uid_index.loc[d.uid.to_numpy()].to_numpy()\\n+ col = item_index.loc[d.item_id.to_numpy()].to_numpy()\\n+ # Organic and deeply completed plays are more diagnostic of taste.\\n+ val = (1.0 + .75*d.is_organic.to_numpy() +\\n+ .5*(d.played_ratio_pct.to_numpy() >= 80)).astype(np.float32)\\n+ mats.append(sparse.coo_matrix((val,(row,col)), shape=(len(uids),len(items))).tocsr())\\n+ del d, row, col, val\\n+ X = sum(mats[1:], mats[0]) if len(mats) > 1 else mats[0]\\n+ X.sum_duplicates()\\n+ X.data = np.log1p(X.data).astype(np.float32)\\n+ # Exact active preference state is cutoff-correct and gets extra confidence.\\n+ stage = 'validation' if history == ['train'] else 'test'\\n+ p = pd.read_parquet(DATA / f'{stage}_preference_state.parquet')\\n+ p = p[p.uid.isin(uids) & p.item_id.isin(items)]\\n+ for state, weight in [('liked', 2.5), ('disliked', -1.25)]:\\n+ q = p[p.state == state]\\n+ if len(q):\\n+ row = uid_index.loc[q.uid.to_numpy()].to_numpy()\\n+ col = item_index.loc[q.item_id.to_numpy()].to_numpy()\\n+ X = X + sparse.coo_matrix((np.full(len(q),weight,np.float32),(row,col)),shape=X.shape).tocsr()\\n+ return X\\n+\\n+\\n+def write_top(uids, items, eligible, score_parts, pop, blends, prefix):\\n+ \\\"\\\"\\\"Materialize rankings for several (latent, popularity) blends in batches.\\\"\\\"\\\"\\n+ outputs = {name: [] for name in blends}\\n+ A, B = score_parts\\n+ for lo in range(0, len(uids), 256):\\n+ hi = min(lo+256, len(uids))\\n+ s = (A[lo:hi] @ B).astype(np.float32)\\n+ # Per-user standardization makes blend coefficients stable.\\n+ s -= s.mean(axis=1, keepdims=True)\\n+ s /= s.std(axis=1, keepdims=True) + 1e-6\\n+ for name, alpha in blends.items():\\n+ z = s + alpha * pop[None,:]\\n+ z[~eligible[lo:hi]] = -np.inf\\n+ ix = np.argpartition(z, -100, axis=1)[:,-100:]\\n+ order = np.take_along_axis(z, ix, axis=1).argsort(axis=1)[:,::-1]\\n+ ix = np.take_along_axis(ix, order, axis=1)\\n+ outputs[name].append(ix)\\n+ for name, chunks in outputs.items():\\n+ top = np.vstack(chunks)\\n+ frame = pd.DataFrame({'uid':np.repeat(uids,100),\\n+ 'item_id':items[top.ravel()],\\n+ 'rank':np.tile(np.arange(1,101),len(uids))})\\n+ frame.to_parquet(OUT / f'{prefix}{name}.parquet', index=False)\\n+\\n+\\n+def latent(stage, history, components=96, final=False):\\n+ uids, items, eligible = load_candidates(stage)\\n+ X = interaction_matrix(history, uids, items)\\n+ # BM25-style row length and item-frequency normalization.\\n+ row_nnz = np.diff(X.indptr).astype(np.float32)\\n+ row_scale = 1.0 / np.sqrt(np.maximum(row_nnz, 1))\\n+ df = np.asarray((X != 0).sum(axis=0)).ravel()\\n+ idf = np.log1p(len(uids) / (20.0 + df)).astype(np.float32)\\n+ Xw = sparse.diags(row_scale) @ X @ sparse.diags(idf)\\n+ svd = TruncatedSVD(n_components=components, n_iter=7, random_state=2026)\\n+ A = svd.fit_transform(Xw).astype(np.float32)\\n+ B = svd.components_.astype(np.float32)\\n+ # Standardized log popularity; latent factors already retain some popularity.\\n+ cat = pd.read_parquet(DATA/'candidate_catalog.parquet').set_index('item_id')\\n+ pop = np.log1p(cat.loc[items,'organic_positive_listens'].to_numpy()).astype(np.float32)\\n+ pop = (pop-pop.mean())/pop.std()\\n+ print('matrix', X.shape, X.nnz, 'explained', svd.explained_variance_ratio_.sum(), flush=True)\\n+ if final:\\n+ write_top(uids,items,eligible,(A,B),pop,{'':0.18},'submission')\\n+ else:\\n+ blends={'m20':-.20,'m10':-.10,'p00':0.,'p10':.10,'p18':.18,'p30':.30,'p50':.50}\\n+ write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\\n@@\\n if __name__ == '__main__':\\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); a=ap.parse_args()\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\\n@@\\n popularity('test', OUT/'submission.parquet')\\n+ if a.latent:\\n+ if a.final: latent('test',['train','val'],a.components,True)\\n+ else: latent('validation',['train'],a.components,False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:57:26.225Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cde890d1-2c4a-4b06-b6a7-f5da6b61ba24", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -6,2 +6,4 @@\n import pandas as pd\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n \n@@ -31,4 +33,85 @@\n \n+def interaction_matrix(history, uids, items):\n+ \"\"\"Weighted implicit matrix. Rows are target users, columns catalog tracks.\"\"\"\n+ uid_index = pd.Series(np.arange(len(uids), dtype=np.int32), index=uids)\n+ item_index = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\n+ mats = []\n+ for split in history:\n+ d = pd.read_parquet(DATA / f'{split}_listens.parquet',\n+ columns=['uid','item_id','is_organic','played_ratio_pct'])\n+ d = d[(d.played_ratio_pct >= 50) & d.uid.isin(uids) & d.item_id.isin(items)]\n+ row = uid_index.loc[d.uid.to_numpy()].to_numpy()\n+ col = item_index.loc[d.item_id.to_numpy()].to_numpy()\n+ # Organic and deeply completed plays are more diagnostic of taste.\n+ val = (1.0 + .75*d.is_organic.to_numpy() +\n+ .5*(d.played_ratio_pct.to_numpy() >= 80)).astype(np.float32)\n+ mats.append(sparse.coo_matrix((val,(row,col)), shape=(len(uids),len(items))).tocsr())\n+ del d, row, col, val\n+ X = sum(mats[1:], mats[0]) if len(mats) > 1 else mats[0]\n+ X.sum_duplicates()\n+ X.data = np.log1p(X.data).astype(np.float32)\n+ # Exact active preference state is cutoff-correct and gets extra confidence.\n+ stage = 'validation' if history == ['train'] else 'test'\n+ p = pd.read_parquet(DATA / f'{stage}_preference_state.parquet')\n+ p = p[p.uid.isin(uids) & p.item_id.isin(items)]\n+ for state, weight in [('liked', 2.5), ('disliked', -1.25)]:\n+ q = p[p.state == state]\n+ if len(q):\n+ row = uid_index.loc[q.uid.to_numpy()].to_numpy()\n+ col = item_index.loc[q.item_id.to_numpy()].to_numpy()\n+ X = X + sparse.coo_matrix((np.full(len(q),weight,np.float32),(row,col)),shape=X.shape).tocsr()\n+ return X\n+\n+\n+def write_top(uids, items, eligible, score_parts, pop, blends, prefix):\n+ \"\"\"Materialize rankings for several (latent, popularity) blends in batches.\"\"\"\n+ outputs = {name: [] for name in blends}\n+ A, B = score_parts\n+ for lo in range(0, len(uids), 256):\n+ hi = min(lo+256, len(uids))\n+ s = (A[lo:hi] @ B).astype(np.float32)\n+ # Per-user standardization makes blend coefficients stable.\n+ s -= s.mean(axis=1, keepdims=True)\n+ s /= s.std(axis=1, keepdims=True) + 1e-6\n+ for name, alpha in blends.items():\n+ z = s + alpha * pop[None,:]\n+ z[~eligible[lo:hi]] = -np.inf\n+ ix = np.argpartition(z, -100, axis=1)[:,-100:]\n+ order = np.take_along_axis(z, ix, axis=1).argsort(axis=1)[:,::-1]\n+ ix = np.take_along_axis(ix, order, axis=1)\n+ outputs[name].append(ix)\n+ for name, chunks in outputs.items():\n+ top = np.vstack(chunks)\n+ frame = pd.DataFrame({'uid':np.repeat(uids,100),\n+ 'item_id':items[top.ravel()],\n+ 'rank':np.tile(np.arange(1,101),len(uids))})\n+ frame.to_parquet(OUT / f'{prefix}{name}.parquet', index=False)\n+\n+\n+def latent(stage, history, components=96, final=False):\n+ uids, items, eligible = load_candidates(stage)\n+ X = interaction_matrix(history, uids, items)\n+ # BM25-style row length and item-frequency normalization.\n+ row_nnz = np.diff(X.indptr).astype(np.float32)\n+ row_scale = 1.0 / np.sqrt(np.maximum(row_nnz, 1))\n+ df = np.asarray((X != 0).sum(axis=0)).ravel()\n+ idf = np.log1p(len(uids) / (20.0 + df)).astype(np.float32)\n+ Xw = sparse.diags(row_scale) @ X @ sparse.diags(idf)\n+ svd = TruncatedSVD(n_components=components, n_iter=7, random_state=2026)\n+ A = svd.fit_transform(Xw).astype(np.float32)\n+ B = svd.components_.astype(np.float32)\n+ # Standardized log popularity; latent factors already retain some popularity.\n+ cat = pd.read_parquet(DATA/'candidate_catalog.parquet').set_index('item_id')\n+ pop = np.log1p(cat.loc[items,'organic_positive_listens'].to_numpy()).astype(np.float32)\n+ pop = (pop-pop.mean())/pop.std()\n+ print('matrix', X.shape, X.nnz, 'explained', svd.explained_variance_ratio_.sum(), flush=True)\n+ if final:\n+ write_top(uids,items,eligible,(A,B),pop,{'':0.18},'submission')\n+ else:\n+ blends={'m20':-.20,'m10':-.10,'p00':0.,'p10':.10,'p18':.18,'p30':.30,'p50':.50}\n+ write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\n+\n+\n if __name__ == '__main__':\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); a=ap.parse_args()\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\n if a.baseline:\n@@ -36 +119,4 @@\n popularity('test', OUT/'submission.parquet')\n+ if a.latent:\n+ if a.final: latent('test',['train','val'],a.components,True)\n+ else: latent('validation',['train'],a.components,False)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6bd6c6487d2b4d7a76ce3dfb7a1", "usage": { "input_tokens": 25201, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 3043, "reasoning_output_tokens": 1299, "total_tokens": 28244 }, "turn_token_usage": { "input_tokens": 294813, "cached_input_tokens": 271104, "cache_write_input_tokens": 0, "output_tokens": 7083, "reasoning_output_tokens": 2191, "total_tokens": 301896 }, "thread_token_usage": { "input_tokens": 294813, "cached_input_tokens": 271104, "cache_write_input_tokens": 0, "output_tokens": 7083, "reasoning_output_tokens": 2191, "total_tokens": 301896 } } ## Step 23 · agent · 2026-09-25T00:57:31.012Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:31.981Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:57:33.147Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c03b85","wall_time_seconds":1.001685435,"session_id":95026,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6f6852087d2af2093b1c04d1470", "usage": { "input_tokens": 