yambda-discovery-ranking-v01 / base-sol-high-cli-0-153-4-r03-recovery-20260923T212844Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-23T21:41:29.590Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-23T21:41:31.108Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-23T21:41:31.108Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-23T21:41:31.108Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-23T21:41:31.108Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-23 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-23T21:41:31.128Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-23T21:41:32.989Z ### Thinking unavailable — no text recorded I’ll profile the event tables and eligibility structure, then build a leakage-safe validation pipeline, score several ranking blends on the public window, and refit the best approach with validation added to history. I’ll keep a valid fallback submission in `/app/solution/` early, then overwrite it only with verified improvements. ### exec · 2026-09-23T21:41:38.330Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-09-23T21:41:38.400Z Script completed Wall time 0.1 seconds Output: total 340M dr-xr-xr-x 1 root root 4.0K Sep 23 21:36 . dr-xr-xr-x 1 root root 4.0K Sep 23 21:39 .. -r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet -r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet -r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. Python itself # is pinned by task.toml and both Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 idna==3.18 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4478b54c887d2ab2952febb429966", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 24, "total_tokens": 14532 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 24, "total_tokens": 14532 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 24, "total_tokens": 14532 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-effaadf0-10f8-4f5d-bb68-ef5f1b2a1c44", "process_id": "58485", "command": [ "/bin/sh", "-lc", "ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lah /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,200p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,200p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7520 }, "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } ## Step 8 · agent · 2026-09-23T21:41:41.194Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:52.942Z { "code": "const r = await tools.exec_command({\"cmd\":\"mkdir -p /app/solution && python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n t=pq.ParquetFile(p)\\n print(p.name, t.metadata.num_rows, t.schema.names)\\n if p.name in ['train_listens.parquet','val_listens.parquet']:\\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), x.is_organic.mean(), (x.played_ratio_pct>=50).mean(),(x.played_ratio_pct>=80).mean())\\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n x=pd.read_parquet(D/f); print('\\\\n',f,x.shape,x.head(),x.dtypes,sep='\\\\n')\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:41:52.997Z 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": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44792a8f087d2a091af49519f72f0", "usage": { "input_tokens": 18794, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 429, "reasoning_output_tokens": 50, "total_tokens": 19223 }, "turn_token_usage": { "input_tokens": 33131, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 624, "reasoning_output_tokens": 74, "total_tokens": 33755 }, "thread_token_usage": { "input_tokens": 33131, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 624, "reasoning_output_tokens": 74, "total_tokens": 33755 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-463d5962-0ed3-467d-bc7d-fbd929a96f76", "process_id": "78401", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), x.is_organic.mean(), (x.played_ratio_pct>=50).mean(),(x.played_ratio_pct>=80).mean())\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), x.is_organic.mean(), (x.played_ratio_pct>=50).mean(),(x.played_ratio_pct>=80).mean())\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\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": 3210 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } ## Step 9 · agent · 2026-09-23T21:41:54.870Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:57.193Z { "code": "const r = await tools.exec_command({\"cmd\":\"which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:41:57.255Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python /usr/local/bin/python3 total 12 drwxr-xr-x 3 agent agent 4096 Sep 23 21:39 . drwxr-xr-x 1 root root 4096 Sep 23 21:41 .. drwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6072e9f7-d5c1-415e-bd05-9849e8e54f88", "process_id": "59830", "command": [ "/bin/sh", "-lc", "which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4220 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447a163a487d294f221bffccca026", "usage": { "input_tokens": 19279, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 13, "total_tokens": 19374 }, "turn_token_usage": { "input_tokens": 52410, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 719, "reasoning_output_tokens": 87, "total_tokens": 53129 }, "thread_token_usage": { "input_tokens": 52410, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 719, "reasoning_output_tokens": 87, "total_tokens": 53129 } } ## Step 10 · agent · 2026-09-23T21:41:58.898Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:01.884Z { "code": "const r = await tools.exec_command({\"cmd\":\"command -v uv || true; find / -maxdepth 4 -type d \\\\( -name site-packages -o -name dist-packages \\\\) 2>/dev/null | head -30; env | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:42:01.955Z Script completed Wall time 0.1 seconds Output: CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_SESSION_ID=01a0d037-7158-7670-9f20-e7a2602bb363 CODEX_THREAD_ID=01a0d037-7158-7670-9f20-e7a2602bb363 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=d891be4d04cf LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447a5996c87d2869b40f1aa0786c3", "usage": { "input_tokens": 19478, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 11, "total_tokens": 19592 }, "turn_token_usage": { "input_tokens": 71888, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 833, "reasoning_output_tokens": 98, "total_tokens": 72721 }, "thread_token_usage": { "input_tokens": 71888, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 833, "reasoning_output_tokens": 98, "total_tokens": 72721 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-02b3d818-2812-42e0-8619-7c69d05fcedd", "process_id": "72286", "command": [ "/bin/sh", "-lc", "command -v uv || true; find / -maxdepth 4 -type d \\( -name site-packages -o -name dist-packages \\) 2>/dev/null | head -30; env | sort | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v uv || true; find / -maxdepth 4 -type d \\( -name site-packages -o -name dist-packages \\) 2>/dev/null | head -30; env | sort | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "CODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCODEX_THREAD_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=d891be4d04cf\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "stderr": "", "aggregated_output": "CODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCODEX_THREAD_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=d891be4d04cf\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3720 }, "formatted_output": "CODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCODEX_THREAD_ID=01a0d037-7158-7670-9f20-e7a2602bb363\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=d891be4d04cf\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n" } } ## Step 11 · agent · 2026-09-23T21:42:03.911Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:07.069Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type f -path '*/bin/python*' 2>/dev/null | head -50; ls -la /usr/local/lib/python3.12/site-packages | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:42:07.289Z Script completed Wall time 0.2 seconds Output: /usr/local/bin/python3.12 /usr/local/bin/python3.12-config total 20 drwxr-xr-x 4 root root 4096 Aug 5 01:13 . drwxr-xr-x 38 root root 4096 Aug 5 01:13 .. -rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt drwxr-xr-x 5 root root 4096 Aug 5 01:13 pip drwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447aa61d887d2b30e15c9d523e88e", "usage": { "input_tokens": 19917, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 21, "total_tokens": 20024 }, "turn_token_usage": { "input_tokens": 91805, "cached_input_tokens": 82304, "cache_write_input_tokens": 0, "output_tokens": 940, "reasoning_output_tokens": 119, "total_tokens": 92745 }, "thread_token_usage": { "input_tokens": 91805, "cached_input_tokens": 82304, "cache_write_input_tokens": 0, "output_tokens": 940, "reasoning_output_tokens": 119, "total_tokens": 92745 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f135d129-45d7-4bb7-93df-4227afdc7034", "process_id": "16109", "command": [ "/bin/sh", "-lc", "find / -type f -path '*/bin/python*' 2>/dev/null | head -50; ls -la /usr/local/lib/python3.12/site-packages | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type f -path '*/bin/python*' 2>/dev/null | head -50; ls -la /usr/local/lib/python3.12/site-packages | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\ntotal 20\ndrwxr-xr-x 4 root root 4096 Aug 5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug 5 01:13 ..\n-rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt\ndrwxr-xr-x 5 root root 4096 Aug 5 01:13 pip\ndrwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info\n", "stderr": "", "aggregated_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\ntotal 20\ndrwxr-xr-x 4 root root 4096 Aug 5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug 5 01:13 ..\n-rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt\ndrwxr-xr-x 5 root root 4096 Aug 5 01:13 pip\ndrwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 52018407 }, "formatted_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\ntotal 20\ndrwxr-xr-x 4 root root 4096 Aug 5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug 5 01:13 ..\n-rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt\ndrwxr-xr-x 5 root root 4096 Aug 5 01:13 pip\ndrwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info\n" } } ## Step 12 · agent · 2026-09-23T21:42:09.867Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:12.593Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'pyarrow*.so' 2>/dev/null | head; ls -la /task\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:42:12.952Z Script completed Wall time 0.4 seconds Output: /opt/task-venv/lib/python3.12/site-packages/pandas total 36 dr-xr-xr-x 1 root root 4096 Sep 23 21:39 . drwxr-xr-x 1 root root 4096 Sep 23 21:41 .. dr-xr-xr-x 1 root root 4096 Sep 23 21:36 data -r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447af7c1c87d293eff9031033a25b", "usage": { "input_tokens": 20194, "cached_input_tokens": 19712, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 40, "total_tokens": 20328 }, "turn_token_usage": { "input_tokens": 111999, "cached_input_tokens": 102016, "cache_write_input_tokens": 0, "output_tokens": 1074, "reasoning_output_tokens": 159, "total_tokens": 113073 }, "thread_token_usage": { "input_tokens": 111999, "cached_input_tokens": 102016, "cache_write_input_tokens": 0, "output_tokens": 1074, "reasoning_output_tokens": 159, "total_tokens": 113073 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5d1f9c04-4564-41a7-93f5-11419fb0d7e4", "process_id": "32814", "command": [ "/bin/sh", "-lc", "find / -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'pyarrow*.so' 2>/dev/null | head; ls -la /task" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'pyarrow*.so' 2>/dev/null | head; ls -la /task" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 191544790 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n" } } ## Step 13 · agent · 2026-09-23T21:42:14.710Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:17.261Z { "code": "let{output,...rest}=await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv/bin | head -30; /opt/task-venv/bin/python -c 'import pandas; print(pandas.__version__)'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});text(rest);text(output);" } ### Tool output · 2026-09-23T21:42:18.014Z Script completed Wall time 0.8 seconds Output: {"chunk_id":"24e39a","wall_time_seconds":0.545568427,"exit_code":0,"original_token_count":382} total 112 dr-xr-xr-x 1 root root 4096 Sep 23 21:39 . dr-xr-xr-x 1 root root 4096 Sep 23 21:39 .. -r--r--r-- 1 root root 9033 Sep 23 21:39 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 23 21:39 activate -r--r--r-- 1 root root 920 Sep 23 21:39 activate.csh -r--r--r-- 1 root root 2195 Sep 23 21:39 activate.fish -r-xr-xr-x 1 root root 219 Sep 23 21:39 chardetect -r-xr-xr-x 1 root root 221 Sep 23 21:39 dotenv -r-xr-xr-x 1 root root 225 Sep 23 21:39 f2py -r-xr-xr-x 1 root root 216 Sep 23 21:39 idna -r-xr-xr-x 1 root root 222 Sep 23 21:39 jsonschema -r-xr-xr-x 1 root root 228 Sep 23 21:39 jupyter -r-xr-xr-x 1 root root 228 Sep 23 21:39 jupyter-migrate -r-xr-xr-x 1 root root 233 Sep 23 21:39 jupyter-troubleshoot -r-xr-xr-x 1 root root 261 Sep 23 21:39 jupyter-trust -r-xr-xr-x 1 root root 228 Sep 23 21:39 jupytext -r-xr-xr-x 1 root root 232 Sep 23 21:39 jupytext-config -r-xr-xr-x 1 root root 218 Sep 23 21:39 kaggle -r-xr-xr-x 1 root root 229 Sep 23 21:39 markdown-it -r-xr-xr-x 1 root root 242 Sep 23 21:39 normalizer -r-xr-xr-x 1 root root 225 Sep 23 21:39 numpy-config -r-xr-xr-x 1 root root 230 Sep 23 21:39 pip -r-xr-xr-x 1 root root 230 Sep 23 21:39 pip3 -r-xr-xr-x 1 root root 230 Sep 23 21:39 pip3.12 lrwxrwxrwx 1 root root 21 Sep 23 21:39 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 23 21:39 python3 -> python lrwxrwxrwx 1 root root 6 Sep 23 21:39 python3.12 -> python -r-xr-xr-x 1 root root 224 Sep 23 21:39 slugify -r-xr-xr-x 1 root root 216 Sep 23 21:39 tqdm 2.3.3 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447b5202087d2a8fbad453339396d", "usage": { "input_tokens": 20507, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 19, "total_tokens": 20614 }, "turn_token_usage": { "input_tokens": 132506, "cached_input_tokens": 121984, "cache_write_input_tokens": 0, "output_tokens": 1181, "reasoning_output_tokens": 178, "total_tokens": 133687 }, "thread_token_usage": { "input_tokens": 132506, "cached_input_tokens": 121984, "cache_write_input_tokens": 0, "output_tokens": 1181, "reasoning_output_tokens": 178, "total_tokens": 133687 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34e55ae9-8071-473c-94a4-e3515ed51a3b", "process_id": "37909", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv/bin | head -30; /opt/task-venv/bin/python -c 'import pandas; print(pandas.__version__)'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /opt/task-venv/bin | head -30; /opt/task-venv/bin/python -c 'import pandas; print(pandas.