28268, "cached_input_tokens": 24960, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 10, "total_tokens": 28351 }, "turn_token_usage": { "input_tokens": 323081, "cached_input_tokens": 296064, "cache_write_input_tokens": 0, "output_tokens": 7166, "reasoning_output_tokens": 2201, "total_tokens": 330247 }, "thread_token_usage": { "input_tokens": 323081, "cached_input_tokens": 296064, "cache_write_input_tokens": 0, "output_tokens": 7166, "reasoning_output_tokens": 2201, "total_tokens": 330247 } } ## Step 24 · agent · 2026-09-25T00:57:37.072Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:37.757Z { "code": "const r = await tools.write_stdin({session_id:95026,chars:\"\",yield_time_ms:30000,max_output_tokens:2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:57:50.102Z Script completed Wall time 12.3 seconds Output: matrix (8456, 30000) 5584017 explained 0.18533733 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c6fd556087d28b30787a73d32a94", "usage": { "input_tokens": 28409, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 28462 }, "turn_token_usage": { "input_tokens": 351490, "cached_input_tokens": 324096, "cache_write_input_tokens": 0, "output_tokens": 7219, "reasoning_output_tokens": 2207, "total_tokens": 358709 }, "thread_token_usage": { "input_tokens": 351490, "cached_input_tokens": 324096, "cache_write_input_tokens": 0, "output_tokens": 7219, "reasoning_output_tokens": 2207, "total_tokens": 358709 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d9182f5c-a2df-4fdd-9ba4-5d7f8f77ea66", "process_id": "95026", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 954710371 }, "formatted_output": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n" } } ## Step 25 · agent · 2026-09-25T00:57:52.978Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:55.255Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_m20.parquet solution/validation_m10.parquet solution/validation_p00.parquet solution/validation_p10.parquet solution/validation_p18.parquet solution/validation_p30.parquet solution/validation_p50.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tail -4 | tr '\\\\n' ' '; echo \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:58:13.311Z Script completed Wall time 18.1 seconds Output: "quality_ndcg@10": 0.020901, "quality_users": 4263 } } solution/validation_m20.parquet "quality_ndcg@10": 0.021019, "quality_users": 4263 } } solution/validation_m10.parquet "quality_ndcg@10": 0.021109, "quality_users": 4263 } } solution/validation_p00.parquet "quality_ndcg@10": 0.021292, "quality_users": 4263 } } solution/validation_p10.parquet "quality_ndcg@10": 0.021458, "quality_users": 4263 } } solution/validation_p18.parquet "quality_ndcg@10": 0.021703, "quality_users": 4263 } } solution/validation_p30.parquet "quality_ndcg@10": 0.022094, "quality_users": 4263 } } solution/validation_p50.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c70e4cf487d2955bf0edd7af8900", "usage": { "input_tokens": 28506, "cached_input_tokens": 28288, "cache_write_input_tokens": 0, "output_tokens": 146, "reasoning_output_tokens": 11, "total_tokens": 28652 }, "turn_token_usage": { "input_tokens": 379996, "cached_input_tokens": 352384, "cache_write_input_tokens": 0, "output_tokens": 7365, "reasoning_output_tokens": 2218, "total_tokens": 387361 }, "thread_token_usage": { "input_tokens": 379996, "cached_input_tokens": 352384, "cache_write_input_tokens": 0, "output_tokens": 7365, "reasoning_output_tokens": 2218, "total_tokens": 387361 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7733062c-33b6-4c20-826e-93ce37f30acc", "process_id": "73036", "command": [ "/bin/sh", "-lc", "for f in solution/validation_m20.parquet solution/validation_m10.parquet solution/validation_p00.parquet solution/validation_p10.parquet solution/validation_p18.parquet solution/validation_p30.parquet solution/validation_p50.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -4 | tr '\\n' ' '; echo \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_m20.parquet solution/validation_m10.parquet solution/validation_p00.parquet solution/validation_p10.parquet solution/validation_p18.parquet solution/validation_p30.parquet solution/validation_p50.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -4 | tr '\\n' ' '; echo \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.020901, \"quality_users\": 4263 } } solution/validation_m20.parquet\n \"quality_ndcg@10\": 0.021019, \"quality_users\": 4263 } } solution/validation_m10.parquet\n \"quality_ndcg@10\": 0.021109, \"quality_users\": 4263 } } solution/validation_p00.parquet\n \"quality_ndcg@10\": 0.021292, \"quality_users\": 4263 } } solution/validation_p10.parquet\n \"quality_ndcg@10\": 0.021458, \"quality_users\": 4263 } } solution/validation_p18.parquet\n \"quality_ndcg@10\": 0.021703, \"quality_users\": 4263 } } solution/validation_p30.parquet\n \"quality_ndcg@10\": 0.022094, \"quality_users\": 4263 } } solution/validation_p50.parquet\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.020901, \"quality_users\": 4263 } } solution/validation_m20.parquet\n \"quality_ndcg@10\": 0.021019, \"quality_users\": 4263 } } solution/validation_m10.parquet\n \"quality_ndcg@10\": 0.021109, \"quality_users\": 4263 } } solution/validation_p00.parquet\n \"quality_ndcg@10\": 0.021292, \"quality_users\": 4263 } } solution/validation_p10.parquet\n \"quality_ndcg@10\": 0.021458, \"quality_users\": 4263 } } solution/validation_p18.parquet\n \"quality_ndcg@10\": 0.021703, \"quality_users\": 4263 } } solution/validation_p30.parquet\n \"quality_ndcg@10\": 0.022094, \"quality_users\": 4263 } } solution/validation_p50.parquet\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 877576976 }, "formatted_output": " \"quality_ndcg@10\": 0.020901, \"quality_users\": 4263 } } solution/validation_m20.parquet\n \"quality_ndcg@10\": 0.021019, \"quality_users\": 4263 } } solution/validation_m10.parquet\n \"quality_ndcg@10\": 0.021109, \"quality_users\": 4263 } } solution/validation_p00.parquet\n \"quality_ndcg@10\": 0.021292, \"quality_users\": 4263 } } solution/validation_p10.parquet\n \"quality_ndcg@10\": 0.021458, \"quality_users\": 4263 } } solution/validation_p18.parquet\n \"quality_ndcg@10\": 0.021703, \"quality_users\": 4263 } } solution/validation_p30.parquet\n \"quality_ndcg@10\": 0.022094, \"quality_users\": 4263 } } solution/validation_p50.parquet\n" } } ## Step 26 · agent · 2026-09-25T00:58:15.672Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:18.113Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- blends={'m20':-.20,'m10':-.10,'p00':0.,'p10':.10,'p18':.18,'p30':.30,'p50':.50}\\n+ blends={'p40':.40,'p50':.50,'p65':.65,'p80':.80,'p100':1.0,'p130':1.3,'p170':1.7}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:58:18.192Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-44e38bab-9fe6-4b74-8229-116073d881ac", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -110,3 +110,3 @@\n else:\n- blends={'m20':-.20,'m10':-.10,'p00':0.,'p10':.10,'p18':.18,'p30':.30,'p50':.50}\n+ blends={'p40':.40,'p50':.50,'p65':.65,'p80':.80,'p100':1.0,'p130':1.3,'p170':1.7}\n write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7258d5c87d28ed78172f4622429", "usage": { "input_tokens": 28906, "cached_input_tokens": 28288, "cache_write_input_tokens": 0, "output_tokens": 202, "reasoning_output_tokens": 58, "total_tokens": 29108 }, "turn_token_usage": { "input_tokens": 408902, "cached_input_tokens": 380672, "cache_write_input_tokens": 0, "output_tokens": 7567, "reasoning_output_tokens": 2276, "total_tokens": 416469 }, "thread_token_usage": { "input_tokens": 408902, "cached_input_tokens": 380672, "cache_write_input_tokens": 0, "output_tokens": 7567, "reasoning_output_tokens": 2276, "total_tokens": 416469 } } ### exec · 2026-09-25T00:58:22.060Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:58:23.226Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9771e9","wall_time_seconds":1.001470568,"session_id":45978,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c72a718c87d2a1ad1eee75153c72", "usage": { "input_tokens": 29132, "cached_input_tokens": 28672, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 0, "total_tokens": 29203 }, "turn_token_usage": { "input_tokens": 438034, "cached_input_tokens": 409344, "cache_write_input_tokens": 0, "output_tokens": 7638, "reasoning_output_tokens": 2276, "total_tokens": 445672 }, "thread_token_usage": { "input_tokens": 438034, "cached_input_tokens": 409344, "cache_write_input_tokens": 0, "output_tokens": 7638, "reasoning_output_tokens": 2276, "total_tokens": 445672 } } ### exec · 2026-09-25T00:58:25.907Z { "code": "const r = await tools.write_stdin({session_id:45978,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:58:40.703Z Script completed Wall time 14.8 seconds Output: matrix (8456, 30000) 5584017 explained 0.18533733 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c72f66e887d281ef56fa2dfc02c1", "usage": { "input_tokens": 29261, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 29306 }, "turn_token_usage": { "input_tokens": 467295, "cached_input_tokens": 427904, "cache_write_input_tokens": 0, "output_tokens": 7683, "reasoning_output_tokens": 2276, "total_tokens": 474978 }, "thread_token_usage": { "input_tokens": 467295, "cached_input_tokens": 427904, "cache_write_input_tokens": 0, "output_tokens": 7683, "reasoning_output_tokens": 2276, "total_tokens": 474978 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8b37c7d8-d03d-44e4-8aa1-5152843189ab", "process_id": "45978", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 96" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 476636928 }, "formatted_output": "matrix (8456, 30000) 5584017 explained 0.18533733\r\n" } } ### exec · 2026-09-25T00:58:46.274Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_p40.parquet solution/validation_p50.parquet solution/validation_p65.parquet solution/validation_p80.parquet solution/validation_p100.parquet solution/validation_p130.parquet solution/validation_p170.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\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:59:04.405Z Script completed Wall time 18.1 seconds Output: validation_p40.parquet "quality_ndcg@10": 0.021881, validation_p50.parquet "quality_ndcg@10": 0.022094, validation_p65.parquet "quality_ndcg@10": 0.022375, validation_p80.parquet "quality_ndcg@10": 0.022508, validation_p100.parquet "quality_ndcg@10": 0.022753, validation_p130.parquet "quality_ndcg@10": 0.022906, validation_p170.parquet "quality_ndcg@10": 0.022994, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c740f96087d2aa53010a953714ca", "usage": { "input_tokens": 29350, "cached_input_tokens": 28928, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 0, "total_tokens": 29485 }, "turn_token_usage": { "input_tokens": 496645, "cached_input_tokens": 456832, "cache_write_input_tokens": 0, "output_tokens": 7818, "reasoning_output_tokens": 2276, "total_tokens": 504463 }, "thread_token_usage": { "input_tokens": 496645, "cached_input_tokens": 456832, "cache_write_input_tokens": 0, "output_tokens": 7818, "reasoning_output_tokens": 2276, "total_tokens": 504463 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d194fe75-c472-4002-89c8-ce6b7cc3dc68", "process_id": "32908", "command": [ "/bin/sh", "-lc", "for f in solution/validation_p40.parquet solution/validation_p50.parquet solution/validation_p65.parquet solution/validation_p80.parquet solution/validation_p100.parquet solution/validation_p130.parquet solution/validation_p170.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_p40.parquet solution/validation_p50.parquet solution/validation_p65.parquet solution/validation_p80.parquet solution/validation_p100.parquet solution/validation_p130.parquet solution/validation_p170.parquet; do printf '%s ' \"$(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\": 0.021881,\nvalidation_p50.parquet \"quality_ndcg@10\": 0.022094,\nvalidation_p65.parquet \"quality_ndcg@10\": 0.022375,\nvalidation_p80.parquet \"quality_ndcg@10\": 0.022508,\nvalidation_p100.parquet \"quality_ndcg@10\": 0.022753,\nvalidation_p130.parquet \"quality_ndcg@10\": 0.022906,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.022994,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.021881,\nvalidation_p50.parquet \"quality_ndcg@10\": 0.022094,\nvalidation_p65.parquet \"quality_ndcg@10\": 0.022375,\nvalidation_p80.parquet \"quality_ndcg@10\": 0.022508,\nvalidation_p100.parquet \"quality_ndcg@10\": 0.022753,\nvalidation_p130.parquet \"quality_ndcg@10\": 0.022906,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.022994,\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 961926327 }, "formatted_output": " \"quality_ndcg@10\": 0.021881,\nvalidation_p50.parquet \"quality_ndcg@10\": 0.022094,\nvalidation_p65.parquet \"quality_ndcg@10\": 0.022375,\nvalidation_p80.parquet \"quality_ndcg@10\": 0.022508,\nvalidation_p100.parquet \"quality_ndcg@10\": 0.022753,\nvalidation_p130.parquet \"quality_ndcg@10\": 0.022906,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.022994,\n" } } ## Step 27 · agent · 2026-09-25T00:59:10.035Z ### Thinking unavailable — no text recorded The latent model has doubled the popularity baseline, and a moderate popularity prior still helps because future organic activity is highly head-weighted. The blend is still improving at the edge of the first sweep, so I’m widening that search and increasing latent capacity before locking the final refit. ### exec · 2026-09-25T00:59:13.163Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- blends={'p40':.40,'p50':.50,'p65':.65,'p80':.80,'p100':1.0,'p130':1.3,'p170':1.7}\\n+ blends={'p130':1.3,'p170':1.7,'p210':2.1,'p260':2.6,'p320':3.2,'p400':4.0,'p550':5.5}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:59:13.199Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-60de0d9c-f94f-40e2-820f-612ab0de899b", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -110,3 +110,3 @@\n else:\n- blends={'p40':.40,'p50':.50,'p65':.65,'p80':.80,'p100':1.0,'p130':1.3,'p170':1.7}\n+ blends={'p130':1.3,'p170':1.7,'p210':2.1,'p260':2.6,'p320':3.2,'p400':4.0,'p550':5.5}\n write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c758927c87d2a7cdf88f780d9521", "usage": { "input_tokens": 29641, "cached_input_tokens": 29056, "cache_write_input_tokens": 0, "output_tokens": 411, "reasoning_output_tokens": 197, "total_tokens": 30052 }, "turn_token_usage": { "input_tokens": 526286, "cached_input_tokens": 485888, "cache_write_input_tokens": 0, "output_tokens": 8229, "reasoning_output_tokens": 2473, "total_tokens": 534515 }, "thread_token_usage": { "input_tokens": 526286, "cached_input_tokens": 485888, "cache_write_input_tokens": 0, "output_tokens": 8229, "reasoning_output_tokens": 2473, "total_tokens": 534515 } } ### exec · 2026-09-25T00:59:16.177Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:59:17.343Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"76ebc7","wall_time_seconds":1.001385161,"session_id":71330,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c761786c87d29bd2fcff4277d9a4", "usage": { "input_tokens": 30076, "cached_input_tokens": 29440, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 0, "total_tokens": 30147 }, "turn_token_usage": { "input_tokens": 556362, "cached_input_tokens": 515328, "cache_write_input_tokens": 0, "output_tokens": 8300, "reasoning_output_tokens": 2473, "total_tokens": 564662 }, "thread_token_usage": { "input_tokens": 556362, "cached_input_tokens": 515328, "cache_write_input_tokens": 0, "output_tokens": 8300, "reasoning_output_tokens": 2473, "total_tokens": 564662 } } ### exec · 2026-09-25T00:59:20.469Z { "code": "const r = await tools.write_stdin({session_id:71330,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:59:36.789Z Script completed Wall time 16.3 seconds Output: matrix (8456, 30000) 5584017 explained 0.24970774 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c765831087d2850a4977f6798e9f", "usage": { "input_tokens": 30205, "cached_input_tokens": 29952, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 30250 }, "turn_token_usage": { "input_tokens": 586567, "cached_input_tokens": 545280, "cache_write_input_tokens": 0, "output_tokens": 8345, "reasoning_output_tokens": 2473, "total_tokens": 594912 }, "thread_token_usage": { "input_tokens": 586567, "cached_input_tokens": 545280, "cache_write_input_tokens": 0, "output_tokens": 8345, "reasoning_output_tokens": 2473, "total_tokens": 594912 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c61077c6-85ab-486f-8ee8-c5c248cf2ae3", "process_id": "71330", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5584017 explained 0.24970774\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5584017 explained 0.24970774\r\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 445298511 }, "formatted_output": "matrix (8456, 30000) 5584017 explained 0.24970774\r\n" } } ## Step 28 · agent · 2026-09-25T00:59:37.861Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:59:40.198Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_p130.parquet solution/validation_p170.parquet solution/validation_p210.parquet solution/validation_p260.parquet solution/validation_p320.parquet solution/validation_p400.parquet solution/validation_p550.parquet; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:59:57.956Z Script completed Wall time 17.8 seconds Output: validation_p130.parquet "quality_ndcg@10": 0.023384, validation_p170.parquet "quality_ndcg@10": 0.023492, validation_p210.parquet "quality_ndcg@10": 0.023485, validation_p260.parquet "quality_ndcg@10": 0.023058, validation_p320.parquet "quality_ndcg@10": 0.021571, validation_p400.parquet "quality_ndcg@10": 0.019594, validation_p550.parquet "quality_ndcg@10": 0.017497, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c778f8f087d288336146aca70cb4", "usage": { "input_tokens": 30294, "cached_input_tokens": 30080, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 6, "total_tokens": 30437 }, "turn_token_usage": { "input_tokens": 616861, "cached_input_tokens": 575360, "cache_write_input_tokens": 0, "output_tokens": 8488, "reasoning_output_tokens": 2479, "total_tokens": 625349 }, "thread_token_usage": { "input_tokens": 616861, "cached_input_tokens": 575360, "cache_write_input_tokens": 0, "output_tokens": 8488, "reasoning_output_tokens": 2479, "total_tokens": 625349 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b7c7db83-db5b-4560-872f-94e7cc527228", "process_id": "37475", "command": [ "/bin/sh", "-lc", "for f in solution/validation_p130.parquet solution/validation_p170.parquet solution/validation_p210.parquet solution/validation_p260.parquet solution/validation_p320.parquet solution/validation_p400.parquet solution/validation_p550.parquet; do printf '%s ' \"$(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 solution/validation_p130.parquet solution/validation_p170.parquet solution/validation_p210.parquet solution/validation_p260.parquet solution/validation_p320.parquet solution/validation_p400.parquet solution/validation_p550.