__version__)'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3\n", "stderr": "", "aggregated_output": "2.3.3\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 545560787 }, "formatted_output": "2.3.3\n" } } ## Step 14 · agent · 2026-09-23T21:42:19.900Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:31.985Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n t=pq.ParquetFile(p)\\n print(p.name, t.metadata.num_rows, t.schema.names)\\n if p.name in ['train_listens.parquet','val_listens.parquet']:\\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), round(x.is_organic.mean(),3), round((x.played_ratio_pct>=50).mean(),3),round((x.played_ratio_pct>=80).mean(),3))\\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n x=pd.read_parquet(D/f); print('\\\\n',f,x.shape,x.head(),x.dtypes,sep='\\\\n')\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:42:34.435Z Script completed Wall time 2.4 seconds Output: candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet 484776 ['uid', 'item_id', 'state'] train_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] stats 9238 845395 0 24786795 0.517 0.632 0.592 train_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] stats 8196 191747 24788600 25393395 0.517 0.643 0.602 val_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic'] validation_preference_state.parquet 469124 ['uid', 'item_id', 'state'] candidate_catalog.parquet (30000, 4) 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 item_id uint32 positive_listens int64 organic_positive_listens uint64 popularity_rank int32 dtype: object target_users.parquet (8456, 3) uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 uid uint32 recent_positive_listens float64 history_positive_listens int64 dtype: object validation_preference_state.parquet (469124, 3) uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked uid uint32 item_id uint32 state object dtype: object test_preference_state.parquet (484776, 3) uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked uid uint32 item_id uint32 state object dtype: object validation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736] test_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447ba3a2087d29aa4c76cf0732355", "usage": { "input_tokens": 21364, "cached_input_tokens": 20352, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 6, "total_tokens": 21757 }, "turn_token_usage": { "input_tokens": 153870, "cached_input_tokens": 142336, "cache_write_input_tokens": 0, "output_tokens": 1574, "reasoning_output_tokens": 184, "total_tokens": 155444 }, "thread_token_usage": { "input_tokens": 153870, "cached_input_tokens": 142336, "cache_write_input_tokens": 0, "output_tokens": 1574, "reasoning_output_tokens": 184, "total_tokens": 155444 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5bb0c9c2-ba29-4b68-8e5f-11aee5b16074", "process_id": "27873", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), round(x.is_organic.mean(),3), round((x.played_ratio_pct>=50).mean(),3),round((x.played_ratio_pct>=80).mean(),3))\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), round(x.is_organic.mean(),3), round((x.played_ratio_pct>=50).mean(),3),round((x.played_ratio_pct>=80).mean(),3))\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 266041720 }, "formatted_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nuid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nuid uint32\nitem_id uint32\nstate object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n" } } ## Step 15 · agent · 2026-09-23T21:42:41.981Z ### Thinking unavailable — no text recorded The catalog has 30,000 items for 8,456 target users, with roughly 28–30k eligible per user, so the main challenge is ordering rather than sparse eligibility. The history is large (43.5M listens), but the user population is compact enough for collaborative and temporal models. I’m first establishing strong popularity/recency baselines and checking how eligibility treats previously consumed tracks before spending time on personalization. ### exec · 2026-09-23T21:42:54.289Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+\\n+def make(npz, out, order=None):\\n+ z=np.load(D/npz)\\n+ uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\\n+ if order is None: order=np.arange(len(items))\\n+ rows=[]\\n+ for r,u in enumerate(uids):\\n+ bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\\n+ pick=order[bits[order].astype(bool)][:100]\\n+ rows.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\\n+ pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\\n+\\n+make('validation_eligible_candidates.npz',O/'validation_pop.parquet')\\n+make('test_eligible_candidates.npz',O/'submission.parquet')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:42:54.355Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-bef2e667-4e30-4345-9362-0b01d139a442", "changes": { "/app/solution/baseline.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD=Path('/task/data'); O=Path('/app/solution')\n\ndef make(npz, out, order=None):\n z=np.load(D/npz)\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\n if order is None: order=np.arange(len(items))\n rows=[]\n for r,u in enumerate(uids):\n bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n pick=order[bits[order].astype(bool)][:100]\n rows.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\n pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\n\nmake('validation_eligible_candidates.npz',O/'validation_pop.parquet')\nmake('test_eligible_candidates.npz',O/'submission.parquet')\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447ca9fe487d2aeff14a5bb31c80d", "usage": { "input_tokens": 22812, "cached_input_tokens": 21248, "cache_write_input_tokens": 0, "output_tokens": 595, "reasoning_output_tokens": 206, "total_tokens": 23407 }, "turn_token_usage": { "input_tokens": 176682, "cached_input_tokens": 163584, "cache_write_input_tokens": 0, "output_tokens": 2169, "reasoning_output_tokens": 390, "total_tokens": 178851 }, "thread_token_usage": { "input_tokens": 176682, "cached_input_tokens": 163584, "cache_write_input_tokens": 0, "output_tokens": 2169, "reasoning_output_tokens": 390, "total_tokens": 178851 } } ## Step 16 · agent · 2026-09-23T21:42:56.062Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:58.591Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:43:28.768Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab447de9cc087d2adfade1c4a2b6029", "usage": { "input_tokens": 23431, "cached_input_tokens": 22656, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 14, "total_tokens": 23532 }, "turn_token_usage": { "input_tokens": 200113, "cached_input_tokens": 186240, "cache_write_input_tokens": 0, "output_tokens": 2270, "reasoning_output_tokens": 404, "total_tokens": 202383 }, "thread_token_usage": { "input_tokens": 200113, "cached_input_tokens": 186240, "cache_write_input_tokens": 0, "output_tokens": 2270, "reasoning_output_tokens": 404, "total_tokens": 202383 } } ## Step 17 · agent · 2026-09-23T21:43:31.105Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:32.866Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution; ps aux | grep '[p]ython'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:43:32.923Z Script completed Wall time 0.1 seconds Output: total 4.0K -rw-r--r-- 1 agent agent 759 Sep 23 21:42 baseline.py /bin/sh: 1: ps: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8e192b11-b545-4d40-8f20-18be42bce4bc", "process_id": "62527", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution; ps aux | grep '[p]ython'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution; ps aux | grep '[p]ython'" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "total 4.0K\n-rw-r--r-- 1 agent agent 759 Sep 23 21:42 baseline.py\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "total 4.0K\n-rw-r--r-- 1 agent agent 759 Sep 23 21:42 baseline.py\n/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3630 }, "formatted_output": "total 4.0K\n-rw-r--r-- 1 agent agent 759 Sep 23 21:42 baseline.py\n/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44800f84887d28c6922c8d75cd856", "usage": { "input_tokens": 23555, "cached_input_tokens": 23296, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 33, "total_tokens": 23652 }, "turn_token_usage": { "input_tokens": 223668, "cached_input_tokens": 209536, "cache_write_input_tokens": 0, "output_tokens": 2367, "reasoning_output_tokens": 437, "total_tokens": 226035 }, "thread_token_usage": { "input_tokens": 223668, "cached_input_tokens": 209536, "cache_write_input_tokens": 0, "output_tokens": 2367, "reasoning_output_tokens": 437, "total_tokens": 226035 } } ## Step 18 · agent · 2026-09-23T21:43:35.246Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:41.023Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/baseline.py\\n@@\\n- rows=[]\\n+ out_uid=np.repeat(uids,100); out_item=np.empty(len(uids)*100,np.int64)\\n@@\\n- rows.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\\n- pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\\n+ out_item[r*100:(r+1)*100]=items[pick]\\n+ pd.DataFrame({'uid':out_uid,'item_id':out_item,'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet(out,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:43:41.044Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7531fb81-df73-4c13-ba7e-aa6fdd72ae34", "changes": { "/app/solution/baseline.py": { "type": "update", "unified_diff": "@@ -10,3 +10,3 @@\n if order is None: order=np.arange(len(items))\n- rows=[]\n+ out_uid=np.repeat(uids,100); out_item=np.empty(len(uids)*100,np.int64)\n for r,u in enumerate(uids):\n@@ -14,4 +14,4 @@\n pick=order[bits[order].astype(bool)][:100]\n- rows.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\n- pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\n+ out_item[r*100:(r+1)*100]=items[pick]\n+ pd.DataFrame({'uid':out_uid,'item_id':out_item,'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet(out,index=False)\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/baseline.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab448053bac87d294da8b709028036f", "usage": { "input_tokens": 23715, "cached_input_tokens": 23424, "cache_write_input_tokens": 0, "output_tokens": 217, "reasoning_output_tokens": 27, "total_tokens": 23932 }, "turn_token_usage": { "input_tokens": 247383, "cached_input_tokens": 232960, "cache_write_input_tokens": 0, "output_tokens": 2584, "reasoning_output_tokens": 464, "total_tokens": 249967 }, "thread_token_usage": { "input_tokens": 247383, "cached_input_tokens": 232960, "cache_write_input_tokens": 0, "output_tokens": 2584, "reasoning_output_tokens": 464, "total_tokens": 249967 } } ### exec · 2026-09-23T21:43:45.117Z { "code": "// @exec: {\"yield_time_ms\": 30000}\n\nconst r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:44:15.283Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4480d5f1087d292b491e871f10775", "usage": { "input_tokens": 23956, "cached_input_tokens": 23552, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 0, "total_tokens": 24054 }, "turn_token_usage": { "input_tokens": 271339, "cached_input_tokens": 256512, "cache_write_input_tokens": 0, "output_tokens": 2682, "reasoning_output_tokens": 464, "total_tokens": 274021 }, "thread_token_usage": { "input_tokens": 271339, "cached_input_tokens": 256512, "cache_write_input_tokens": 0, "output_tokens": 2682, "reasoning_output_tokens": 464, "total_tokens": 274021 } } ## Step 19 · agent · 2026-09-23T21:44:18.681Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:20.633Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/baseline.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:44:21.804Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"acfec0","wall_time_seconds":1.001822206,"session_id":34315,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4482f78e487d29d53cf055e9b9cb0", "usage": { "input_tokens": 24077, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 111, "reasoning_output_tokens": 47, "total_tokens": 24188 }, "turn_token_usage": { "input_tokens": 295416, "cached_input_tokens": 280320, "cache_write_input_tokens": 0, "output_tokens": 2793, "reasoning_output_tokens": 511, "total_tokens": 298209 }, "thread_token_usage": { "input_tokens": 295416, "cached_input_tokens": 280320, "cache_write_input_tokens": 0, "output_tokens": 2793, "reasoning_output_tokens": 511, "total_tokens": 298209 } } ## Step 20 · agent · 2026-09-23T21:44:23.267Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:24.510Z { "code": "const r = await tools.write_stdin({\"session_id\":34315,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:44:54.519Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"d8cc8a","wall_time_seconds":30.001348205,"session_id":34315,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44836023c87d29bc4056dfa45ddb5", "usage": { "input_tokens": 24245, "cached_input_tokens": 23936, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 24300 }, "turn_token_usage": { "input_tokens": 319661, "cached_input_tokens": 304256, "cache_write_input_tokens": 0, "output_tokens": 2848, "reasoning_output_tokens": 518, "total_tokens": 322509 }, "thread_token_usage": { "input_tokens": 319661, "cached_input_tokens": 304256, "cache_write_input_tokens": 0, "output_tokens": 2848, "reasoning_output_tokens": 518, "total_tokens": 322509 } } ## Step 21 · agent · 2026-09-23T21:44:57.412Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:58.683Z { "code": "const r = await tools.write_stdin({\"session_id\":34315,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:45:28.692Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"b0ed7b","wall_time_seconds":30.000482393,"session_id":34315,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44856eb4087d292f465b0696cf549", "usage": { "input_tokens": 24359, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 32, "total_tokens": 24439 }, "turn_token_usage": { "input_tokens": 344020, "cached_input_tokens": 315392, "cache_write_input_tokens": 0, "output_tokens": 2928, "reasoning_output_tokens": 550, "total_tokens": 346948 }, "thread_token_usage": { "input_tokens": 344020, "cached_input_tokens": 315392, "cache_write_input_tokens": 0, "output_tokens": 2928, "reasoning_output_tokens": 550, "total_tokens": 346948 } } ## Step 22 · agent · 2026-09-23T21:45:32.368Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:33.746Z { "code": "const r = await tools.write_stdin({\"session_id\":34315,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-09-23T21:45:33.814Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "/app/solution/baseline.py", line 18, in make('validation_eligible_candidates.npz',O/'validation_pop.parquet') File "/app/solution/baseline.py", line 13, in make bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little') ~^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1 data = self._decompressor.decompress(data, n) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44878e3f087d2be88412945b19af1", "usage": { "input_tokens": 24498, "cached_input_tokens": 24192, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 81, "total_tokens": 24631 }, "turn_token_usage": { "input_tokens": 368518, "cached_input_tokens": 339584, "cache_write_input_tokens": 0, "output_tokens": 3061, "reasoning_output_tokens": 631, "total_tokens": 371579 }, "thread_token_usage": { "input_tokens": 368518, "cached_input_tokens": 339584, "cache_write_input_tokens": 0, "output_tokens": 3061, "reasoning_output_tokens": 631, "total_tokens": 371579 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b2cecb1d-f2b0-43c8-a0b1-197bb0300cd5", "process_id": "34315", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/baseline.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/baseline.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/baseline.py\", line 18, in \n make('validation_eligible_candidates.npz',O/'validation_pop.parquet')\n File \"/app/solution/baseline.py\", line 13, in make\n bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/baseline.py\", line 18, in \n make('validation_eligible_candidates.npz',O/'validation_pop.parquet')\n File \"/app/solution/baseline.py\", line 13, in make\n bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 73, "nanos": 10378656 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/baseline.py\", line 18, in \n make('validation_eligible_candidates.npz',O/'validation_pop.parquet')\n File \"/app/solution/baseline.py\", line 13, in make\n bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } ## Step 23 · agent · 2026-09-23T21:45:36.608Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:41.223Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/baseline.py\\n@@\\n- uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\\n+ uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\n@@\\n- bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\\n+ bits=np.unpackbits(packed[r],count=len(items),bitorder='little')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:45:41.295Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c2de67b5-0031-489d-a849-0b2e2ab98759", "changes": { "/app/solution/baseline.py": { "type": "update", "unified_diff": "@@ -8,3 +8,3 @@\n z=np.load(D/npz)\n- uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\n+ uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\n if order is None: order=np.arange(len(items))\n@@ -12,3 +12,3 @@\n for r,u in enumerate(uids):\n- bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n+ bits=np.unpackbits(packed[r],count=len(items),bitorder='little')\n pick=order[bits[order].astype(bool)][:100]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/baseline.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4487e157887d2b3d215c4c3d72dc4", "usage": { "input_tokens": 24993, "cached_input_tokens": 24064, "cache_write_input_tokens": 0, "output_tokens": 173, "reasoning_output_tokens": 19, "total_tokens": 25166 }, "turn_token_usage": { "input_tokens": 393511, "cached_input_tokens": 363648, "cache_write_input_tokens": 0, "output_tokens": 3234, "reasoning_output_tokens": 650, "total_tokens": 396745 }, "thread_token_usage": { "input_tokens": 393511, "cached_input_tokens": 363648, "cache_write_input_tokens": 0, "output_tokens": 3234, "reasoning_output_tokens": 650, "total_tokens": 396745 } } ### exec · 2026-09-23T21:45:45.044Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:45:49.748Z Script completed Wall time 4.7 seconds Output: { "model": "/app/solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4488593ac87d294351c37bf744073", "usage": { "input_tokens": 25190, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 0, "total_tokens": 25275 }, "turn_token_usage": { "input_tokens": 418701, "cached_input_tokens": 388480, "cache_write_input_tokens": 0, "output_tokens": 3319, "reasoning_output_tokens": 650, "total_tokens": 422020 }, "thread_token_usage": { "input_tokens": 418701, "cached_input_tokens": 388480, "cache_write_input_tokens": 0, "output_tokens": 3319, "reasoning_output_tokens": 650, "total_tokens": 422020 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c1b24379-3630-4282-bdfc-24c53900beb1", "process_id": "62728", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 534681349 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 24 · agent · 2026-09-23T21:46:04.758Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:46:26.697Z { "code": "const r = await tools.exec_command({\"cmd\":\"cat > /tmp/analyze.py <<'PY'\\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet\\nD='/task/data/'\\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\ntarg=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int)); E=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\\nli=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nrel=rel[rel.uid.isin(targ)]; rel=rel[[E.is_eligible(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\\nrr=np.array([rank[int(i)] for i in rel.item_id]); print('rel',len(rel),rel.uid.nunique(),'likes',sum(rel.g==3)); print('pop pct',np.quantile(rr,[0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1])); print('top rates',[(k,(rr<=k).mean()) for k in [10,100,500,1000,3000,10000,30000]])\\n# Catalog positive historical pairs eligible check and future prior overlap using batch build set only target-related\\nuids=pd.Index(sorted(targ)); items=pd.Index(cat.item_id.astype('int64'))\\npairs=set(); counts=0\\npf=pq.ParquetFile(D+'train_listens.parquet')\\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\\n x=b.to_pandas(); x=x[(x.played_ratio_pct>=50)&x.uid.isin(targ)&x.item_id.isin(items)]\\n pairs.update(zip(x.uid.astype(int),x.item_id.astype(int))); counts+=len(x)\\nprint('hist catalog events/pairs',counts,len(pairs))\\nprint('future rel seen hist',np.mean([(int(u),int(i)) in pairs for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]))\\nss=list(pairs)[:200000];print('hist pairs eligible sample',np.mean([E.is_eligible(u,i) for u,i in ss]))\\nPY\\n/opt/task-venv/bin/python /tmp/analyze.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:46:27.868Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"616e19","wall_time_seconds":1.001725443,"session_id":99913,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4488e059887d2a84ea6a0cc58bcc2", "usage": { "input_tokens": 25344, "cached_input_tokens": 24960, "cache_write_input_tokens": 0, "output_tokens": 1108, "reasoning_output_tokens": 408, "total_tokens": 26452 }, "turn_token_usage": { "input_tokens": 444045, "cached_input_tokens": 413440, "cache_write_input_tokens": 0, "output_tokens": 4427, "reasoning_output_tokens": 1058, "total_tokens": 448472 }, "thread_token_usage": { "input_tokens": 444045, "cached_input_tokens": 413440, "cache_write_input_tokens": 0, "output_tokens": 4427, "reasoning_output_tokens": 1058, "total_tokens": 448472 } } ## Step 25 · agent · 2026-09-23T21:46:29.129Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:46:30.363Z { "code": "const r = await tools.write_stdin({\"session_id\":99913,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:46:40.639Z Script completed Wall time 10.3 seconds Output: rel 37769 4263 likes 4417 pop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04] top rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))] hist catalog events/pairs 19959853 5514505 future rel seen hist 0.0 hist pairs eligible sample 0.0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab448b4127487d292f20d973eaee206", "usage": { "input_tokens": 26509, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 26562 }, "turn_token_usage": { "input_tokens": 470554, "cached_input_tokens": 438656, "cache_write_input_tokens": 0, "output_tokens": 4480, "reasoning_output_tokens": 1064, "total_tokens": 475034 }, "thread_token_usage": { "input_tokens": 470554, "cached_input_tokens": 438656, "cache_write_input_tokens": 0, "output_tokens": 4480, "reasoning_output_tokens": 1064, "total_tokens": 475034 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1fe1e794-cd01-48e5-9feb-39f65bdfdd00", "process_id": "99913", "command": [ "/bin/sh", "-lc", "cat > /tmp/analyze.py <<'PY'\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\ntarg=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int)); E=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\nli=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[rel.uid.isin(targ)]; rel=rel[[E.is_eligible(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nrr=np.array([rank[int(i)] for i in rel.item_id]); print('rel',len(rel),rel.uid.nunique(),'likes',sum(rel.g==3)); print('pop pct',np.quantile(rr,[0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1])); print('top rates',[(k,(rr<=k).mean()) for k in [10,100,500,1000,3000,10000,30000]])\n# Catalog positive historical pairs eligible check and future prior overlap using batch build set only target-related\nuids=pd.Index(sorted(targ)); items=pd.Index(cat.item_id.astype('int64'))\npairs=set(); counts=0\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n x=b.to_pandas(); x=x[(x.played_ratio_pct>=50)&x.uid.isin(targ)&x.item_id.isin(items)]\n pairs.update(zip(x.uid.astype(int),x.item_id.astype(int))); counts+=len(x)\nprint('hist catalog events/pairs',counts,len(pairs))\nprint('future rel seen hist',np.mean([(int(u),int(i)) in pairs for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]))\nss=list(pairs)[:200000];print('hist pairs eligible sample',np.mean([E.is_eligible(u,i) for u,i in ss]))\nPY\n/opt/task-venv/bin/python /tmp/analyze.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /tmp/analyze.py <<'PY'\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\ntarg=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int)); E=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\nli=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[rel.uid.isin(targ)]; rel=rel[[E.is_eligible(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nrr=np.array([rank[int(i)] for i in rel.item_id]); print('rel',len(rel),rel.uid.nunique(),'likes',sum(rel.g==3)); print('pop pct',np.quantile(rr,[0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1])); print('top rates',[(k,(rr<=k).mean()) for k in [10,100,500,1000,3000,10000,30000]])\n# Catalog positive historical pairs eligible check and future prior overlap using batch build set only target-related\nuids=pd.Index(sorted(targ)); items=pd.Index(cat.item_id.astype('int64'))\npairs=set(); counts=0\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n x=b.to_pandas(); x=x[(x.played_ratio_pct>=50)&x.uid.isin(targ)&x.item_id.isin(items)]\n pairs.update(zip(x.uid.astype(int),x.item_id.astype(int))); counts+=len(x)\nprint('hist catalog events/pairs',counts,len(pairs))\nprint('future rel seen hist',np.mean([(int(u),int(i)) in pairs for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]))\nss=list(pairs)[:200000];print('hist pairs eligible sample',np.mean([E.is_eligible(u,i) for u,i in ss]))\nPY\n/opt/task-venv/bin/python /tmp/analyze.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rel 37769 4263 likes 4417\npop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04]\ntop rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))]\nhist catalog events/pairs 19959853 5514505\nfuture rel seen hist 0.0\nhist pairs eligible sample 0.0\n", "stderr": "", "aggregated_output": "rel 37769 4263 likes 4417\npop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04]\ntop rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))]\nhist catalog events/pairs 19959853 5514505\nfuture rel seen hist 0.0\nhist pairs eligible sample 0.0\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 771150998 }, "formatted_output": "rel 37769 4263 likes 4417\npop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04]\ntop rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))]\nhist catalog events/pairs 19959853 5514505\nfuture rel seen hist 0.0\nhist pairs eligible sample 0.0\n" } } ## Step 26 · agent · 2026-09-23T21:46:59.632Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-09-23T21:47:07.764Z ### Thinking unavailable — no text recorded A key structural fact is now confirmed: validation eligibility excludes every user–track pair with a prior positive listen, and none of the validation relevant pairs are historical repeats. That rules out memorization and makes this a true next-item problem. I’m shifting the model toward collaborative taste transfer (what similar listeners discovered) plus recent organic item momentum; explicit state will serve as a taste signal and disliked tracks as negatives. ### exec · 2026-09-23T21:47:48.420Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/experiment.py\\n+from pathlib import Path\\n+import numpy as np, pandas as pd, pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+import sys, time\\n+sys.path.insert(0,'/task')\\n+from ranking_evaluation import ndcg_at_k\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items)\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+iz=pd.Series(np.arange(ni,dtype=np.int32),index=items)\\n+z=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64)\\n+\\n+# labels for rapid local iteration\\n+li=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\n+urow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i in enumerate(items)}\\n+gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=urow.get(int(u)); c=icol.get(int(i))\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\\n+\\n+def evaluate_order(order,name):\\n+ vals=[]\\n+ for u,g in gold.items():\\n+ r=urow[u]; bits=np.unpackbits(packed[r],count=ni,bitorder='little')\\n+ pick=order[bits[order].astype(bool)][:10]\\n+ vals.append(ndcg_at_k(items[pick].tolist(),g,10))\\n+ print(name,round(float(np.mean(vals)),6),flush=True)\\n+\\n+# temporal/global counts\\n+names=[]; arr=[]\\n+for w in [7,14,30,60,90,180,1000]:\\n+ for org in [0,1]:\\n+ names.append(f'p{w}_o{org}'); arr.append(np.zeros(ni,np.float32))\\n+names += ['decay14_org','decay30_org','strong30_org']; arr += [np.zeros(ni,np.float32) for _ in range(3)]\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for b in pf.iter_batches(batch_size=750000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n+ x=b.to_pandas(); pos=x.played_ratio_pct.to_numpy()>=50; ii=iz.reindex(x.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(np.int32); age=(CUT-x.timestamp.to_numpy())/DAY; organic=x.is_organic.to_numpy()==1\\n+ k=0\\n+ for w in [7,14,30,60,90,180,1000]:\\n+ for org in [0,1]:\\n+ m=ok&pos&(age<=w)&(organic if org else True); arr[k]+=np.bincount(ii[m],minlength=ni); k+=1\\n+ for hl,k in [(14,14),(30,15)]:\\n+ m=ok&pos&organic; arr[k]+=np.bincount(ii[m],weights=np.exp(-age[m]/hl),minlength=ni)\\n+ m=ok&organic&(x.played_ratio_pct.to_numpy()>=80)&(age<=30); arr[16]+=np.bincount(ii[m],minlength=ni)\\n+for n,a in zip(names,arr): evaluate_order(np.argsort(-a,kind='stable'),n)\\n+# Likes popularity\\n+likes=pd.read_parquet(D/'train_likes.parquet'); ii=iz.reindex(likes.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(int); age=(CUT-likes.timestamp.to_numpy())/DAY\\n+for w in [7,30,90,1000]:\\n+ for org in [0,1]:\\n+ m=ok&(age<=w)&((likes.is_organic.to_numpy()==1) if org else True); a=np.bincount(ii[m],minlength=ni); evaluate_order(np.argsort(-a,kind='stable'),f'like{w}_o{org}')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:47:48.512Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e77f7f0c-d703-4c6f-b12a-89ebb5be140c", "changes": { "/app/solution/experiment.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np, pandas as pd, pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport sys, time\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import ndcg_at_k\n\nD=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items)\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\niz=pd.Series(np.arange(ni,dtype=np.int32),index=items)\nz=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64)\n\n# labels for rapid local iteration\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nurow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i in enumerate(items)}\ngold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=urow.get(int(u)); c=icol.get(int(i))\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\n\ndef evaluate_order(order,name):\n vals=[]\n for u,g in gold.items():\n r=urow[u]; bits=np.unpackbits(packed[r],count=ni,bitorder='little')\n pick=order[bits[order].astype(bool)][:10]\n vals.append(ndcg_at_k(items[pick].tolist(),g,10))\n print(name,round(float(np.mean(vals)),6),flush=True)\n\n# temporal/global counts\nnames=[]; arr=[]\nfor w in [7,14,30,60,90,180,1000]:\n for org in [0,1]:\n names.append(f'p{w}_o{org}'); arr.append(np.zeros(ni,np.float32))\nnames += ['decay14_org','decay30_org','strong30_org']; arr += [np.zeros(ni,np.float32) for _ in range(3)]\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=750000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); pos=x.played_ratio_pct.to_numpy()>=50; ii=iz.reindex(x.