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\": 0.023384,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023492,\nvalidation_p210.parquet \"quality_ndcg@10\": 0.023485,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.023058,\nvalidation_p320.parquet \"quality_ndcg@10\": 0.021571,\nvalidation_p400.parquet \"quality_ndcg@10\": 0.019594,\nvalidation_p550.parquet \"quality_ndcg@10\": 0.017497,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.023384,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023492,\nvalidation_p210.parquet \"quality_ndcg@10\": 0.023485,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.023058,\nvalidation_p320.parquet \"quality_ndcg@10\": 0.021571,\nvalidation_p400.parquet \"quality_ndcg@10\": 0.019594,\nvalidation_p550.parquet \"quality_ndcg@10\": 0.017497,\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 589070366 }, "formatted_output": " \"quality_ndcg@10\": 0.023384,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023492,\nvalidation_p210.parquet \"quality_ndcg@10\": 0.023485,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.023058,\nvalidation_p320.parquet \"quality_ndcg@10\": 0.021571,\nvalidation_p400.parquet \"quality_ndcg@10\": 0.019594,\nvalidation_p550.parquet \"quality_ndcg@10\": 0.017497,\n" } } ## Step 29 · agent · 2026-09-25T01:00:00.433Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:00:03.058Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- blends={'p130':1.3,'p170':1.7,'p210':2.1,'p260':2.6,'p320':3.2,'p400':4.0,'p550':5.5}\\n+ blends={'p120':1.2,'p140':1.4,'p155':1.55,'p170':1.7,'p185':1.85,'p200':2.0,'p220':2.2}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:00:03.102Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-21a97ece-9118-4920-9449-c9975e0fa9e7", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -110,3 +110,3 @@\n else:\n- blends={'p130':1.3,'p170':1.7,'p210':2.1,'p260':2.6,'p320':3.2,'p400':4.0,'p550':5.5}\n+ blends={'p120':1.2,'p140':1.4,'p155':1.55,'p170':1.7,'p185':1.85,'p200':2.0,'p220':2.2}\n write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c78e240887d283fda64dc0fe5e73", "usage": { "input_tokens": 30593, "cached_input_tokens": 30080, "cache_write_input_tokens": 0, "output_tokens": 205, "reasoning_output_tokens": 50, "total_tokens": 30798 }, "turn_token_usage": { "input_tokens": 647454, "cached_input_tokens": 605440, "cache_write_input_tokens": 0, "output_tokens": 8693, "reasoning_output_tokens": 2529, "total_tokens": 656147 }, "thread_token_usage": { "input_tokens": 647454, "cached_input_tokens": 605440, "cache_write_input_tokens": 0, "output_tokens": 8693, "reasoning_output_tokens": 2529, "total_tokens": 656147 } } ### exec · 2026-09-25T01:00:06.730Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 320\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T01:00:07.896Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"66a9b0","wall_time_seconds":1.001632116,"session_id":1731,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c79380dc87d2a023d447dd93aba2", "usage": { "input_tokens": 30822, "cached_input_tokens": 30464, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 0, "total_tokens": 30893 }, "turn_token_usage": { "input_tokens": 678276, "cached_input_tokens": 635904, "cache_write_input_tokens": 0, "output_tokens": 8764, "reasoning_output_tokens": 2529, "total_tokens": 687040 }, "thread_token_usage": { "input_tokens": 678276, "cached_input_tokens": 635904, "cache_write_input_tokens": 0, "output_tokens": 8764, "reasoning_output_tokens": 2529, "total_tokens": 687040 } } ### exec · 2026-09-25T01:00:12.687Z { "code": "const r = await tools.write_stdin({session_id:1731,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:00:32.830Z Script completed Wall time 20.1 seconds Output: matrix (8456, 30000) 5584017 explained 0.31274486 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7983c3087d294aa8376a6cdc643", "usage": { "input_tokens": 30952, "cached_input_tokens": 30592, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 30997 }, "turn_token_usage": { "input_tokens": 709228, "cached_input_tokens": 666496, "cache_write_input_tokens": 0, "output_tokens": 8809, "reasoning_output_tokens": 2529, "total_tokens": 718037 }, "thread_token_usage": { "input_tokens": 709228, "cached_input_tokens": 666496, "cache_write_input_tokens": 0, "output_tokens": 8809, "reasoning_output_tokens": 2529, "total_tokens": 718037 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-824d8fee-d13a-4ddf-a87b-554c58b89fce", "process_id": "1731", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 320" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 320" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5584017 explained 0.31274486\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5584017 explained 0.31274486\r\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 934801929 }, "formatted_output": "matrix (8456, 30000) 5584017 explained 0.31274486\r\n" } } ### exec · 2026-09-25T01:00:40.202Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.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\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:00:58.162Z Script completed Wall time 18.0 seconds Output: validation_p120.parquet "quality_ndcg@10": 0.022942, validation_p140.parquet "quality_ndcg@10": 0.023429, validation_p155.parquet "quality_ndcg@10": 0.023202, validation_p170.parquet "quality_ndcg@10": 0.023293, validation_p185.parquet "quality_ndcg@10": 0.023241, validation_p200.parquet "quality_ndcg@10": 0.022682, validation_p220.parquet "quality_ndcg@10": 0.022526, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7b0fe4887d2b5904ef5bd4460f4", "usage": { "input_tokens": 31041, "cached_input_tokens": 30720, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 0, "total_tokens": 31176 }, "turn_token_usage": { "input_tokens": 740269, "cached_input_tokens": 697216, "cache_write_input_tokens": 0, "output_tokens": 8944, "reasoning_output_tokens": 2529, "total_tokens": 749213 }, "thread_token_usage": { "input_tokens": 740269, "cached_input_tokens": 697216, "cache_write_input_tokens": 0, "output_tokens": 8944, "reasoning_output_tokens": 2529, "total_tokens": 749213 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e86dcb48-61f3-49f4-814b-58dd3f9cd662", "process_id": "67296", "command": [ "/bin/sh", "-lc", "for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.parquet; do printf '%s ' \"$(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\": 0.022942,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023429,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.023202,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023293,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023241,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.022682,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.022526,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.022942,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023429,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.023202,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023293,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023241,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.022682,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.022526,\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 790382760 }, "formatted_output": " \"quality_ndcg@10\": 0.022942,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023429,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.023202,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023293,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023241,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.022682,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.022526,\n" } } ## Step 30 · agent · 2026-09-25T01:01:14.759Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-25T01:01:16.339Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:01:33.090Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n def latent(stage, history, components=96, final=False):\\n@@\\n if final:\\n- write_top(uids,items,eligible,(A,B),pop,{'':0.18},'submission')\\n+ write_top(uids,items,eligible,(A,B),pop,{'':1.70},'submission')\\n@@\\n write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\\n+\\n+\\n+def user_knn(k=150):\\n+ uids, items, eligible = load_candidates('validation')\\n+ X = interaction_matrix(['train'],uids,items)\\n+ df=np.asarray((X!