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(np.int32); age=(CUT-x.timestamp.to_numpy())/DAY; organic=x.is_organic.to_numpy()==1\n k=0\n for w in [7,14,30,60,90,180,1000]:\n for org in [0,1]:\n m=ok&pos&(age<=w)&(organic if org else True); arr[k]+=np.bincount(ii[m],minlength=ni); k+=1\n for hl,k in [(14,14),(30,15)]:\n m=ok&pos&organic; arr[k]+=np.bincount(ii[m],weights=np.exp(-age[m]/hl),minlength=ni)\n m=ok&organic&(x.played_ratio_pct.to_numpy()>=80)&(age<=30); arr[16]+=np.bincount(ii[m],minlength=ni)\nfor n,a in zip(names,arr): evaluate_order(np.argsort(-a,kind='stable'),n)\n# Likes popularity\nlikes=pd.read_parquet(D/'train_likes.parquet'); ii=iz.reindex(likes.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(int); age=(CUT-likes.timestamp.to_numpy())/DAY\nfor w in [7,30,90,1000]:\n for org in [0,1]:\n m=ok&(age<=w)&((likes.is_organic.to_numpy()==1) if org else True); a=np.bincount(ii[m],minlength=ni); evaluate_order(np.argsort(-a,kind='stable'),f'like{w}_o{org}')\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/experiment.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab448c0e1b887d29dbe049aebeee703", "usage": { "input_tokens": 26834, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 2039, "reasoning_output_tokens": 767, "total_tokens": 28873 }, "turn_token_usage": { "input_tokens": 497388, "cached_input_tokens": 465024, "cache_write_input_tokens": 0, "output_tokens": 6519, "reasoning_output_tokens": 1831, "total_tokens": 503907 }, "thread_token_usage": { "input_tokens": 497388, "cached_input_tokens": 465024, "cache_write_input_tokens": 0, "output_tokens": 6519, "reasoning_output_tokens": 1831, "total_tokens": 503907 } } ## Step 28 · agent · 2026-09-23T21:47:49.977Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:47:51.695Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:47:52.870Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"40bdd1","wall_time_seconds":1.00145995,"session_id":41244,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44904c30087d289d76da0a0bd0d95", "usage": { "input_tokens": 28897, "cached_input_tokens": 24320, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 6, "total_tokens": 28966 }, "turn_token_usage": { "input_tokens": 526285, "cached_input_tokens": 489344, "cache_write_input_tokens": 0, "output_tokens": 6588, "reasoning_output_tokens": 1837, "total_tokens": 532873 }, "thread_token_usage": { "input_tokens": 526285, "cached_input_tokens": 489344, "cache_write_input_tokens": 0, "output_tokens": 6588, "reasoning_output_tokens": 1837, "total_tokens": 532873 } } ## Step 29 · agent · 2026-09-23T21:47:54.741Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:47:55.958Z { "code": "const r = await tools.write_stdin({\"session_id\":41244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-09-23T21:48:06.955Z Script completed Wall time 11.0 seconds Output: p7_o0 0.030933 p7_o1 0.028456 p14_o0 0.025124 p14_o1 0.026849 p30_o0 0.017182 p30_o1 0.01431 p60_o0 0.016892 p60_o1 0.015967 p90_o0 0.015972 p90_o1 0.015801 p180_o0 0.012116 p180_o1 0.011913 p1000_o0 0.01071 p1000_o1 0.010577 decay14_org 0.023724 decay30_org 0.016688 strong30_org 0.013314 like7_o0 0.030187 like7_o1 0.029297 like30_o0 0.016812 like30_o1 0.018429 like90_o0 0.015093 like90_o1 0.016217 like1000_o0 0.012135 like1000_o1 0.012054 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44909459c87d2948945570f75c5a1", "usage": { "input_tokens": 29023, "cached_input_tokens": 28672, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 29076 }, "turn_token_usage": { "input_tokens": 555308, "cached_input_tokens": 518016, "cache_write_input_tokens": 0, "output_tokens": 6641, "reasoning_output_tokens": 1843, "total_tokens": 561949 }, "thread_token_usage": { "input_tokens": 555308, "cached_input_tokens": 518016, "cache_write_input_tokens": 0, "output_tokens": 6641, "reasoning_output_tokens": 1843, "total_tokens": 561949 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2c137f09-7d92-457f-a0dc-674abe34dc4c", "process_id": "41244", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/experiment.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/experiment.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "p7_o0 0.030933\np7_o1 0.028456\np14_o0 0.025124\np14_o1 0.026849\np30_o0 0.017182\np30_o1 0.01431\np60_o0 0.016892\np60_o1 0.015967\np90_o0 0.015972\np90_o1 0.015801\np180_o0 0.012116\np180_o1 0.011913\np1000_o0 0.01071\np1000_o1 0.010577\ndecay14_org 0.023724\ndecay30_org 0.016688\nstrong30_org 0.013314\nlike7_o0 0.030187\nlike7_o1 0.029297\nlike30_o0 0.016812\nlike30_o1 0.018429\nlike90_o0 0.015093\nlike90_o1 0.016217\nlike1000_o0 0.012135\nlike1000_o1 0.012054\n", "stderr": "", "aggregated_output": "p7_o0 0.030933\np7_o1 0.028456\np14_o0 0.025124\np14_o1 0.026849\np30_o0 0.017182\np30_o1 0.01431\np60_o0 0.016892\np60_o1 0.015967\np90_o0 0.015972\np90_o1 0.015801\np180_o0 0.012116\np180_o1 0.011913\np1000_o0 0.01071\np1000_o1 0.010577\ndecay14_org 0.023724\ndecay30_org 0.016688\nstrong30_org 0.013314\nlike7_o0 0.030187\nlike7_o1 0.029297\nlike30_o0 0.016812\nlike30_o1 0.018429\nlike90_o0 0.015093\nlike90_o1 0.016217\nlike1000_o0 0.012135\nlike1000_o1 0.012054\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 84269156 }, "formatted_output": "p7_o0 0.030933\np7_o1 0.028456\np14_o0 0.025124\np14_o1 0.026849\np30_o0 0.017182\np30_o1 0.01431\np60_o0 0.016892\np60_o1 0.015967\np90_o0 0.015972\np90_o1 0.015801\np180_o0 0.012116\np180_o1 0.011913\np1000_o0 0.01071\np1000_o1 0.010577\ndecay14_org 0.023724\ndecay30_org 0.016688\nstrong30_org 0.013314\nlike7_o0 0.030187\nlike7_o1 0.029297\nlike30_o0 0.016812\nlike30_o1 0.018429\nlike90_o0 0.015093\nlike90_o1 0.016217\nlike1000_o0 0.012135\nlike1000_o1 0.012054\n" } } ## Step 30 · agent · 2026-09-23T21:48:25.357Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-23T21:48:39.668Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:28.411Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/dev_svd.py\\n+from pathlib import Path\\n+import sys,time\\n+import numpy as np,pandas as pd,pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+sys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); item_index=pd.Series(np.arange(ni,dtype=np.int32),index=items)\\n+# all history users\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()])).astype(np.int64)\\n+ui=pd.Series(np.arange(len(users),dtype=np.int32),index=users); targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); target_rows=ui.loc[targets].to_numpy()\\n+rows=[]; cols=[]; vals=[]; rec=[]\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for b in pf.iter_batches(batch_size=1000000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\\n+ x=b.to_pandas(); m=x.played_ratio_pct.to_numpy()>=50; x=x[m]; cc=item_index.reindex(x.item_id).to_numpy(); ok=~pd.isna(cc); x=x[ok]; cc=cc[ok].astype(np.int32)\\n+ rows.append(ui.loc[x.uid].to_numpy(np.int32)); cols.append(cc); vals.append(np.ones(len(x),np.float32)); age=(CUT-x.timestamp.to_numpy())/DAY; rec.append((np.exp(-age/45)*(1+0.35*x.is_organic.to_numpy())).astype(np.float32))\\n+ print('read',sum(map(len,rows)),flush=True)\\n+r=np.concatenate(rows); c=np.concatenate(cols)\\n+X=sparse.coo_matrix((np.concatenate(vals),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); X.data=np.log1p(X.data)\\n+R=sparse.coo_matrix((np.concatenate(rec),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); R.data=np.log1p(R.data)\\n+del rows,cols,vals,rec,r,c\\n+sparse.save_npz(O/'train_catalog_counts.npz',X); sparse.save_npz(O/'train_catalog_recent.npz',R)\\n+# truth and eligibility\\n+z=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64); urow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i in enumerate(items)}\\n+li=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ rr=urow.get(int(u)); cc=icol.get(int(i));\\n+ if rr is not None and cc is not None and ((packed[rr,cc//8]>>(cc%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\\n+# recent 7 popularity used for blend\\n+recent=np.asarray((R>0).sum(axis=0)).ravel().astype(np.float32) # unique listeners recent-weight presence approximation\\n+# exact event pop from rows already unavailable: aggregate R sum\\n+recent_sum=np.asarray(R.sum(axis=0)).ravel().astype(np.float32)\\n+\\n+def score_model(M,label,ncomp=64):\\n+ print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\\n+ # evaluate blends after per-row standardization. pop transformed to z-scale\\n+ pop=np.log1p(recent_sum); pop=(pop-pop.mean())/(pop.std()+1e-6)\\n+ results={a:[] for a in [0,.1,.2,.35,.5,.75,1,1.5,2]}\\n+ pred_by_u={a:{} for a in results}\\n+ for s in range(0,len(targets),256):\\n+ ids=np.arange(s,min(s+256,len(targets))); sc=U[target_rows[ids]]@V\\n+ mu=sc.mean(1,keepdims=True); sd=sc.std(1,keepdims=True)+1e-7; sc=(sc-mu)/sd\\n+ for j,k in enumerate(ids):\\n+ u=int(targets[k]); er=urow[u]; bits=np.unpackbits(packed[er],count=ni,bitorder='little').astype(bool)\\n+ for a in results:\\n+ ss=sc[j]+a*pop; ss[~bits]=-np.inf; top=np.argpartition(ss,-10)[-10:]; top=top[np.argsort(-ss[top])]; pred_by_u[a][u]=items[top].tolist()\\n+ for a in results:\\n+ results[a]=float(np.mean([ndcg_at_k(pred_by_u[a][u],g,10) for u,g in gold.items()]))\\n+ print(label,results,flush=True)\\n+ best=max(results,key=results.get); return sv,best,results[best]\\n+\\n+for M,label in [(X,'count'),(R,'recent')]:\\n+ # normalize rows for taste-focused SVD\\n+ for norm in [False,True]:\\n+ A=M.copy()\\n+ if norm:\\n+ rs=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel())+1e-8; A=sparse.diags(1/rs)@A\\n+ for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:49:28.504Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e12a5d93-d370-4ed3-81dc-8d1e846b911e", "changes": { "/app/solution/dev_svd.py": { "type": "add", "content": "from pathlib import Path\nimport sys,time\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\nD=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); item_index=pd.Series(np.arange(ni,dtype=np.int32),index=items)\n# all history users\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()])).astype(np.int64)\nui=pd.Series(np.arange(len(users),dtype=np.int32),index=users); targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); target_rows=ui.loc[targets].to_numpy()\nrows=[]; cols=[]; vals=[]; rec=[]\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); m=x.played_ratio_pct.to_numpy()>=50; x=x[m]; cc=item_index.reindex(x.item_id).to_numpy(); ok=~pd.isna(cc); x=x[ok]; cc=cc[ok].astype(np.int32)\n rows.append(ui.loc[x.uid].to_numpy(np.int32)); cols.append(cc); vals.append(np.ones(len(x),np.float32)); age=(CUT-x.timestamp.to_numpy())/DAY; rec.append((np.exp(-age/45)*(1+0.35*x.is_organic.to_numpy())).astype(np.float32))\n print('read',sum(map(len,rows)),flush=True)\nr=np.concatenate(rows); c=np.concatenate(cols)\nX=sparse.coo_matrix((np.concatenate(vals),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); X.data=np.log1p(X.data)\nR=sparse.coo_matrix((np.concatenate(rec),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); R.data=np.log1p(R.data)\ndel rows,cols,vals,rec,r,c\nsparse.save_npz(O/'train_catalog_counts.npz',X); sparse.save_npz(O/'train_catalog_recent.npz',R)\n# truth and eligibility\nz=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64); urow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i in enumerate(items)}\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n rr=urow.get(int(u)); cc=icol.get(int(i));\n if rr is not None and cc is not None and ((packed[rr,cc//8]>>(cc%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\n# recent 7 popularity used for blend\nrecent=np.asarray((R>0).sum(axis=0)).ravel().astype(np.float32) # unique listeners recent-weight presence approximation\n# exact event pop from rows already unavailable: aggregate R sum\nrecent_sum=np.asarray(R.sum(axis=0)).ravel().astype(np.float32)\n\ndef score_model(M,label,ncomp=64):\n print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n # evaluate blends after per-row standardization. pop transformed to z-scale\n pop=np.log1p(recent_sum); pop=(pop-pop.mean())/(pop.std()+1e-6)\n results={a:[] for a in [0,.1,.2,.35,.5,.75,1,1.5,2]}\n pred_by_u={a:{} for a in results}\n for s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets))); sc=U[target_rows[ids]]@V\n mu=sc.mean(1,keepdims=True); sd=sc.std(1,keepdims=True)+1e-7; sc=(sc-mu)/sd\n for j,k in enumerate(ids):\n u=int(targets[k]); er=urow[u]; bits=np.unpackbits(packed[er],count=ni,bitorder='little').astype(bool)\n for a in results:\n ss=sc[j]+a*pop; ss[~bits]=-np.inf; top=np.argpartition(ss,-10)[-10:]; top=top[np.argsort(-ss[top])]; pred_by_u[a][u]=items[top].tolist()\n for a in results:\n results[a]=float(np.mean([ndcg_at_k(pred_by_u[a][u],g,10) for u,g in gold.items()]))\n print(label,results,flush=True)\n best=max(results,key=results.get); return sv,best,results[best]\n\nfor M,label in [(X,'count'),(R,'recent')]:\n # normalize rows for taste-focused SVD\n for norm in [False,True]:\n A=M.copy()\n if norm:\n rs=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel())+1e-8; A=sparse.diags(1/rs)@A\n for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/dev_svd.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44917406887d28884c350409c7066", "usage": { "input_tokens": 29352, "cached_input_tokens": 26624, "cache_write_input_tokens": 0, "output_tokens": 2486, "reasoning_output_tokens": 957, "total_tokens": 31838 }, "turn_token_usage": { "input_tokens": 584660, "cached_input_tokens": 544640, "cache_write_input_tokens": 0, "output_tokens": 9127, "reasoning_output_tokens": 2800, "total_tokens": 593787 }, "thread_token_usage": { "input_tokens": 584660, "cached_input_tokens": 544640, "cache_write_input_tokens": 0, "output_tokens": 9127, "reasoning_output_tokens": 2800, "total_tokens": 593787 } } ## Step 32 · agent · 2026-09-23T21:49:30.694Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:32.450Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/dev_svd.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:49:33.668Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c8d8ca","wall_time_seconds":1.001733727,"session_id":46778,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44968ee6487d2a030a015bc86e0b2", "usage": { "input_tokens": 31862, "cached_input_tokens": 29184, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 21, "total_tokens": 31947 }, "turn_token_usage": { "input_tokens": 616522, "cached_input_tokens": 573824, "cache_write_input_tokens": 0, "output_tokens": 9212, "reasoning_output_tokens": 2821, "total_tokens": 625734 }, "thread_token_usage": { "input_tokens": 616522, "cached_input_tokens": 573824, "cache_write_input_tokens": 0, "output_tokens": 9212, "reasoning_output_tokens": 2821, "total_tokens": 625734 } } ## Step 33 · agent · 2026-09-23T21:49:35.537Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:36.269Z { "code": "const r = await tools.write_stdin({\"session_id\":46778,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:50:06.286Z Script completed Wall time 30.0 seconds Output: read 446673 read 908896 read 1340365 read 1785737 read 2234580 read 2709445 read 3185874 read 3652603 read 4090504 read 4527184 read 5004134 read 5470718 read 5944315 read 6418602 read 6909729 read 7394955 read 7832967 read 8248997 read 8723144 read 9196517 read 9674330 read 10155336 read 10631815 read 11064652 read 11522531 read 11970245 read 12443394 read 12909457 read 13330236 read 13800518 read 14274349 read 14730189 read 15204898 read 15667516 read 16133636 read 16586586 read 17040408 read 17502704 read 17981413 read 18436772 read 18903433 read 19367547 read 19850665 read 20056329 fit count 32 variance 0.27032307 count {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345} fit count 64 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4496de5f487d2a797765c44f78b5f", "usage": { "input_tokens": 32006, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 32061 }, "turn_token_usage": { "input_tokens": 648528, "cached_input_tokens": 605568, "cache_write_input_tokens": 0, "output_tokens": 9267, "reasoning_output_tokens": 2829, "total_tokens": 657795 }, "thread_token_usage": { "input_tokens": 648528, "cached_input_tokens": 605568, "cache_write_input_tokens": 0, "output_tokens": 9267, "reasoning_output_tokens": 2829, "total_tokens": 657795 } } ## Step 34 · agent · 2026-09-23T21:50:09.989Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:50:11.167Z { "code": "const r = await tools.write_stdin({\"session_id\":46778,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:50:41.186Z Script completed Wall time 30.0 seconds Output: variance 0.32269448 count {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972} fit count 128 variance 0.38011488 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab4498e8ed487d2b30fdd4ea35ec82a", "usage": { "input_tokens": 32494, "cached_input_tokens": 31872, "cache_write_input_tokens": 0, "output_tokens": 112, "reasoning_output_tokens": 65, "total_tokens": 32606 }, "turn_token_usage": { "input_tokens": 681022, "cached_input_tokens": 637440, "cache_write_input_tokens": 0, "output_tokens": 9379, "reasoning_output_tokens": 2894, "total_tokens": 690401 }, "thread_token_usage": { "input_tokens": 681022, "cached_input_tokens": 637440, "cache_write_input_tokens": 0, "output_tokens": 9379, "reasoning_output_tokens": 2894, "total_tokens": 690401 } } ## Step 35 · agent · 2026-09-23T21:50:44.007Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:50:45.165Z { "code": "const r = await tools.write_stdin({\"session_id\":46778,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:51:15.195Z Script completed Wall time 30.0 seconds Output: count {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557} fit count_norm 32 variance 0.16401763 count_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444} fit count_norm 64 variance 0.2067266 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab449b1a54c87d2adeee4817de140e4", "usage": { "input_tokens": 32780, "cached_input_tokens": 32256, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 32833 }, "turn_token_usage": { "input_tokens": 713802, "cached_input_tokens": 669696, "cache_write_input_tokens": 0, "output_tokens": 9432, "reasoning_output_tokens": 2900, "total_tokens": 723234 }, "thread_token_usage": { "input_tokens": 713802, "cached_input_tokens": 669696, "cache_write_input_tokens": 0, "output_tokens": 9432, "reasoning_output_tokens": 2900, "total_tokens": 723234 } } ## Step 36 · agent · 2026-09-23T21:51:17.701Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:19.068Z { "code": "const r = await tools.write_stdin({\"session_id\":46778,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}); text(r.output);" } ### Tool output · 2026-09-23T21:51:19.401Z Script completed Wall time 0.3 seconds Output: count_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062} fit count_norm 128 Traceback (most recent call last): File "/app/solution/dev_svd.py", line 59, in for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/dev_svd.py", line 36, in score_model print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True) ^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py", line 1365, in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py", line 244, in fit_transform U, Sigma, VT = _randomized_svd( ^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py", line 568, in _randomized_svd Q = _randomized_range_finder( ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py", line 350, in _randomized_range_finder Q, _ = normalizer(A @ Q) ^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py", line 355, in lu lu_dispatcher(a1, u, p, permute_l) KeyboardInterrupt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab449d3b03487d28d939b4f79c7e718", "usage": { "input_tokens": 33145, "cached_input_tokens": 32640, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 31, "total_tokens": 33228 }, "turn_token_usage": { "input_tokens": 746947, "cached_input_tokens": 702336, "cache_write_input_tokens": 0, "output_tokens": 9515, "reasoning_output_tokens": 2931, "total_tokens": 756462 }, "thread_token_usage": { "input_tokens": 746947, "cached_input_tokens": 702336, "cache_write_input_tokens": 0, "output_tokens": 9515, "reasoning_output_tokens": 2931, "total_tokens": 756462 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aee69d87-9a3c-4fa7-b62b-0cd68cb29239", "process_id": "46778", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/dev_svd.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/dev_svd.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n File \"/app/solution/dev_svd.py\", line 59, in \n for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/dev_svd.py\", line 36, in score_model\n print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n U, Sigma, VT = _randomized_svd(\n ^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n Q = _randomized_range_finder(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n Q, _ = normalizer(A @ Q)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n File \"/app/solution/dev_svd.py\", line 59, in \n for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/dev_svd.py\", line 36, in score_model\n print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n U, Sigma, VT = _randomized_svd(\n ^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n Q = _randomized_range_finder(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n Q, _ = normalizer(A @ Q)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 106, "nanos": 732728853 }, "formatted_output": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n File \"/app/solution/dev_svd.py\", line 59, in \n for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/dev_svd.py\", line 36, in score_model\n print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n U, Sigma, VT = _randomized_svd(\n ^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n Q = _randomized_range_finder(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n Q, _ = normalizer(A @ Q)\n ^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n" } } ## Step 37 · agent · 2026-09-23T21:51:31.068Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:06.168Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_global.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\n+sys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data'); CUT=24786800; DAY=86400\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); ix=pd.Series(np.arange(ni),index=items)\\n+z=np.load(D/'validation_eligible_candidates.npz'); us=z['uids'].astype(int); packed=z['packed_eligible']; ur={u:r for r,u in enumerate(us)}; ic={int(i):c for c,i in enumerate(items)}\\n+li=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic']); rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+def ev(score,name):\\n+ order=np.argsort(-score,kind='stable'); v=[]\\n+ for u,g in gold.items():\\n+ b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little'); p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\\n+ q=np.mean(v); print(name,round(q,6));return q\\n+W=[1,2,3,5,7,10,14,21,30]; A={}\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for b in pf.iter_batches(batch_size=500000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n+ x=b.to_pandas(); age=(CUT-x.timestamp.to_numpy())/DAY; jj=ix.reindex(x.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int); org=x.is_organic.to_numpy()==1; ratio=x.played_ratio_pct.to_numpy()\\n+ for w in W:\\n+ for typ,m0 in [('all',ratio>=50),('org',(ratio>=50)&org),('rec',(ratio>=50)&~org),('strong',(ratio>=80)),('skip',ratio<20)]:\\n+ k=f'{typ}{w}';m=ok&m0&(age<=w);A[k]=A.get(k,0)+np.bincount(jj[m],minlength=ni)\\n+likes=pd.read_parquet(D/'train_likes.parquet');age=(CUT-likes.timestamp.to_numpy())/DAY;jj=ix.reindex(likes.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int)\\n+for w in W:A[f'like{w}']=np.bincount(jj[ok&(age<=w)],minlength=ni)\\n+best=(0,None)\\n+for k,a in A.items():\\n+ q=ev(a,k)\\n+ if q>best[0]:best=q,k\\n+# standardized log score blends around best individual horizons\\n+keys=['all3','all5','all7','all10','all14','org3','org5','org7','rec7','like7','like5','strong7']\\n+Z={k:(np.log1p(A[k])-np.log1p(A[k]).mean())/(np.log1p(A[k]).std()+1e-9) for k in keys}\\n+for a in [0,.1,.2,.3,.5,.75,1,1.5,2]:\\n+ for b in [0,.1,.25,.5,1]:\\n+ q=ev(Z['all7']+a*Z['like7']+b*Z['all3'],f'blend like={a} d3={b}')\\n+ if q>best[0]:best=q,('blend',a,b)\\n+print('BEST',best)\\n+np.savez('/app/solution/global_signals.npz',**A)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:52:06.219Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-69ed39a1-c0fa-4b21-a385-5a68016ad448", "changes": { "/app/solution/tune_global.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\nD=Path('/task/data'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); ix=pd.Series(np.arange(ni),index=items)\nz=np.load(D/'validation_eligible_candidates.npz'); us=z['uids'].astype(int); packed=z['packed_eligible']; ur={u:r for r,u in enumerate(us)}; ic={int(i):c for c,i in enumerate(items)}\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic']); rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\ndef ev(score,name):\n order=np.argsort(-score,kind='stable'); v=[]\n for u,g in gold.items():\n b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little'); p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\n q=np.mean(v); print(name,round(q,6));return q\nW=[1,2,3,5,7,10,14,21,30]; A={}\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=500000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); age=(CUT-x.timestamp.to_numpy())/DAY; jj=ix.reindex(x.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int); org=x.is_organic.to_numpy()==1; ratio=x.played_ratio_pct.to_numpy()\n for w in W:\n for typ,m0 in [('all',ratio>=50),('org',(ratio>=50)&org),('rec',(ratio>=50)&~org),('strong',(ratio>=80)),('skip',ratio<20)]:\n k=f'{typ}{w}';m=ok&m0&(age<=w);A[k]=A.get(k,0)+np.bincount(jj[m],minlength=ni)\nlikes=pd.read_parquet(D/'train_likes.parquet');age=(CUT-likes.timestamp.to_numpy())/DAY;jj=ix.reindex(likes.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int)\nfor w in W:A[f'like{w}']=np.bincount(jj[ok&(age<=w)],minlength=ni)\nbest=(0,None)\nfor k,a in A.items():\n q=ev(a,k)\n if q>best[0]:best=q,k\n# standardized log score blends around best individual horizons\nkeys=['all3','all5','all7','all10','all14','org3','org5','org7','rec7','like7','like5','strong7']\nZ={k:(np.log1p(A[k])-np.log1p(A[k]).mean())/(np.log1p(A[k]).std()+1e-9) for k in keys}\nfor a in [0,.1,.2,.3,.5,.75,1,1.5,2]:\n for b in [0,.1,.25,.5,1]:\n q=ev(Z['all7']+a*Z['like7']+b*Z['all3'],f'blend like={a} d3={b}')\n if q>best[0]:best=q,('blend',a,b)\nprint('BEST',best)\nnp.savez('/app/solution/global_signals.npz',**A)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_global.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab449d7a6fc87d2931d36565277093a", "usage": { "input_tokens": 33862, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 1431, "reasoning_output_tokens": 323, "total_tokens": 35293 }, "turn_token_usage": { "input_tokens": 780809, "cached_input_tokens": 731136, "cache_write_input_tokens": 0, "output_tokens": 10946, "reasoning_output_tokens": 3254, "total_tokens": 791755 }, "thread_token_usage": { "input_tokens": 780809, "cached_input_tokens": 731136, "cache_write_input_tokens": 0, "output_tokens": 10946, "reasoning_output_tokens": 3254, "total_tokens": 791755 } } ## Step 38 · agent · 2026-09-23T21:52:08.607Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:09.713Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/tune_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:52:10.882Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"52c053","wall_time_seconds":1.001316981,"session_id":41411,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a06aaf087d2bd186d81a1c0ea0f", "usage": { "input_tokens": 35317, "cached_input_tokens": 33664, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 6, "total_tokens": 35387 }, "turn_token_usage": { "input_tokens": 816126, "cached_input_tokens": 764800, "cache_write_input_tokens": 0, "output_tokens": 11016, "reasoning_output_tokens": 3260, "total_tokens": 827142 }, "thread_token_usage": { "input_tokens": 816126, "cached_input_tokens": 764800, "cache_write_input_tokens": 0, "output_tokens": 11016, "reasoning_output_tokens": 3260, "total_tokens": 827142 } } ## Step 39 · agent · 2026-09-23T21:52:14.053Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:15.471Z { "code": "const r = await tools.write_stdin({\"session_id\":41411,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:52:45.480Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a0b2d5087d2ab361bd73a128690", "usage": { "input_tokens": 35444, "cached_input_tokens": 35200, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 35497 }, "turn_token_usage": { "input_tokens": 851570, "cached_input_tokens": 800000, "cache_write_input_tokens": 0, "output_tokens": 11069, "reasoning_output_tokens": 3266, "total_tokens": 862639 }, "thread_token_usage": { "input_tokens": 851570, "cached_input_tokens": 800000, "cache_write_input_tokens": 0, "output_tokens": 11069, "reasoning_output_tokens": 3266, "total_tokens": 862639 } } ## Step 40 · agent · 2026-09-23T21:52:48.305Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:49.496Z { "code": "const r = await tools.write_stdin({\"session_id\":41411,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:52:51.746Z Script completed Wall time 2.2 seconds Output: all1 0.018312 org1 0.018231 rec1 0.009206 strong1 0.017614 skip1 0.028891 all2 0.02061 org2 0.021367 rec2 0.007519 strong2 0.020396 skip2 0.030657 all3 0.028592 org3 0.026298 rec3 0.011398 strong3 0.023621 skip3 0.031376 all5 0.029501 org5 0.027385 rec5 0.013583 strong5 0.024779 skip5 0.032211 all7 0.030933 org7 0.028456 rec7 0.015199 strong7 0.030107 skip7 0.029497 all10 0.028858 org10 0.027443 rec10 0.01385 strong10 0.026372 skip10 0.029402 all14 0.025124 org14 0.026849 rec14 0.013997 strong14 0.022238 skip14 0.024044 all21 0.020286 org21 0.018416 rec21 0.012978 strong21 0.020034 skip21 0.018828 all30 0.017182 org30 0.01431 rec30 0.010585 strong30 0.016688 skip30 0.016514 like1 0.01253 like2 0.021055 like3 0.030269 like5 0.025373 like7 0.030187 like10 0.030801 like14 0.020652 like21 0.016229 like30 0.016812 blend like=0 d3=0 0.030933 blend like=0 d3=0.1 0.030575 blend like=0 d3=0.25 0.030366 blend like=0 d3=0.5 0.030484 blend like=0 d3=1 0.029623 blend like=0.1 d3=0 0.033046 blend like=0.1 d3=0.1 0.032577 blend like=0.1 d3=0.25 0.032492 blend like=0.1 d3=0.5 0.032056 blend like=0.1 d3=1 0.031225 blend like=0.2 d3=0 0.032908 blend like=0.2 d3=0.1 0.033074 blend like=0.2 d3=0.25 0.032657 blend like=0.2 d3=0.5 0.032726 blend like=0.2 d3=1 0.032333 blend like=0.3 d3=0 0.032865 blend like=0.3 d3=0.1 0.032945 blend like=0.3 d3=0.25 0.032885 blend like=0.3 d3=0.5 0.032684 blend like=0.3 d3=1 0.032552 blend like=0.5 d3=0 0.032932 blend like=0.5 d3=0.1 0.032885 blend like=0.5 d3=0.25 0.032944 blend like=0.5 d3=0.5 0.032808 blend like=0.5 d3=1 0.032615 blend like=0.75 d3=0 0.032799 blend like=0.75 d3=0.1 0.033184 blend like=0.75 d3=0.25 0.033041 blend like=0.75 d3=0.5 0.032874 blend like=0.75 d3=1 0.032515 blend like=1 d3=0 0.031725 blend like=1 d3=0.1 0.031826 blend like=1 d3=0.25 0.032763 blend like=1 d3=0.5 0.033059 blend like=1 d3=1 0.032846 blend like=1.5 d3=0 0.031162 blend like=1.5 d3=0.1 0.031906 blend like=1.5 d3=0.25 0.031944 blend like=1.5 d3=0.5 0.032383 blend like=1.5 d3=1 0.033141 blend like=2 d3=0 0.031119 blend like=2 d3=0.1 0.031058 blend like=2 d3=0.25 0.031617 blend like=2 d3=0.5 0.032015 blend like=2 d3=1 0.032392 BEST (np.float64(0.033184193148729016), ('blend', 0.75, 0.1)) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a2ddd2487d29589e7488bda2306", "usage": { "input_tokens": 35520, "cached_input_tokens": 35328, "cache_write_input_tokens": 0, "output_tokens": 67, "reasoning_output_tokens": 20, "total_tokens": 35587 }, "turn_token_usage": { "input_tokens": 887090, "cached_input_tokens": 835328, "cache_write_input_tokens": 0, "output_tokens": 11136, "reasoning_output_tokens": 3286, "total_tokens": 898226 }, "thread_token_usage": { "input_tokens": 887090, "cached_input_tokens": 835328, "cache_write_input_tokens": 0, "output_tokens": 11136, "reasoning_output_tokens": 3286, "total_tokens": 898226 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d623b06c-b220-4ddc-a440-49199f6d5c2d", "process_id": "41411", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/tune_global.