=0).sum(axis=0)).ravel()\\n+ idf=np.log1p(len(uids)/(20+df)).astype(np.float32)\\n+ Q=X@sparse.diags(idf)\\n+ norm=np.sqrt(np.asarray(Q.multiply(Q).sum(1)).ravel())+1e-6\\n+ Q=sparse.diags(1/norm)@Q\\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet').set_index('item_id')\\n+ pop=np.log1p(cat.loc[items,'organic_positive_listens'].to_numpy()).astype(np.float32); pop=(pop-pop.mean())/pop.std()\\n+ blends={'k_p08':.8,'k_p12':1.2,'k_p16':1.6,'k_p20':2.0,'k_p25':2.5}\\n+ outs={n:[] for n in blends}\\n+ for lo in range(0,len(uids),128):\\n+ hi=min(lo+128,len(uids)); sim=(Q[lo:hi]@Q.T).toarray(); sim[np.arange(hi-lo),np.arange(lo,hi)]=-np.inf\\n+ ix=np.argpartition(sim,-k,axis=1)[:,-k:]\\n+ sv=np.take_along_axis(sim,ix,axis=1); rr=np.repeat(np.arange(hi-lo),k)\\n+ N=sparse.csr_matrix((sv.ravel(),(rr,ix.ravel())),shape=(hi-lo,len(uids)))\\n+ s=(N@X).toarray().astype(np.float32); s-=s.mean(1,keepdims=True); s/=s.std(1,keepdims=True)+1e-6\\n+ for n,a in blends.items():\\n+ z=s+a*pop; z[~eligible[lo:hi]]=-np.inf; ti=np.argpartition(z,-100,axis=1)[:,-100:]\\n+ order=np.take_along_axis(z,ti,1).argsort(1)[:,::-1]; outs[n].append(np.take_along_axis(ti,order,1))\\n+ for n,ch in outs.items():\\n+ top=np.vstack(ch); pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet(OUT/f'validation_{n}.parquet',index=False)\\n@@\\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\\n@@\\n if a.latent:\\n@@\\n else: latent('validation',['train'],a.components,False)\\n+ if a.knn: user_knn(a.neighbors)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:01:33.151Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2df4d281-5f2b-4503-a68e-b272efb2bbca", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -108,3 +108,3 @@\n if final:\n- write_top(uids,items,eligible,(A,B),pop,{'':0.18},'submission')\n+ write_top(uids,items,eligible,(A,B),pop,{'':1.70},'submission')\n else:\n@@ -114,4 +114,29 @@\n \n+def user_knn(k=150):\n+ uids, items, eligible = load_candidates('validation')\n+ X = interaction_matrix(['train'],uids,items)\n+ df=np.asarray((X!=0).sum(axis=0)).ravel()\n+ idf=np.log1p(len(uids)/(20+df)).astype(np.float32)\n+ Q=X@sparse.diags(idf)\n+ norm=np.sqrt(np.asarray(Q.multiply(Q).sum(1)).ravel())+1e-6\n+ Q=sparse.diags(1/norm)@Q\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet').set_index('item_id')\n+ pop=np.log1p(cat.loc[items,'organic_positive_listens'].to_numpy()).astype(np.float32); pop=(pop-pop.mean())/pop.std()\n+ blends={'k_p08':.8,'k_p12':1.2,'k_p16':1.6,'k_p20':2.0,'k_p25':2.5}\n+ outs={n:[] for n in blends}\n+ for lo in range(0,len(uids),128):\n+ hi=min(lo+128,len(uids)); sim=(Q[lo:hi]@Q.T).toarray(); sim[np.arange(hi-lo),np.arange(lo,hi)]=-np.inf\n+ ix=np.argpartition(sim,-k,axis=1)[:,-k:]\n+ sv=np.take_along_axis(sim,ix,axis=1); rr=np.repeat(np.arange(hi-lo),k)\n+ N=sparse.csr_matrix((sv.ravel(),(rr,ix.ravel())),shape=(hi-lo,len(uids)))\n+ s=(N@X).toarray().astype(np.float32); s-=s.mean(1,keepdims=True); s/=s.std(1,keepdims=True)+1e-6\n+ for n,a in blends.items():\n+ z=s+a*pop; z[~eligible[lo:hi]]=-np.inf; ti=np.argpartition(z,-100,axis=1)[:,-100:]\n+ order=np.take_along_axis(z,ti,1).argsort(1)[:,::-1]; outs[n].append(np.take_along_axis(ti,order,1))\n+ for n,ch in outs.items():\n+ top=np.vstack(ch); pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet(OUT/f'validation_{n}.parquet',index=False)\n+\n+\n if __name__ == '__main__':\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\n if a.baseline:\n@@ -122 +147,2 @@\n else: latent('validation',['train'],a.components,False)\n+ if a.knn: user_knn(a.neighbors)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7ca62e487d2ae4da0cde2717ddb", "usage": { "input_tokens": 31332, "cached_input_tokens": 30848, "cache_write_input_tokens": 0, "output_tokens": 1536, "reasoning_output_tokens": 593, "total_tokens": 32868 }, "turn_token_usage": { "input_tokens": 771601, "cached_input_tokens": 728064, "cache_write_input_tokens": 0, "output_tokens": 10480, "reasoning_output_tokens": 3122, "total_tokens": 782081 }, "thread_token_usage": { "input_tokens": 771601, "cached_input_tokens": 728064, "cache_write_input_tokens": 0, "output_tokens": 10480, "reasoning_output_tokens": 3122, "total_tokens": 782081 } } ## Step 32 · agent · 2026-09-25T01:01:41.204Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:01:42.373Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --knn --neighbors 150\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T01:01:43.539Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5f08a8","wall_time_seconds":1.000936105,"session_id":17135,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7edba6c87d285b1fc74eed0d6a9", "usage": { "input_tokens": 32892, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 11, "total_tokens": 32977 }, "turn_token_usage": { "input_tokens": 804493, "cached_input_tokens": 759168, "cache_write_input_tokens": 0, "output_tokens": 10565, "reasoning_output_tokens": 3133, "total_tokens": 815058 }, "thread_token_usage": { "input_tokens": 804493, "cached_input_tokens": 759168, "cache_write_input_tokens": 0, "output_tokens": 10565, "reasoning_output_tokens": 3133, "total_tokens": 815058 } } ### exec · 2026-09-25T01:01:50.056Z { "code": "const r = await tools.write_stdin({session_id:17135,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:02:07.561Z Script completed Wall time 17.5 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c7f7e14c87d2b48794d31682359b", "usage": { "input_tokens": 33036, "cached_input_tokens": 32768, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 33081 }, "turn_token_usage": { "input_tokens": 837529, "cached_input_tokens": 791936, "cache_write_input_tokens": 0, "output_tokens": 10610, "reasoning_output_tokens": 3133, "total_tokens": 848139 }, "thread_token_usage": { "input_tokens": 837529, "cached_input_tokens": 791936, "cache_write_input_tokens": 0, "output_tokens": 10610, "reasoning_output_tokens": 3133, "total_tokens": 848139 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d19fad7c-ae69-4f77-9afe-e216178311be", "process_id": "17135", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --knn --neighbors 150" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --knn --neighbors 150" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 17503290 }, "formatted_output": "" } } ### exec · 2026-09-25T01:02:14.577Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_k_*.parquet; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:02:27.236Z Script completed Wall time 12.7 seconds Output: validation_k_p08.parquet "quality_ndcg@10": 0.016556, validation_k_p12.parquet "quality_ndcg@10": 0.016363, validation_k_p16.parquet "quality_ndcg@10": 0.016105, validation_k_p20.parquet "quality_ndcg@10": 0.015756, validation_k_p25.parquet "quality_ndcg@10": 0.015523, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c80fe20087d2a4e8a8f7f11f7181", "usage": { "input_tokens": 33104, "cached_input_tokens": 32896, "cache_write_input_tokens": 0, "output_tokens": 94, "reasoning_output_tokens": 0, "total_tokens": 33198 }, "turn_token_usage": { "input_tokens": 870633, "cached_input_tokens": 824832, "cache_write_input_tokens": 0, "output_tokens": 10704, "reasoning_output_tokens": 3133, "total_tokens": 881337 }, "thread_token_usage": { "input_tokens": 870633, "cached_input_tokens": 824832, "cache_write_input_tokens": 0, "output_tokens": 10704, "reasoning_output_tokens": 3133, "total_tokens": 881337 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-279374ec-97ff-4263-86dd-2099789d23a2", "process_id": "57695", "command": [ "/bin/sh", "-lc", "for f in solution/validation_k_*.parquet; do printf '%s ' \"$(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 solution/validation_k_*.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\": 0.016556,\nvalidation_k_p12.parquet \"quality_ndcg@10\": 0.016363,\nvalidation_k_p16.parquet \"quality_ndcg@10\": 0.016105,\nvalidation_k_p20.parquet \"quality_ndcg@10\": 0.015756,\nvalidation_k_p25.parquet \"quality_ndcg@10\": 0.015523,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.016556,\nvalidation_k_p12.parquet \"quality_ndcg@10\": 0.016363,\nvalidation_k_p16.parquet \"quality_ndcg@10\": 0.016105,\nvalidation_k_p20.parquet \"quality_ndcg@10\": 0.015756,\nvalidation_k_p25.parquet \"quality_ndcg@10\": 0.015523,\n", "exit_code": 0, "duration": { "secs": 12, "nanos": 490196660 }, "formatted_output": " \"quality_ndcg@10\": 0.016556,\nvalidation_k_p12.parquet \"quality_ndcg@10\": 0.016363,\nvalidation_k_p16.parquet \"quality_ndcg@10\": 0.016105,\nvalidation_k_p20.parquet \"quality_ndcg@10\": 0.015756,\nvalidation_k_p25.parquet \"quality_ndcg@10\": 0.015523,\n" } } ## Step 33 · agent · 2026-09-25T01:02:36.172Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:02:45.688Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n-def interaction_matrix(history, uids, items):\\n+def interaction_matrix(history, uids, items, threshold=50, organic_bonus=.75):\\n@@\\n- d = d[(d.played_ratio_pct >= 50) & d.uid.isin(uids) & d.item_id.isin(items)]\\n+ d = d[(d.played_ratio_pct >= threshold) & d.uid.isin(uids) & d.item_id.isin(items)]\\n@@\\n- val = (1.0 + .75*d.is_organic.to_numpy() +\\n+ val = (1.0 + organic_bonus*d.is_organic.to_numpy() +\\n@@\\n-def latent(stage, history, components=96, final=False):\\n+def latent(stage, history, components=96, final=False, threshold=50, organic_bonus=.75):\\n@@\\n- X = interaction_matrix(history, uids, items)\\n+ X = interaction_matrix(history, uids, items, threshold, organic_bonus)\\n@@\\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); ap.add_argument('--threshold',type=int,default=50); ap.add_argument('--organic-bonus',type=float,default=.75); a=ap.parse_args()\\n@@\\n- if a.final: latent('test',['train','val'],a.components,True)\\n- else: latent('validation',['train'],a.components,False)\\n+ if a.final: latent('test',['train','val'],a.components,True,a.threshold,a.organic_bonus)\\n+ else: latent('validation',['train'],a.components,False,a.threshold,a.organic_bonus)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:02:45.749Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8695e096-8277-4a34-a931-0a13f8092c1d", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -33,3 +33,3 @@\n \n-def interaction_matrix(history, uids, items):\n+def interaction_matrix(history, uids, items, threshold=50, organic_bonus=.75):\n \"\"\"Weighted implicit matrix. Rows are target users, columns catalog tracks.