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/tune_global.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "all1 0.018312\norg1 0.018231\nrec1 0.009206\nstrong1 0.017614\nskip1 0.028891\nall2 0.02061\norg2 0.021367\nrec2 0.007519\nstrong2 0.020396\nskip2 0.030657\nall3 0.028592\norg3 0.026298\nrec3 0.011398\nstrong3 0.023621\nskip3 0.031376\nall5 0.029501\norg5 0.027385\nrec5 0.013583\nstrong5 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d3=0.5 0.032726\nblend like=0.2 d3=1 0.032333\nblend like=0.3 d3=0 0.032865\nblend like=0.3 d3=0.1 0.032945\nblend like=0.3 d3=0.25 0.032885\nblend like=0.3 d3=0.5 0.032684\nblend like=0.3 d3=1 0.032552\nblend like=0.5 d3=0 0.032932\nblend like=0.5 d3=0.1 0.032885\nblend like=0.5 d3=0.25 0.032944\nblend like=0.5 d3=0.5 0.032808\nblend like=0.5 d3=1 0.032615\nblend like=0.75 d3=0 0.032799\nblend like=0.75 d3=0.1 0.033184\nblend like=0.75 d3=0.25 0.033041\nblend like=0.75 d3=0.5 0.032874\nblend like=0.75 d3=1 0.032515\nblend like=1 d3=0 0.031725\nblend like=1 d3=0.1 0.031826\nblend like=1 d3=0.25 0.032763\nblend like=1 d3=0.5 0.033059\nblend like=1 d3=1 0.032846\nblend like=1.5 d3=0 0.031162\nblend like=1.5 d3=0.1 0.031906\nblend like=1.5 d3=0.25 0.031944\nblend like=1.5 d3=0.5 0.032383\nblend like=1.5 d3=1 0.033141\nblend like=2 d3=0 0.031119\nblend like=2 d3=0.1 0.031058\nblend like=2 d3=0.25 0.031617\nblend like=2 d3=0.5 0.032015\nblend like=2 d3=1 0.032392\nBEST (np.float64(0.033184193148729016), ('blend', 0.75, 0.1))\n", "stderr": "", "aggregated_output": "all1 0.018312\norg1 0.018231\nrec1 0.009206\nstrong1 0.017614\nskip1 0.028891\nall2 0.02061\norg2 0.021367\nrec2 0.007519\nstrong2 0.020396\nskip2 0.030657\nall3 0.028592\norg3 0.026298\nrec3 0.011398\nstrong3 0.023621\nskip3 0.031376\nall5 0.029501\norg5 0.027385\nrec5 0.013583\nstrong5 0.024779\nskip5 0.032211\nall7 0.030933\norg7 0.028456\nrec7 0.015199\nstrong7 0.030107\nskip7 0.029497\nall10 0.028858\norg10 0.027443\nrec10 0.01385\nstrong10 0.026372\nskip10 0.029402\nall14 0.025124\norg14 0.026849\nrec14 0.013997\nstrong14 0.022238\nskip14 0.024044\nall21 0.020286\norg21 0.018416\nrec21 0.012978\nstrong21 0.020034\nskip21 0.018828\nall30 0.017182\norg30 0.01431\nrec30 0.010585\nstrong30 0.016688\nskip30 0.016514\nlike1 0.01253\nlike2 0.021055\nlike3 0.030269\nlike5 0.025373\nlike7 0.030187\nlike10 0.030801\nlike14 0.020652\nlike21 0.016229\nlike30 0.016812\nblend like=0 d3=0 0.030933\nblend like=0 d3=0.1 0.030575\nblend like=0 d3=0.25 0.030366\nblend like=0 d3=0.5 0.030484\nblend like=0 d3=1 0.029623\nblend like=0.1 d3=0 0.033046\nblend like=0.1 d3=0.1 0.032577\nblend like=0.1 d3=0.25 0.032492\nblend like=0.1 d3=0.5 0.032056\nblend like=0.1 d3=1 0.031225\nblend like=0.2 d3=0 0.032908\nblend like=0.2 d3=0.1 0.033074\nblend like=0.2 d3=0.25 0.032657\nblend like=0.2 d3=0.5 0.032726\nblend like=0.2 d3=1 0.032333\nblend like=0.3 d3=0 0.032865\nblend like=0.3 d3=0.1 0.032945\nblend like=0.3 d3=0.25 0.032885\nblend like=0.3 d3=0.5 0.032684\nblend like=0.3 d3=1 0.032552\nblend like=0.5 d3=0 0.032932\nblend like=0.5 d3=0.1 0.032885\nblend like=0.5 d3=0.25 0.032944\nblend like=0.5 d3=0.5 0.032808\nblend like=0.5 d3=1 0.032615\nblend like=0.75 d3=0 0.032799\nblend like=0.75 d3=0.1 0.033184\nblend like=0.75 d3=0.25 0.033041\nblend like=0.75 d3=0.5 0.032874\nblend like=0.75 d3=1 0.032515\nblend like=1 d3=0 0.031725\nblend like=1 d3=0.1 0.031826\nblend like=1 d3=0.25 0.032763\nblend like=1 d3=0.5 0.033059\nblend like=1 d3=1 0.032846\nblend like=1.5 d3=0 0.031162\nblend like=1.5 d3=0.1 0.031906\nblend like=1.5 d3=0.25 0.031944\nblend like=1.5 d3=0.5 0.032383\nblend like=1.5 d3=1 0.033141\nblend like=2 d3=0 0.031119\nblend like=2 d3=0.1 0.031058\nblend like=2 d3=0.25 0.031617\nblend like=2 d3=0.5 0.032015\nblend like=2 d3=1 0.032392\nBEST (np.float64(0.033184193148729016), ('blend', 0.75, 0.1))\n", "exit_code": 0, "duration": { "secs": 41, "nanos": 863984680 }, "formatted_output": "all1 0.018312\norg1 0.018231\nrec1 0.009206\nstrong1 0.017614\nskip1 0.028891\nall2 0.02061\norg2 0.021367\nrec2 0.007519\nstrong2 0.020396\nskip2 0.030657\nall3 0.028592\norg3 0.026298\nrec3 0.011398\nstrong3 0.023621\nskip3 0.031376\nall5 0.029501\norg5 0.027385\nrec5 0.013583\nstrong5 0.024779\nskip5 0.032211\nall7 0.030933\norg7 0.028456\nrec7 0.015199\nstrong7 0.030107\nskip7 0.029497\nall10 0.028858\norg10 0.027443\nrec10 0.01385\nstrong10 0.026372\nskip10 0.029402\nall14 0.025124\norg14 0.026849\nrec14 0.013997\nstrong14 0.022238\nskip14 0.024044\nall21 0.020286\norg21 0.018416\nrec21 0.012978\nstrong21 0.020034\nskip21 0.018828\nall30 0.017182\norg30 0.01431\nrec30 0.010585\nstrong30 0.016688\nskip30 0.016514\nlike1 0.01253\nlike2 0.021055\nlike3 0.030269\nlike5 0.025373\nlike7 0.030187\nlike10 0.030801\nlike14 0.020652\nlike21 0.016229\nlike30 0.016812\nblend like=0 d3=0 0.030933\nblend like=0 d3=0.1 0.030575\nblend like=0 d3=0.25 0.030366\nblend like=0 d3=0.5 0.030484\nblend like=0 d3=1 0.029623\nblend like=0.1 d3=0 0.033046\nblend like=0.1 d3=0.1 0.032577\nblend like=0.1 d3=0.25 0.032492\nblend like=0.1 d3=0.5 0.032056\nblend like=0.1 d3=1 0.031225\nblend like=0.2 d3=0 0.032908\nblend like=0.2 d3=0.1 0.033074\nblend like=0.2 d3=0.25 0.032657\nblend like=0.2 d3=0.5 0.032726\nblend like=0.2 d3=1 0.032333\nblend like=0.3 d3=0 0.032865\nblend like=0.3 d3=0.1 0.032945\nblend like=0.3 d3=0.25 0.032885\nblend like=0.3 d3=0.5 0.032684\nblend like=0.3 d3=1 0.032552\nblend like=0.5 d3=0 0.032932\nblend like=0.5 d3=0.1 0.032885\nblend like=0.5 d3=0.25 0.032944\nblend like=0.5 d3=0.5 0.032808\nblend like=0.5 d3=1 0.032615\nblend like=0.75 d3=0 0.032799\nblend like=0.75 d3=0.1 0.033184\nblend like=0.75 d3=0.25 0.033041\nblend like=0.75 d3=0.5 0.032874\nblend like=0.75 d3=1 0.032515\nblend like=1 d3=0 0.031725\nblend like=1 d3=0.1 0.031826\nblend like=1 d3=0.25 0.032763\nblend like=1 d3=0.5 0.033059\nblend like=1 d3=1 0.032846\nblend like=1.5 d3=0 0.031162\nblend like=1.5 d3=0.1 0.031906\nblend like=1.5 d3=0.25 0.031944\nblend like=1.5 d3=0.5 0.032383\nblend like=1.5 d3=1 0.033141\nblend like=2 d3=0 0.031119\nblend like=2 d3=0.1 0.031058\nblend like=2 d3=0.25 0.031617\nblend like=2 d3=0.5 0.032015\nblend like=2 d3=1 0.032392\nBEST (np.float64(0.033184193148729016), ('blend', 0.75, 0.1))\n" } } ## Step 41 · agent · 2026-09-23T21:53:10.549Z ### Thinking unavailable — no text recorded ## Step 42 · agent · 2026-09-23T21:53:18.059Z ### Thinking unavailable — no text recorded The strongest non-personalized signal is very short-term momentum: seven-day positive listens, lightly blended with recent likes, reaches public NDCG@10 ≈ 0.0332 versus 0.0107 for lifetime popularity. Interestingly, recent skip volume is also predictive—not as preference, but as an exposure/momentum proxy. I’m now testing whether latent taste factors add value on top of that already strong temporal baseline, rather than evaluating personalization in isolation. ### exec · 2026-09-23T21:54:01.656Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/hybrid_dev.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+sys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data');O=Path('/app/solution')\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)}\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+# user ordering matches sorted all train users in dev_svd\\n+import pyarrow.parquet as pq\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\\n+X=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32)\\n+# strengthen explicit current likes; disliked state is omitted from factor fitting\\n+pref=pd.read_parquet(D/'validation_preference_state.parquet'); pref=pref[pref.state.eq('liked')]; rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc); L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\\n+signals=np.load(O/'global_signals.npz');Z={k:(np.log1p(signals[k])-np.log1p(signals[k]).mean())/(np.log1p(signals[k]).std()+1e-9) for k in signals.files}\\n+# eligibility + truth\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];eus=z['uids'].astype(int);ur={u:r for r,u in enumerate(eus)}\\n+li=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+\\n+def eval_global(sc,name):\\n+ order=np.argsort(-sc);v=[]\\n+ for u,g in gold.items():\\n+ b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little');p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\\n+ print(name,np.mean(v),flush=True)\\n+\\n+base=Z['all7']+.75*Z['like7']+.1*Z['all3']\\n+for k in ['skip3','skip5','skip7','org7','strong7','all5','all10']:\\n+ for w in [-.5,-.25,-.1,.1,.25,.5,1]:eval_global(base+w*Z[k],f'base {k} {w}')\\n+\\n+for lw in [0,1,3,6]:\\n+ A=X+lw*L\\n+ # row-normalizing degrades earlier; use raw confidence\\n+ print('FIT likes',lw,flush=True);sv=TruncatedSVD(n_components=128,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32)\\n+ alphas=[0,.025,.05,.075,.1,.15,.2,.3,.5,1]\\n+ vals={a:[] for a in alphas}; ranked={a:{} for a in alphas}\\n+ for s in range(0,len(targets),256):\\n+ ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\\n+ for j,k0 in enumerate(ids):\\n+ u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\\n+ for a in alphas:\\n+ ss=base+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];ranked[a][u]=items[top].tolist()\\n+ for a in alphas: vals[a]=np.mean([ndcg_at_k(ranked[a][u],g,10) for u,g in gold.items()])\\n+ print('HYBRID',lw,vals,flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:54:01.754Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1985049d-49c0-4dc7-a51b-7dd4366695eb", "changes": { "/app/solution/hybrid_dev.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)}\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n# user ordering matches sorted all train users in dev_svd\nimport pyarrow.parquet as pq\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32)\n# strengthen explicit current likes; disliked state is omitted from factor fitting\npref=pd.read_parquet(D/'validation_preference_state.parquet'); pref=pref[pref.state.eq('liked')]; rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc); L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsignals=np.load(O/'global_signals.npz');Z={k:(np.log1p(signals[k])-np.log1p(signals[k]).mean())/(np.log1p(signals[k]).std()+1e-9) for k in signals.files}\n# eligibility + truth\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];eus=z['uids'].astype(int);ur={u:r for r,u in enumerate(eus)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\n\ndef eval_global(sc,name):\n order=np.argsort(-sc);v=[]\n for u,g in gold.items():\n b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little');p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\n print(name,np.mean(v),flush=True)\n\nbase=Z['all7']+.75*Z['like7']+.1*Z['all3']\nfor k in ['skip3','skip5','skip7','org7','strong7','all5','all10']:\n for w in [-.5,-.25,-.1,.1,.25,.5,1]:eval_global(base+w*Z[k],f'base {k} {w}')\n\nfor lw in [0,1,3,6]:\n A=X+lw*L\n # row-normalizing degrades earlier; use raw confidence\n print('FIT likes',lw,flush=True);sv=TruncatedSVD(n_components=128,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32)\n alphas=[0,.025,.05,.075,.1,.15,.2,.3,.5,1]\n vals={a:[] for a in alphas}; ranked={a:{} for a in alphas}\n for s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for a in alphas:\n ss=base+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];ranked[a][u]=items[top].tolist()\n for a in alphas: vals[a]=np.mean([ndcg_at_k(ranked[a][u],g,10) for u,g in gold.items()])\n print('HYBRID',lw,vals,flush=True)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/hybrid_dev.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a33eb3887d293ad1960f38fe56d", "usage": { "input_tokens": 36814, "cached_input_tokens": 33024, "cache_write_input_tokens": 0, "output_tokens": 2094, "reasoning_output_tokens": 750, "total_tokens": 38908 }, "turn_token_usage": { "input_tokens": 923904, "cached_input_tokens": 868352, "cache_write_input_tokens": 0, "output_tokens": 13230, "reasoning_output_tokens": 4036, "total_tokens": 937134 }, "thread_token_usage": { "input_tokens": 923904, "cached_input_tokens": 868352, "cache_write_input_tokens": 0, "output_tokens": 13230, "reasoning_output_tokens": 4036, "total_tokens": 937134 } } ## Step 43 · agent · 2026-09-23T21:54:03.552Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:05.349Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/hybrid_dev.