\"\"\"\n@@ -41,3 +41,3 @@\n columns=['uid','item_id','is_organic','played_ratio_pct'])\n- d = d[(d.played_ratio_pct >= 50) & d.uid.isin(uids) & d.item_id.isin(items)]\n+ d = d[(d.played_ratio_pct >= threshold) & d.uid.isin(uids) & d.item_id.isin(items)]\n row = uid_index.loc[d.uid.to_numpy()].to_numpy()\n@@ -45,3 +45,3 @@\n # Organic and deeply completed plays are more diagnostic of taste.\n- val = (1.0 + .75*d.is_organic.to_numpy() +\n+ val = (1.0 + organic_bonus*d.is_organic.to_numpy() +\n .5*(d.played_ratio_pct.to_numpy() >= 80)).astype(np.float32)\n@@ -90,5 +90,5 @@\n \n-def latent(stage, history, components=96, final=False):\n+def latent(stage, history, components=96, final=False, threshold=50, organic_bonus=.75):\n uids, items, eligible = load_candidates(stage)\n- X = interaction_matrix(history, uids, items)\n+ X = interaction_matrix(history, uids, items, threshold, organic_bonus)\n # BM25-style row length and item-frequency normalization.\n@@ -140,3 +140,3 @@\n if __name__ == '__main__':\n- ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); a=ap.parse_args()\n+ ap=argparse.ArgumentParser(); ap.add_argument('--baseline', action='store_true'); ap.add_argument('--latent',action='store_true'); ap.add_argument('--knn',action='store_true'); ap.add_argument('--neighbors',type=int,default=150); ap.add_argument('--final',action='store_true'); ap.add_argument('--components',type=int,default=96); ap.add_argument('--threshold',type=int,default=50); ap.add_argument('--organic-bonus',type=float,default=.75); a=ap.parse_args()\n if a.baseline:\n@@ -145,4 +145,4 @@\n if a.latent:\n- if a.final: latent('test',['train','val'],a.components,True)\n- else: latent('validation',['train'],a.components,False)\n+ if a.final: latent('test',['train','val'],a.components,True,a.threshold,a.organic_bonus)\n+ else: latent('validation',['train'],a.components,False,a.threshold,a.organic_bonus)\n if a.knn: user_knn(a.neighbors)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c823762c87d281ba1415fe4af08b", "usage": { "input_tokens": 33321, "cached_input_tokens": 32896, "cache_write_input_tokens": 0, "output_tokens": 704, "reasoning_output_tokens": 139, "total_tokens": 34025 }, "turn_token_usage": { "input_tokens": 903954, "cached_input_tokens": 857728, "cache_write_input_tokens": 0, "output_tokens": 11408, "reasoning_output_tokens": 3272, "total_tokens": 915362 }, "thread_token_usage": { "input_tokens": 903954, "cached_input_tokens": 857728, "cache_write_input_tokens": 0, "output_tokens": 11408, "reasoning_output_tokens": 3272, "total_tokens": 915362 } } ## Step 34 · agent · 2026-09-25T01:02:52.361Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:02:53.813Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T01:02:54.978Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"41520a","wall_time_seconds":1.001667951,"session_id":21308,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c836273887d29190144f22be23fd", "usage": { "input_tokens": 34049, "cached_input_tokens": 33152, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 13, "total_tokens": 34147 }, "turn_token_usage": { "input_tokens": 938003, "cached_input_tokens": 890880, "cache_write_input_tokens": 0, "output_tokens": 11506, "reasoning_output_tokens": 3285, "total_tokens": 949509 }, "thread_token_usage": { "input_tokens": 938003, "cached_input_tokens": 890880, "cache_write_input_tokens": 0, "output_tokens": 11506, "reasoning_output_tokens": 3285, "total_tokens": 949509 } } ### exec · 2026-09-25T01:03:00.139Z { "code": "const r = await tools.write_stdin({session_id:21308,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:03:14.207Z Script completed Wall time 14.1 seconds Output: matrix (8456, 30000) 5372656 explained 0.24368818 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c83f2b1887d2bd327047f11d8b34", "usage": { "input_tokens": 34204, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 34249 }, "turn_token_usage": { "input_tokens": 972207, "cached_input_tokens": 924800, "cache_write_input_tokens": 0, "output_tokens": 11551, "reasoning_output_tokens": 3285, "total_tokens": 983758 }, "thread_token_usage": { "input_tokens": 972207, "cached_input_tokens": 924800, "cache_write_input_tokens": 0, "output_tokens": 11551, "reasoning_output_tokens": 3285, "total_tokens": 983758 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae05e95b-ca3b-4e96-bae1-f4e01c8cdfd1", "process_id": "21308", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 228455279 }, "formatted_output": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n" } } ## Step 35 · agent · 2026-09-25T01:03:19.726Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:03:22.021Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.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\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:03:40.073Z Script completed Wall time 18.0 seconds Output: validation_p120.parquet "quality_ndcg@10": 0.023416, validation_p140.parquet "quality_ndcg@10": 0.023138, validation_p155.parquet "quality_ndcg@10": 0.02317, validation_p170.parquet "quality_ndcg@10": 0.023509, validation_p185.parquet "quality_ndcg@10": 0.023809, validation_p200.parquet "quality_ndcg@10": 0.023747, validation_p220.parquet "quality_ndcg@10": 0.023853, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c852828c87d2a4bebf54a62a061f", "usage": { "input_tokens": 34293, "cached_input_tokens": 34048, "cache_write_input_tokens": 0, "output_tokens": 158, "reasoning_output_tokens": 21, "total_tokens": 34451 }, "turn_token_usage": { "input_tokens": 1006500, "cached_input_tokens": 958848, "cache_write_input_tokens": 0, "output_tokens": 11709, "reasoning_output_tokens": 3306, "total_tokens": 1018209 }, "thread_token_usage": { "input_tokens": 1006500, "cached_input_tokens": 958848, "cache_write_input_tokens": 0, "output_tokens": 11709, "reasoning_output_tokens": 3306, "total_tokens": 1018209 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b460c396-27e1-4c36-bc39-18b14730436e", "process_id": "61976", "command": [ "/bin/sh", "-lc", "for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.parquet; do printf '%s ' \"$(basename \"$f\")\"; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_p120.parquet solution/validation_p140.parquet solution/validation_p155.parquet solution/validation_p170.parquet solution/validation_p185.parquet solution/validation_p200.parquet solution/validation_p220.parquet; do printf '%s ' \"$(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\": 0.023416,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023138,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.02317,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023509,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023809,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.023416,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023138,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.02317,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023509,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023809,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 840139018 }, "formatted_output": " \"quality_ndcg@10\": 0.023416,\nvalidation_p140.parquet \"quality_ndcg@10\": 0.023138,\nvalidation_p155.parquet \"quality_ndcg@10\": 0.02317,\nvalidation_p170.parquet \"quality_ndcg@10\": 0.023509,\nvalidation_p185.parquet \"quality_ndcg@10\": 0.023809,\nvalidation_p200.parquet \"quality_ndcg@10\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\n" } } ## Step 36 · agent · 2026-09-25T01:03:46.217Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:03:48.877Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- blends={'p120':1.2,'p140':1.4,'p155':1.55,'p170':1.7,'p185':1.85,'p200':2.0,'p220':2.2}\\n+ blends={'p200':2.0,'p220':2.2,'p240':2.4,'p260':2.6,'p280':2.8,'p300':3.0,'p330':3.3}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:03:48.940Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-51d2d662-644c-4382-ac15-373a19b0b299", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -110,3 +110,3 @@\n else:\n- blends={'p120':1.2,'p140':1.4,'p155':1.55,'p170':1.7,'p185':1.85,'p200':2.0,'p220':2.2}\n+ blends={'p200':2.0,'p220':2.2,'p240':2.4,'p260':2.6,'p280':2.8,'p300':3.0,'p330':3.3}\n write_top(uids,items,eligible,(A,B),pop,blends,'validation_')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c86c41a087d291b6c9e5f04c56b3", "usage": { "input_tokens": 34607, "cached_input_tokens": 34176, "cache_write_input_tokens": 0, "output_tokens": 191, "reasoning_output_tokens": 36, "total_tokens": 34798 }, "turn_token_usage": { "input_tokens": 1041107, "cached_input_tokens": 993024, "cache_write_input_tokens": 0, "output_tokens": 11900, "reasoning_output_tokens": 3342, "total_tokens": 1053007 }, "thread_token_usage": { "input_tokens": 1041107, "cached_input_tokens": 993024, "cache_write_input_tokens": 0, "output_tokens": 11900, "reasoning_output_tokens": 3342, "total_tokens": 1053007 } } ### exec · 2026-09-25T01:03:54.738Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T01:03:55.904Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"46724a","wall_time_seconds":1.001165761,"session_id":58194,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c875458087d286fee19567f6fb15", "usage": { "input_tokens": 34822, "cached_input_tokens": 34432, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 