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:54:06.516Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c6a40e","wall_time_seconds":1.001460532,"session_id":31306,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a7a32c087d290de1fa6c47691c5", "usage": { "input_tokens": 38932, "cached_input_tokens": 36608, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 6, "total_tokens": 39002 }, "turn_token_usage": { "input_tokens": 962836, "cached_input_tokens": 904960, "cache_write_input_tokens": 0, "output_tokens": 13300, "reasoning_output_tokens": 4042, "total_tokens": 976136 }, "thread_token_usage": { "input_tokens": 962836, "cached_input_tokens": 904960, "cache_write_input_tokens": 0, "output_tokens": 13300, "reasoning_output_tokens": 4042, "total_tokens": 976136 } } ## Step 44 · agent · 2026-09-23T21:54:08.259Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:09.532Z { "code": "const r = await tools.write_stdin({\"session_id\":31306,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:54:39.570Z Script completed Wall time 30.0 seconds Output: base skip3 -0.5 0.029946779281351112 base skip3 -0.25 0.03126644382123068 base skip3 -0.1 0.03190438624519286 base skip3 0.1 0.03315521543325755 base skip3 0.25 0.032995691816150745 base skip3 0.5 0.033010758087615506 base skip3 1 0.03286394776193453 base skip5 -0.5 0.030548146102484734 base skip5 -0.25 0.03149592280885828 base skip5 -0.1 0.03229907625191908 base skip5 0.1 0.03307387227747661 base skip5 0.25 0.033032604521235145 base skip5 0.5 0.03295122741581933 base skip5 1 0.03282146617257311 base skip7 -0.5 0.03067223190540012 base skip7 -0.25 0.0321525812772168 base skip7 -0.1 0.03252061069716571 base skip7 0.1 0.03312741468674745 base skip7 0.25 0.03299343374046417 base skip7 0.5 0.03279525530657301 base skip7 1 0.03266542940289131 base org7 -0.5 0.03205272010389338 base org7 -0.25 0.03300502711337684 base org7 -0.1 0.03313037428098415 base org7 0.1 0.032816139469484716 base org7 0.25 0.032639072505816456 base org7 0.5 0.03229999075222186 base org7 1 0.03175319000106286 base strong7 -0.5 0.0316744178426837 base strong7 -0.25 0.03197551953278427 base strong7 -0.1 0.03285695705168735 base strong7 0.1 0.0329429937264542 base strong7 0.25 0.03280316860644252 base strong7 0.5 0.032864072768996874 base strong7 1 0.0328351140783166 base all5 -0.5 0.030896843458784698 base all5 -0.25 0.03144107091612027 base all5 -0.1 0.03249522919707681 base all5 0.1 0.03296405754725993 base all5 0.25 0.032936905522787635 base all5 0.5 0.03292526126860776 base all5 1 0.03281140685975686 base all10 -0.5 0.030585729365031615 base all10 -0.25 0.03134656975107349 base all10 -0.1 0.03237540406464214 base all10 0.1 0.0330652505316981 base all10 0.25 0.03295460588424532 base all10 0.5 0.033073852997783225 base all10 1 0.033313012636909126 FIT likes 0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a7ec25087d29296946bf0eba2ed", "usage": { "input_tokens": 39061, "cached_input_tokens": 38784, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 39114 }, "turn_token_usage": { "input_tokens": 1001897, "cached_input_tokens": 943744, "cache_write_input_tokens": 0, "output_tokens": 13353, "reasoning_output_tokens": 4048, "total_tokens": 1015250 }, "thread_token_usage": { "input_tokens": 1001897, "cached_input_tokens": 943744, "cache_write_input_tokens": 0, "output_tokens": 13353, "reasoning_output_tokens": 4048, "total_tokens": 1015250 } } ## Step 45 · agent · 2026-09-23T21:54:41.996Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:43.219Z { "code": "const r = await tools.write_stdin({\"session_id\":31306,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:55:13.228Z Script completed Wall time 30.0 seconds Output: HYBRID 0 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03373753337192489), 0.05: np.float64(0.03441873844184196), 0.075: np.float64(0.03488859984165817), 0.1: np.float64(0.03556748248959376), 0.15: np.float64(0.03665253520941666), 0.2: np.float64(0.03771278342304864), 0.3: np.float64(0.03872116805229546), 0.5: np.float64(0.037856284128177695), 1: np.float64(0.03027163987803039)} FIT likes 1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44a9ffadc87d29cd95e32f74e501d", "usage": { "input_tokens": 39961, "cached_input_tokens": 38912, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 29, "total_tokens": 40037 }, "turn_token_usage": { "input_tokens": 1041858, "cached_input_tokens": 982656, "cache_write_input_tokens": 0, "output_tokens": 13429, "reasoning_output_tokens": 4077, "total_tokens": 1055287 }, "thread_token_usage": { "input_tokens": 1041858, "cached_input_tokens": 982656, "cache_write_input_tokens": 0, "output_tokens": 13429, "reasoning_output_tokens": 4077, "total_tokens": 1055287 } } ## Step 46 · agent · 2026-09-23T21:55:15.570Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:55:16.769Z { "code": "const r = await tools.write_stdin({\"session_id\":31306,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:55:46.786Z Script completed Wall time 30.0 seconds Output: HYBRID 1 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.033750137858625934), 0.05: np.float64(0.03450076058465348), 0.075: np.float64(0.03535947620299418), 0.1: np.float64(0.03573640876159942), 0.15: np.float64(0.03733431409438778), 0.2: np.float64(0.038318194226979484), 0.3: np.float64(0.03884630768990876), 0.5: np.float64(0.038268314571897756), 1: np.float64(0.03069837978192861)} FIT likes 3 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44ac1897887d2a41c9c9aba9dbf74", "usage": { "input_tokens": 40246, "cached_input_tokens": 39808, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 16, "total_tokens": 40309 }, "turn_token_usage": { "input_tokens": 1082104, "cached_input_tokens": 1022464, "cache_write_input_tokens": 0, "output_tokens": 13492, "reasoning_output_tokens": 4093, "total_tokens": 1095596 }, "thread_token_usage": { "input_tokens": 1082104, "cached_input_tokens": 1022464, "cache_write_input_tokens": 0, "output_tokens": 13492, "reasoning_output_tokens": 4093, "total_tokens": 1095596 } } ## Step 47 · agent · 2026-09-23T21:55:51.668Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:55:51.669Z { "code": "const r = await tools.write_stdin({\"session_id\":31306,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:56:21.679Z Script completed Wall time 30.0 seconds Output: HYBRID 3 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03363019503776941), 0.05: np.float64(0.03424765995512439), 0.075: np.float64(0.03489279634823226), 0.1: np.float64(0.03584731525947013), 0.15: np.float64(0.0375129802329693), 0.2: np.float64(0.03810286144051453), 0.3: np.float64(0.03868415063515547), 0.5: np.float64(0.038082746180387726), 1: np.float64(0.031281630759976115)} FIT likes 6 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44ae3132887d2b8e00e2de6abb301", "usage": { "input_tokens": 40518, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 40575 }, "turn_token_usage": { "input_tokens": 1122622, "cached_input_tokens": 1062528, "cache_write_input_tokens": 0, "output_tokens": 13549, "reasoning_output_tokens": 4103, "total_tokens": 1136171 }, "thread_token_usage": { "input_tokens": 1122622, "cached_input_tokens": 1062528, "cache_write_input_tokens": 0, "output_tokens": 13549, "reasoning_output_tokens": 4103, "total_tokens": 1136171 } } ## Step 48 · agent · 2026-09-23T21:56:23.773Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:56:24.942Z { "code": "const r = await tools.write_stdin({\"session_id\":31306,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-09-23T21:56:27.953Z Script completed Wall time 3.0 seconds Output: HYBRID 6 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03360268449095774), 0.05: np.float64(0.034145985720123645), 0.075: np.float64(0.035121704925288454), 0.1: np.float64(0.03612894879082033), 0.15: np.float64(0.037548126586520535), 0.2: np.float64(0.038080153808612294), 0.3: np.float64(0.03879245566063955), 0.5: np.float64(0.03741409128350689), 1: np.float64(0.030652308034569007)} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b05dfa087d2be8c7e8060932e2b", "usage": { "input_tokens": 40784, "cached_input_tokens": 40320, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 40845 }, "turn_token_usage": { "input_tokens": 1163406, "cached_input_tokens": 1102848, "cache_write_input_tokens": 0, "output_tokens": 13610, "reasoning_output_tokens": 4117, "total_tokens": 1177016 }, "thread_token_usage": { "input_tokens": 1163406, "cached_input_tokens": 1102848, "cache_write_input_tokens": 0, "output_tokens": 13610, "reasoning_output_tokens": 4117, "total_tokens": 1177016 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2b4619c7-60a4-4a46-a9f7-fb0a8522b49a", "process_id": "31306", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/hybrid_dev.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/hybrid_dev.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base skip3 -0.5 0.029946779281351112\nbase skip3 -0.25 0.03126644382123068\nbase skip3 -0.1 0.03190438624519286\nbase skip3 0.1 0.03315521543325755\nbase skip3 0.25 0.032995691816150745\nbase skip3 0.5 0.033010758087615506\nbase skip3 1 0.03286394776193453\nbase skip5 -0.5 0.030548146102484734\nbase skip5 -0.25 0.03149592280885828\nbase skip5 -0.1 0.03229907625191908\nbase skip5 0.1 0.03307387227747661\nbase skip5 0.25 0.033032604521235145\nbase skip5 0.5 0.03295122741581933\nbase skip5 1 0.03282146617257311\nbase skip7 -0.5 0.03067223190540012\nbase skip7 -0.25 0.0321525812772168\nbase skip7 -0.1 0.03252061069716571\nbase skip7 0.1 0.03312741468674745\nbase skip7 0.25 0.03299343374046417\nbase skip7 0.5 0.03279525530657301\nbase skip7 1 0.03266542940289131\nbase org7 -0.5 0.03205272010389338\nbase org7 -0.25 0.03300502711337684\nbase org7 -0.1 0.03313037428098415\nbase org7 0.1 0.032816139469484716\nbase org7 0.25 0.032639072505816456\nbase org7 0.5 0.03229999075222186\nbase org7 1 0.03175319000106286\nbase strong7 -0.5 0.0316744178426837\nbase strong7 -0.25 0.03197551953278427\nbase strong7 -0.1 0.03285695705168735\nbase strong7 0.1 0.0329429937264542\nbase strong7 0.25 0.03280316860644252\nbase strong7 0.5 0.032864072768996874\nbase strong7 1 0.0328351140783166\nbase all5 -0.5 0.030896843458784698\nbase all5 -0.25 0.03144107091612027\nbase all5 -0.1 0.03249522919707681\nbase all5 0.1 0.03296405754725993\nbase all5 0.25 0.032936905522787635\nbase all5 0.5 0.03292526126860776\nbase all5 1 0.03281140685975686\nbase all10 -0.5 0.030585729365031615\nbase all10 -0.25 0.03134656975107349\nbase all10 -0.1 0.03237540406464214\nbase all10 0.1 0.0330652505316981\nbase all10 0.25 0.03295460588424532\nbase all10 0.5 0.033073852997783225\nbase all10 1 0.033313012636909126\nFIT likes 0\nHYBRID 0 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03373753337192489), 0.05: np.float64(0.03441873844184196), 0.075: np.float64(0.03488859984165817), 0.1: np.float64(0.03556748248959376), 0.15: np.float64(0.03665253520941666), 0.2: np.float64(0.03771278342304864), 0.3: np.float64(0.03872116805229546), 0.5: np.float64(0.037856284128177695), 1: np.float64(0.03027163987803039)}\nFIT likes 1\nHYBRID 1 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.033750137858625934), 0.05: np.float64(0.03450076058465348), 0.075: np.float64(0.03535947620299418), 0.1: np.float64(0.03573640876159942), 0.15: np.float64(0.03733431409438778), 0.2: np.float64(0.038318194226979484), 0.3: np.float64(0.03884630768990876), 0.5: np.float64(0.038268314571897756), 1: np.float64(0.03069837978192861)}\nFIT likes 3\nHYBRID 3 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03363019503776941), 0.05: np.float64(0.03424765995512439), 0.075: np.float64(0.03489279634823226), 0.1: np.float64(0.03584731525947013), 0.15: np.float64(0.0375129802329693), 0.2: np.float64(0.03810286144051453), 0.3: np.float64(0.03868415063515547), 0.5: np.float64(0.038082746180387726), 1: np.float64(0.031281630759976115)}\nFIT likes 6\nHYBRID 6 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03360268449095774), 0.05: np.float64(0.034145985720123645), 0.075: np.float64(0.035121704925288454), 0.1: np.float64(0.03612894879082033), 0.15: np.float64(0.037548126586520535), 0.2: np.float64(0.038080153808612294), 0.3: np.float64(0.03879245566063955), 0.5: np.float64(0.03741409128350689), 1: np.float64(0.030652308034569007)}\n", "stderr": "", "aggregated_output": "base skip3 -0.5 0.029946779281351112\nbase skip3 -0.25 0.03126644382123068\nbase skip3 -0.1 0.03190438624519286\nbase skip3 0.1 0.03315521543325755\nbase skip3 0.25 0.032995691816150745\nbase skip3 0.5 0.033010758087615506\nbase skip3 1 0.03286394776193453\nbase skip5 -0.5 0.030548146102484734\nbase skip5 -0.25 0.03149592280885828\nbase skip5 -0.1 0.03229907625191908\nbase skip5 0.1 0.03307387227747661\nbase skip5 0.25 0.033032604521235145\nbase skip5 0.5 0.03295122741581933\nbase skip5 1 0.03282146617257311\nbase skip7 -0.5 0.03067223190540012\nbase skip7 -0.25 0.0321525812772168\nbase skip7 -0.1 0.03252061069716571\nbase skip7 0.1 0.03312741468674745\nbase skip7 0.25 0.03299343374046417\nbase skip7 0.5 0.03279525530657301\nbase skip7 1 0.03266542940289131\nbase org7 -0.5 0.03205272010389338\nbase org7 -0.25 0.03300502711337684\nbase org7 -0.1 0.03313037428098415\nbase org7 0.1 0.032816139469484716\nbase org7 0.25 0.032639072505816456\nbase org7 0.5 0.03229999075222186\nbase org7 1 0.03175319000106286\nbase strong7 -0.5 0.0316744178426837\nbase strong7 -0.25 0.03197551953278427\nbase strong7 -0.1 0.03285695705168735\nbase strong7 0.1 0.0329429937264542\nbase strong7 0.25 0.03280316860644252\nbase strong7 0.5 0.032864072768996874\nbase strong7 1 0.0328351140783166\nbase all5 -0.5 0.030896843458784698\nbase all5 -0.25 0.03144107091612027\nbase all5 -0.1 0.03249522919707681\nbase all5 0.1 0.03296405754725993\nbase all5 0.25 0.032936905522787635\nbase all5 0.5 0.03292526126860776\nbase all5 1 0.03281140685975686\nbase all10 -0.5 0.030585729365031615\nbase all10 -0.25 0.03134656975107349\nbase all10 -0.1 0.03237540406464214\nbase all10 0.1 0.0330652505316981\nbase all10 0.25 0.03295460588424532\nbase all10 0.5 0.033073852997783225\nbase all10 1 0.033313012636909126\nFIT likes 0\nHYBRID 0 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03373753337192489), 0.05: np.float64(0.03441873844184196), 0.075: np.float64(0.03488859984165817), 0.1: np.float64(0.03556748248959376), 0.15: np.float64(0.03665253520941666), 0.2: np.float64(0.03771278342304864), 0.3: np.float64(0.03872116805229546), 0.5: np.float64(0.037856284128177695), 1: np.float64(0.03027163987803039)}\nFIT likes 1\nHYBRID 1 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.033750137858625934), 0.05: np.float64(0.03450076058465348), 0.075: np.float64(0.03535947620299418), 0.1: np.float64(0.03573640876159942), 0.15: np.float64(0.03733431409438778), 0.2: np.float64(0.038318194226979484), 0.3: np.float64(0.03884630768990876), 0.5: np.float64(0.038268314571897756), 1: np.float64(0.03069837978192861)}\nFIT likes 3\nHYBRID 3 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03363019503776941), 0.05: np.float64(0.03424765995512439), 0.075: np.float64(0.03489279634823226), 0.1: np.float64(0.03584731525947013), 0.15: np.float64(0.0375129802329693), 0.2: np.float64(0.03810286144051453), 0.3: np.float64(0.03868415063515547), 0.5: np.float64(0.038082746180387726), 1: np.float64(0.031281630759976115)}\nFIT likes 6\nHYBRID 6 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03360268449095774), 0.05: np.float64(0.034145985720123645), 0.075: np.float64(0.035121704925288454), 0.1: np.float64(0.03612894879082033), 0.15: np.float64(0.037548126586520535), 0.2: np.float64(0.038080153808612294), 0.3: np.float64(0.03879245566063955), 0.5: np.float64(0.03741409128350689), 1: np.float64(0.030652308034569007)}\n", "exit_code": 0, "duration": { "secs": 142, "nanos": 437315876 }, "formatted_output": "base skip3 -0.5 0.029946779281351112\nbase skip3 -0.25 0.03126644382123068\nbase skip3 -0.1 0.03190438624519286\nbase skip3 0.1 0.03315521543325755\nbase skip3 0.25 0.032995691816150745\nbase skip3 0.5 0.033010758087615506\nbase skip3 1 0.03286394776193453\nbase skip5 -0.5 0.030548146102484734\nbase skip5 -0.25 0.03149592280885828\nbase skip5 -0.1 0.03229907625191908\nbase skip5 0.1 0.03307387227747661\nbase skip5 0.25 0.033032604521235145\nbase skip5 0.5 0.03295122741581933\nbase skip5 1 0.03282146617257311\nbase skip7 -0.5 0.03067223190540012\nbase skip7 -0.25 0.0321525812772168\nbase skip7 -0.1 0.03252061069716571\nbase skip7 0.1 0.03312741468674745\nbase skip7 0.25 0.03299343374046417\nbase skip7 0.5 0.03279525530657301\nbase skip7 1 0.03266542940289131\nbase org7 -0.5 0.03205272010389338\nbase org7 -0.25 0.03300502711337684\nbase org7 -0.1 0.03313037428098415\nbase org7 0.1 0.032816139469484716\nbase org7 0.25 0.032639072505816456\nbase org7 0.5 0.03229999075222186\nbase org7 1 0.03175319000106286\nbase strong7 -0.5 