0, "total_tokens": 34905 }, "turn_token_usage": { "input_tokens": 1075929, "cached_input_tokens": 1027456, "cache_write_input_tokens": 0, "output_tokens": 11983, "reasoning_output_tokens": 3342, "total_tokens": 1087912 }, "thread_token_usage": { "input_tokens": 1075929, "cached_input_tokens": 1027456, "cache_write_input_tokens": 0, "output_tokens": 11983, "reasoning_output_tokens": 3342, "total_tokens": 1087912 } } ### exec · 2026-09-25T01:03:58.917Z { "code": "const r = await tools.write_stdin({session_id:58194,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:04:15.210Z Script completed Wall time 16.3 seconds Output: matrix (8456, 30000) 5372656 explained 0.24368818 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c87c134487d2ba568b546a2c52c1", "usage": { "input_tokens": 34962, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 35007 }, "turn_token_usage": { "input_tokens": 1110891, "cached_input_tokens": 1062144, "cache_write_input_tokens": 0, "output_tokens": 12028, "reasoning_output_tokens": 3342, "total_tokens": 1122919 }, "thread_token_usage": { "input_tokens": 1110891, "cached_input_tokens": 1062144, "cache_write_input_tokens": 0, "output_tokens": 12028, "reasoning_output_tokens": 3342, "total_tokens": 1122919 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d7db8c24-a612-4602-8986-c34ae93d2f03", "process_id": "58194", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/build_ranker.py --latent --components 192 --threshold 80 --organic-bonus 1.5" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 304790011 }, "formatted_output": "matrix (8456, 30000) 5372656 explained 0.24368818\r\n" } } ### exec · 2026-09-25T01:04:19.735Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_p200.parquet solution/validation_p220.parquet solution/validation_p240.parquet solution/validation_p260.parquet solution/validation_p280.parquet solution/validation_p300.parquet solution/validation_p330.parquet; do printf '%s ' \\\"$(basename \\\"$f\\\")\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:04:37.575Z Script completed Wall time 17.8 seconds Output: validation_p200.parquet "quality_ndcg@10": 0.023747, validation_p220.parquet "quality_ndcg@10": 0.023853, validation_p240.parquet "quality_ndcg@10": 0.023038, validation_p260.parquet "quality_ndcg@10": 0.022544, validation_p280.parquet "quality_ndcg@10": 0.022304, validation_p300.parquet "quality_ndcg@10": 0.021836, validation_p330.parquet "quality_ndcg@10": 0.021096, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c88f66d487d2a49c4806457614e2", "usage": { "input_tokens": 35051, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 0, "total_tokens": 35186 }, "turn_token_usage": { "input_tokens": 1145942, "cached_input_tokens": 1096960, "cache_write_input_tokens": 0, "output_tokens": 12163, "reasoning_output_tokens": 3342, "total_tokens": 1158105 }, "thread_token_usage": { "input_tokens": 1145942, "cached_input_tokens": 1096960, "cache_write_input_tokens": 0, "output_tokens": 12163, "reasoning_output_tokens": 3342, "total_tokens": 1158105 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ad89d084-bcef-4809-9471-2ad7b697c665", "process_id": "43269", "command": [ "/bin/sh", "-lc", "for f in solution/validation_p200.parquet solution/validation_p220.parquet solution/validation_p240.parquet solution/validation_p260.parquet solution/validation_p280.parquet solution/validation_p300.parquet solution/validation_p330.parquet; do printf '%s ' \"$(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 solution/validation_p200.parquet solution/validation_p220.parquet solution/validation_p240.parquet solution/validation_p260.parquet solution/validation_p280.parquet solution/validation_p300.parquet solution/validation_p330.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\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\nvalidation_p240.parquet \"quality_ndcg@10\": 0.023038,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.022544,\nvalidation_p280.parquet \"quality_ndcg@10\": 0.022304,\nvalidation_p300.parquet \"quality_ndcg@10\": 0.021836,\nvalidation_p330.parquet \"quality_ndcg@10\": 0.021096,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\nvalidation_p240.parquet \"quality_ndcg@10\": 0.023038,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.022544,\nvalidation_p280.parquet \"quality_ndcg@10\": 0.022304,\nvalidation_p300.parquet \"quality_ndcg@10\": 0.021836,\nvalidation_p330.parquet \"quality_ndcg@10\": 0.021096,\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 671828745 }, "formatted_output": " \"quality_ndcg@10\": 0.023747,\nvalidation_p220.parquet \"quality_ndcg@10\": 0.023853,\nvalidation_p240.parquet \"quality_ndcg@10\": 0.023038,\nvalidation_p260.parquet \"quality_ndcg@10\": 0.022544,\nvalidation_p280.parquet \"quality_ndcg@10\": 0.022304,\nvalidation_p300.parquet \"quality_ndcg@10\": 0.021836,\nvalidation_p330.parquet \"quality_ndcg@10\": 0.021096,\n" } } ## Step 37 · agent · 2026-09-25T01:04:42.766Z ### Thinking unavailable — no text recorded The aligned model variant—80%+ listens, stronger organic weighting, 192 latent factors—improves again to public NDCG@10 `0.023853` (versus `0.010577` for popularity). The optimum is a 2.2-standard-deviation popularity blend. I’m now freezing that recipe and refitting on train+validation with the test-cutoff preference state. ### exec · 2026-09-25T01:04:45.794Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- write_top(uids,items,eligible,(A,B),pop,{'':1.70},'submission')\\n+ write_top(uids,items,eligible,(A,B),pop,{'':2.20},'submission')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:04:45.858Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-20c502ee-5c1d-4c48-8bb4-e9bf71b518c3", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -108,3 +108,3 @@\n if final:\n- write_top(uids,items,eligible,(A,B),pop,{'':1.70},'submission')\n+ write_top(uids,items,eligible,(A,B),pop,{'':2.20},'submission')\n else:\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8a5c66087d2bd5a45e0b18efd8c", "usage": { "input_tokens": 35342, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 355, "reasoning_output_tokens": 164, "total_tokens": 35697 }, "turn_token_usage": { "input_tokens": 1181284, "cached_input_tokens": 1131776, "cache_write_input_tokens": 0, "output_tokens": 12518, "reasoning_output_tokens": 3506, "total_tokens": 1193802 }, "thread_token_usage": { "input_tokens": 1181284, "cached_input_tokens": 1131776, "cache_write_input_tokens": 0, "output_tokens": 12518, "reasoning_output_tokens": 3506, "total_tokens": 1193802 } } ### exec · 2026-09-25T01:04:49.478Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/validation_p220.parquet solution/validation.parquet\\n/opt/task-venv/bin/python -u solution/build_ranker.py --latent --final --components 192 --threshold 80 --organic-bonus 1.5\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000,\"tty\":true}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T01:04:50.644Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9add99","wall_time_seconds":1.001514943,"session_id":22717,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8ae1c0087d2bcb34afedb4583e3", "usage": { "input_tokens": 35721, "cached_input_tokens": 29184, "cache_write_input_tokens": 0, "output_tokens": 99, "reasoning_output_tokens": 0, "total_tokens": 35820 }, "turn_token_usage": { "input_tokens": 1217005, "cached_input_tokens": 1160960, "cache_write_input_tokens": 0, "output_tokens": 12617, "reasoning_output_tokens": 3506, "total_tokens": 1229622 }, "thread_token_usage": { "input_tokens": 1217005, "cached_input_tokens": 1160960, "cache_write_input_tokens": 0, "output_tokens": 12617, "reasoning_output_tokens": 3506, "total_tokens": 1229622 } } ### exec · 2026-09-25T01:04:52.520Z { "code": "const r = await tools.write_stdin({session_id:22717,chars:\"\",yield_time_ms:30000,max_output_tokens:2000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:05:04.215Z Script completed Wall time 11.7 seconds Output: matrix (8456, 30000) 5523236 explained 0.24520041 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8b2eca487d2be4614980990bcfb", "usage": { "input_tokens": 35877, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 35922 }, "turn_token_usage": { "input_tokens": 1252882, "cached_input_tokens": 1196544, "cache_write_input_tokens": 0, "output_tokens": 12662, "reasoning_output_tokens": 3506, "total_tokens": 1265544 }, "thread_token_usage": { "input_tokens": 1252882, "cached_input_tokens": 1196544, "cache_write_input_tokens": 0, "output_tokens": 12662, "reasoning_output_tokens": 3506, "total_tokens": 1265544 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50b5afe8-45e8-4cf5-929e-30b004e168b6", "process_id": "22717", "command": [ "/bin/sh", "-lc", "cp solution/validation_p220.parquet solution/validation.parquet\n/opt/task-venv/bin/python -u solution/build_ranker.py --latent --final --components 192 --threshold 80 --organic-bonus 1.5" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/validation_p220.parquet solution/validation.parquet\n/opt/task-venv/bin/python -u solution/build_ranker.py --latent --final --components 192 --threshold 80 --organic-bonus 1.5" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (8456, 30000) 5523236 explained 0.24520041\r\n", "stderr": "", "aggregated_output": "matrix (8456, 30000) 5523236 explained 0.24520041\r\n", "exit_code": 0, "duration": { "secs": 14, "nanos": 570205315 }, "formatted_output": "matrix (8456, 30000) 5523236 explained 0.24520041\r\n" } } ## Step 38 · agent · 2026-09-25T01:05:07.164Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:12.458Z { "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 100 eligible,\\n+distinct tracks per target user.\\n+\\n+The model is a weighted implicit-feedback recommender. It keeps 80%+ completed\\n+catalog listens, weights organic plays more heavily, adds the cutoff-correct\\n+active like/dislike state, applies user-length and item-frequency normalization,\\n+and fits a 192-component truncated SVD. Candidate scores blend the personalized\\n+latent reconstruction with training organic popularity. All ineligible tracks\\n+are masked before top-100 selection.\\n+\\n+Model choices were made using train history only against the subsequent public\\n+window. The selected validation ranking scored **0.023853 NDCG@10** (the pure\\n+popularity baseline scored 0.010577). The final model is then refit on\\n+train+validation events and uses the supplied test-cutoff preference state.\\n+\\n+Rebuild from `/app` with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python solution/build_ranker.py \\\\\\n+ --latent --final --components 192 --threshold 80 --organic-bonus 1.5\\n+```\\n+\\n+`validation.parquet` preserves the selected public-window ranking.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:05:12.508Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8c4e52d2-088a-48f9-95f8-8a434f5189d9", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Music-discovery ranker\n\n`submission.parquet` is the final test-cutoff ranking. It contains 100 eligible,\ndistinct tracks per target user.\n\nThe model is a weighted implicit-feedback recommender. It keeps 80%+ completed\ncatalog listens, weights organic plays more heavily, adds the cutoff-correct\nactive like/dislike state, applies user-length and item-frequency normalization,\nand fits a 192-component truncated SVD. Candidate scores blend the personalized\nlatent reconstruction with training organic popularity. All ineligible tracks\nare masked before top-100 selection.\n\nModel choices were made using train history only against the subsequent public\nwindow. The selected validation ranking scored **0.023853 NDCG@10** (the pure\npopularity baseline scored 0.010577). The final model is then refit on\ntrain+validation events and uses the supplied test-cutoff preference state.\n\nRebuild from `/app` with:\n\n```sh\n/opt/task-venv/bin/python solution/build_ranker.py \\\n --latent --final --components 192 --threshold 80 --organic-bonus 1.5\n```\n\n`validation.parquet` preserves the selected public-window ranking.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8c06acc87d2bfb07de2ccedab4c", "usage": { "input_tokens": 35966, "cached_input_tokens": 35712, "cache_write_input_tokens": 0, "output_tokens": 408, "reasoning_output_tokens": 82, "total_tokens": 36374 }, "turn_token_usage": { "input_tokens": 1288848, "cached_input_tokens": 1232256, "cache_write_input_tokens": 0, "output_tokens": 13070, "reasoning_output_tokens": 3588, "total_tokens": 1301918 }, "thread_token_usage": { "input_tokens": 1288848, "cached_input_tokens": 1232256, "cache_write_input_tokens": 0, "output_tokens": 13070, "reasoning_output_tokens": 3588, "total_tokens": 1301918 } } ### exec · 2026-09-25T01:05:19.761Z { "code": "const r = await tools.exec_command({cmd:\"/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, CandidateSet\\nD='/task/data'; f=pd.read_parquet('solution/submission.parquet'); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); x=validate_ranking(f,targets)\\nz=np.load(D+'/test_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']); p=z['packed_eligible']; r=ui.loc[x.uid].to_numpy(); c=ii.loc[x.item_id].to_numpy(); ok=((p[r,c//8]>>(c%8))&1).astype(bool)\\nprint({'rows':len(x),'users':x.uid.nunique(),'all_eligible':bool(ok.all()),'duplicate_pairs':int(x.duplicated(['uid','item_id']).sum()),'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max()),'bytes':__import__('os').path.getsize('solution/submission.parquet')})\\nPY\\nls -lh solution/submission.parquet solution/validation.parquet solution/build_ranker.py solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T01:05:21.062Z Script completed Wall time 1.3 seconds Output: {'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1646579} -rw-r--r-- 1 agent agent 1.1K Sep 25 01:05 solution/README.md -rw-r--r-- 1 agent agent 7.9K Sep 25 01:04 solution/build_ranker.py -rw-r--r-- 1 agent agent 1.6M Sep 25 01:05 solution/submission.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 01:04 solution/validation.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8c8cf3487d2b519a6efb89ecab6", "usage": { "input_tokens": 36398, "cached_input_tokens": 35200, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 0, "total_tokens": 36732 }, "turn_token_usage": { "input_tokens": 1325246, "cached_input_tokens": 1267456, "cache_write_input_tokens": 0, "output_tokens": 13404, "reasoning_output_tokens": 3588, "total_tokens": 1338650 }, "thread_token_usage": { "input_tokens": 1325246, "cached_input_tokens": 1267456, "cache_write_input_tokens": 0, "output_tokens": 13404, "reasoning_output_tokens": 3588, "total_tokens": 1338650 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e5862fb0-d60a-40ee-bd40-437fda2ae9ba", "process_id": "1293", "command": [ "/bin/sh", "-lc", "/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, CandidateSet\nD='/task/data'; f=pd.read_parquet('solution/submission.parquet'); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); x=validate_ranking(f,targets)\nz=np.load(D+'/test_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']); p=z['packed_eligible']; r=ui.loc[x.uid].to_numpy(); c=ii.loc[x.item_id].to_numpy(); ok=((p[r,c//8]>>(c%8))&1).astype(bool)\nprint({'rows':len(x),'users':x.uid.nunique(),'all_eligible':bool(ok.all()),'duplicate_pairs':int(x.duplicated(['uid','item_id']).sum()),'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max()),'bytes':__import__('os').path.getsize('solution/submission.parquet')})\nPY\nls -lh solution/submission.parquet solution/validation.parquet solution/build_ranker.py solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/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, CandidateSet\nD='/task/data'; f=pd.read_parquet('solution/submission.parquet'); targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); x=validate_ranking(f,targets)\nz=np.load(D+'/test_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']); p=z['packed_eligible']; r=ui.loc[x.uid].to_numpy(); c=ii.loc[x.item_id].to_numpy(); ok=((p[r,c//8]>>(c%8))&1).astype(bool)\nprint({'rows':len(x),'users':x.uid.nunique(),'all_eligible':bool(ok.all()),'duplicate_pairs':int(x.duplicated(['uid','item_id']).sum()),'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max()),'bytes':__import__('os').path.getsize('solution/submission.parquet')})\nPY\nls -lh solution/submission.parquet solution/validation.parquet solution/build_ranker.py solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1646579}\n-rw-r--r-- 1 agent agent 1.1K Sep 25 01:05 solution/README.md\n-rw-r--r-- 1 agent agent 7.9K Sep 25 01:04 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:05 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:04 solution/validation.parquet\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1646579}\n-rw-r--r-- 1 agent agent 1.1K Sep 25 01:05 solution/README.md\n-rw-r--r-- 1 agent agent 7.9K Sep 25 01:04 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:05 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:04 solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 126576568 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'all_eligible': True, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1646579}\n-rw-r--r-- 1 agent agent 1.1K Sep 25 01:05 solution/README.md\n-rw-r--r-- 1 agent agent 7.9K Sep 25 01:04 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:05 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:04 solution/validation.parquet\n" } } ## Step 39 · agent · 2026-09-25T01:05:22.325Z ### Thinking unavailable — no text recorded Built and validated the music-discovery ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Reproducible builder: [build_ranker.py](/app/solution/build_ranker.py) - Documentation: [README.md](/app/solution/README.md) - Selected validation ranking: [validation.parquet](/app/solution/validation.parquet) Public validation NDCG@10: **0.023853**, versus **0.010577** for the popularity baseline. Final validation confirms 845,600 rows, 8,456 users, exactly 100 unique eligible tracks per user, and ranks 1–100. ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "session_id": "01a0d60d-d7ae-7d51-a878-21e6460c44b0", "root_turn_id": "01a0d60d-d7bd-7220-b0ed-b0ef0a1a5b77", "response_id": "resp_0b6ea3dd7c770037016ab5c8d1407887d2805316db4fcb13ac", "usage": { "input_tokens": 36916, "cached_input_tokens": 36224, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 18, "total_tokens": 37081 }, "turn_token_usage": { "input_tokens": 1362162, "cached_input_tokens": 1303680, "cache_write_input_tokens": 0, "output_tokens": 13569, "reasoning_output_tokens": 3606, "total_tokens": 1375731 }, "thread_token_usage": { "input_tokens": 1362162, "cached_input_tokens": 1303680, "cache_write_input_tokens": 0, "output_tokens": 13569, "reasoning_output_tokens": 3606, "total_tokens": 1375731 } }