0.0316744178426837\nbase strong7 -0.25 0.03197551953278427\nbase strong7 -0.1 0.03285695705168735\nbase strong7 0.1 0.0329429937264542\nbase strong7 0.25 0.03280316860644252\nbase strong7 0.5 0.032864072768996874\nbase strong7 1 0.0328351140783166\nbase all5 -0.5 0.030896843458784698\nbase all5 -0.25 0.03144107091612027\nbase all5 -0.1 0.03249522919707681\nbase all5 0.1 0.03296405754725993\nbase all5 0.25 0.032936905522787635\nbase all5 0.5 0.03292526126860776\nbase all5 1 0.03281140685975686\nbase all10 -0.5 0.030585729365031615\nbase all10 -0.25 0.03134656975107349\nbase all10 -0.1 0.03237540406464214\nbase all10 0.1 0.0330652505316981\nbase all10 0.25 0.03295460588424532\nbase all10 0.5 0.033073852997783225\nbase all10 1 0.033313012636909126\nFIT likes 0\nHYBRID 0 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03373753337192489), 0.05: np.float64(0.03441873844184196), 0.075: np.float64(0.03488859984165817), 0.1: np.float64(0.03556748248959376), 0.15: np.float64(0.03665253520941666), 0.2: np.float64(0.03771278342304864), 0.3: np.float64(0.03872116805229546), 0.5: np.float64(0.037856284128177695), 1: np.float64(0.03027163987803039)}\nFIT likes 1\nHYBRID 1 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.033750137858625934), 0.05: np.float64(0.03450076058465348), 0.075: np.float64(0.03535947620299418), 0.1: np.float64(0.03573640876159942), 0.15: np.float64(0.03733431409438778), 0.2: np.float64(0.038318194226979484), 0.3: np.float64(0.03884630768990876), 0.5: np.float64(0.038268314571897756), 1: np.float64(0.03069837978192861)}\nFIT likes 3\nHYBRID 3 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03363019503776941), 0.05: np.float64(0.03424765995512439), 0.075: np.float64(0.03489279634823226), 0.1: np.float64(0.03584731525947013), 0.15: np.float64(0.0375129802329693), 0.2: np.float64(0.03810286144051453), 0.3: np.float64(0.03868415063515547), 0.5: np.float64(0.038082746180387726), 1: np.float64(0.031281630759976115)}\nFIT likes 6\nHYBRID 6 {0: np.float64(0.033184193148729016), 0.025: np.float64(0.03360268449095774), 0.05: np.float64(0.034145985720123645), 0.075: np.float64(0.035121704925288454), 0.1: np.float64(0.03612894879082033), 0.15: np.float64(0.037548126586520535), 0.2: np.float64(0.038080153808612294), 0.3: np.float64(0.03879245566063955), 0.5: np.float64(0.03741409128350689), 1: np.float64(0.030652308034569007)}\n" } } ## Step 49 · agent · 2026-09-23T21:56:49.246Z ### Thinking unavailable — no text recorded ## Step 50 · agent · 2026-09-23T21:56:53.681Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:57:29.699Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/compare_models.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+sys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data');O=Path('/app/solution')\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\\n+X=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32);R=sparse.load_npz(O/'train_catalog_recent.npz').astype(np.float32)\\n+pref=pd.read_parquet(D/'validation_preference_state.parquet');pref=pref[pref.state.eq('liked')];rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc);L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\\n+sig=np.load(O/'global_signals.npz');Z={k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9) for k in sig.files};bases={'b1':Z['all7']+.75*Z['like7']+.1*Z['all3'],'b2':Z['all7']+.75*Z['like7']+.1*Z['all3']+Z['all10']}\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\\n+li=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+alphas=[.15,.2,.25,.3,.35,.4,.5]\\n+def fit_eval(A,name,k):\\n+ print('fit',name,k,flush=True);sv=TruncatedSVD(n_components=k,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32);np.savez_compressed(O/f'factors_{name}_{k}.npz',U=U,V=V)\\n+ vals={(bn,a):[] for bn in bases for a in alphas};pred={(bn,a):{} for bn in bases for a in alphas}\\n+ for s in range(0,len(targets),256):\\n+ ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\\n+ for j,k0 in enumerate(ids):\\n+ u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\\n+ for bn,b in bases.items():\\n+ for a in alphas:\\n+ ss=b+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(bn,a)][u]=items[top].tolist()\\n+ for q in vals: vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\\n+ print(name,k,vals,flush=True)\\n+for A,n,k in [(X+L,'countlikes',128),(X+L,'countlikes',256),(R+L,'recentlikes',128)]:fit_eval(A,n,k)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T21:57:29.767Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-aca9228a-f8b7-4061-ad86-ec93095eee24", "changes": { "/app/solution/compare_models.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32);R=sparse.load_npz(O/'train_catalog_recent.npz').astype(np.float32)\npref=pd.read_parquet(D/'validation_preference_state.parquet');pref=pref[pref.state.eq('liked')];rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc);L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsig=np.load(O/'global_signals.npz');Z={k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9) for k in sig.files};bases={'b1':Z['all7']+.75*Z['like7']+.1*Z['all3'],'b2':Z['all7']+.75*Z['like7']+.1*Z['all3']+Z['all10']}\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nalphas=[.15,.2,.25,.3,.35,.4,.5]\ndef fit_eval(A,name,k):\n print('fit',name,k,flush=True);sv=TruncatedSVD(n_components=k,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32);np.savez_compressed(O/f'factors_{name}_{k}.npz',U=U,V=V)\n vals={(bn,a):[] for bn in bases for a in alphas};pred={(bn,a):{} for bn in bases for a in alphas}\n for s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for bn,b in bases.items():\n for a in alphas:\n ss=b+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(bn,a)][u]=items[top].tolist()\n for q in vals: vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\n print(name,k,vals,flush=True)\nfor A,n,k in [(X+L,'countlikes',128),(X+L,'countlikes',256),(R+L,'recentlikes',128)]:fit_eval(A,n,k)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/compare_models.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b0c281487d2810a69b863d49c80", "usage": { "input_tokens": 41049, "cached_input_tokens": 40576, "cache_write_input_tokens": 0, "output_tokens": 1784, "reasoning_output_tokens": 649, "total_tokens": 42833 }, "turn_token_usage": { "input_tokens": 1204455, "cached_input_tokens": 1143424, "cache_write_input_tokens": 0, "output_tokens": 15394, "reasoning_output_tokens": 4766, "total_tokens": 1219849 }, "thread_token_usage": { "input_tokens": 1204455, "cached_input_tokens": 1143424, "cache_write_input_tokens": 0, "output_tokens": 15394, "reasoning_output_tokens": 4766, "total_tokens": 1219849 } } ## Step 51 · agent · 2026-09-23T21:57:32.202Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:57:33.953Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/compare_models.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T21:57:35.167Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b22f0a","wall_time_seconds":1.001176591,"session_id":11167,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b4a16c487d280242538449a6b84", "usage": { "input_tokens": 42857, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 6, "total_tokens": 42927 }, "turn_token_usage": { "input_tokens": 1247312, "cached_input_tokens": 1184256, "cache_write_input_tokens": 0, "output_tokens": 15464, "reasoning_output_tokens": 4772, "total_tokens": 1262776 }, "thread_token_usage": { "input_tokens": 1247312, "cached_input_tokens": 1184256, "cache_write_input_tokens": 0, "output_tokens": 15464, "reasoning_output_tokens": 4772, "total_tokens": 1262776 } } ## Step 52 · agent · 2026-09-23T21:57:36.530Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:57:38.494Z { "code": "const r = await tools.write_stdin({\"session_id\":11167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:58:08.586Z Script completed Wall time 30.0 seconds Output: fit countlikes 128 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b4f5dc487d2bf3bc1fb17f3c9aa", "usage": { "input_tokens": 42986, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 43040 }, "turn_token_usage": { "input_tokens": 1290298, "cached_input_tokens": 1226880, "cache_write_input_tokens": 0, "output_tokens": 15518, "reasoning_output_tokens": 4779, "total_tokens": 1305816 }, "thread_token_usage": { "input_tokens": 1290298, "cached_input_tokens": 1226880, "cache_write_input_tokens": 0, "output_tokens": 15518, "reasoning_output_tokens": 4779, "total_tokens": 1305816 } } ### exec · 2026-09-23T21:58:11.469Z { "code": "const r = await tools.write_stdin({\"session_id\":11167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:58:41.491Z Script completed Wall time 30.0 seconds Output: countlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)} fit countlikes 256 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b70ccc887d29409bb2d4b92c1e9", "usage": { "input_tokens": 43069, "cached_input_tokens": 42752, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 43114 }, "turn_token_usage": { "input_tokens": 1333367, "cached_input_tokens": 1269632, "cache_write_input_tokens": 0, "output_tokens": 15563, "reasoning_output_tokens": 4779, "total_tokens": 1348930 }, "thread_token_usage": { "input_tokens": 1333367, "cached_input_tokens": 1269632, "cache_write_input_tokens": 0, "output_tokens": 15563, "reasoning_output_tokens": 4779, "total_tokens": 1348930 } } ## Step 53 · agent · 2026-09-23T21:58:44.467Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:58:45.682Z { "code": "const r = await tools.write_stdin({\"session_id\":11167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:59:15.692Z Script completed Wall time 30.0 seconds Output: countlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)} fit recentlikes 128 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44b91b0ec87d2a6fc093867e8e738", "usage": { "input_tokens": 43456, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 15, "total_tokens": 43518 }, "turn_token_usage": { "input_tokens": 1376823, "cached_input_tokens": 1312512, "cache_write_input_tokens": 0, "output_tokens": 15625, "reasoning_output_tokens": 4794, "total_tokens": 1392448 }, "thread_token_usage": { "input_tokens": 1376823, "cached_input_tokens": 1312512, "cache_write_input_tokens": 0, "output_tokens": 15625, "reasoning_output_tokens": 4794, "total_tokens": 1392448 } } ## Step 54 · agent · 2026-09-23T21:59:18.157Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:59:19.369Z { "code": "const r = await tools.write_stdin({\"session_id\":11167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T21:59:34.528Z Script completed Wall time 15.1 seconds Output: recentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44bb3e24c87d29650d3a57b49ebbe", "usage": { "input_tokens": 43860, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 41, "total_tokens": 43948 }, "turn_token_usage": { "input_tokens": 1420683, "cached_input_tokens": 1355776, "cache_write_input_tokens": 0, "output_tokens": 15713, "reasoning_output_tokens": 4835, "total_tokens": 1436396 }, "thread_token_usage": { "input_tokens": 1420683, "cached_input_tokens": 1355776, "cache_write_input_tokens": 0, "output_tokens": 15713, "reasoning_output_tokens": 4835, "total_tokens": 1436396 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5a93b5b-b703-4669-9dba-0dd6b079a268", "process_id": "11167", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/compare_models.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/compare_models.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n", "stderr": "", "aggregated_output": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n", "exit_code": 0, "duration": { "secs": 120, "nanos": 360374920 }, "formatted_output": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n" } } ## Step 55 · agent · 2026-09-23T21:59:41.139Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:10.248Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/ensemble_validate.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\n+sys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\\n+F=[]\\n+for f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\\n+ q=np.load(O/f);F.append((q['U'],q['V']))\\n+sig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\\n+li=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+weights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\\n+for s in range(0,len(targets),256):\\n+ ids=np.arange(s,min(s+256,len(targets)));Ps=[]\\n+ for U,V in F[:2]:\\n+ P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8);Ps.append(P)\\n+ for j,k0 in enumerate(ids):\\n+ u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\\n+ for w in weights:\\n+ taste=(1-w)*Ps[0][j]+w*Ps[1][j]\\n+ for a in alphas:\\n+ ss=base+a*taste;ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(w,a)][u]=items[top].tolist()\\n+for q in vals:vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\\n+print(vals);print('BEST',max(vals,key=vals.get),max(vals.values()))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T22:00:10.343Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-340867d9-ce24-4292-b4c3-dac7f45ba899", "changes": { "/app/solution/ensemble_validate.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nF=[]\nfor f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n q=np.load(O/f);F.append((q['U'],q['V']))\nsig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nweights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\nfor s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets)));Ps=[]\n for U,V in F[:2]:\n P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8);Ps.append(P)\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for w in weights:\n taste=(1-w)*Ps[0][j]+w*Ps[1][j]\n for a in alphas:\n ss=base+a*taste;ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(w,a)][u]=items[top].tolist()\nfor q in vals:vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\nprint(vals);print('BEST',max(vals,key=vals.get),max(vals.values()))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/ensemble_validate.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44bc6ca5c87d2b8b101071aa62f4d", "usage": { "input_tokens": 44284, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 1073, "reasoning_output_tokens": 154, "total_tokens": 45357 }, "turn_token_usage": { "input_tokens": 1464967, "cached_input_tokens": 1399424, "cache_write_input_tokens": 0, "output_tokens": 16786, "reasoning_output_tokens": 4989, "total_tokens": 1481753 }, "thread_token_usage": { "input_tokens": 1464967, "cached_input_tokens": 1399424, "cache_write_input_tokens": 0, "output_tokens": 16786, "reasoning_output_tokens": 4989, "total_tokens": 1481753 } } ### exec · 2026-09-23T22:00:14.768Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/ensemble_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:00:15.971Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"c2467f","wall_time_seconds":1.001046516,"session_id":75633,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44beacdc487d2a31c96e591b55529", "usage": { "input_tokens": 45381, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 45443 }, "turn_token_usage": { "input_tokens": 1510348, "cached_input_tokens": 1443584, "cache_write_input_tokens": 0, "output_tokens": 16848, "reasoning_output_tokens": 4989, "total_tokens": 1527196 }, "thread_token_usage": { "input_tokens": 1510348, "cached_input_tokens": 1443584, "cache_write_input_tokens": 0, "output_tokens": 16848, "reasoning_output_tokens": 4989, "total_tokens": 1527196 } } ## Step 56 · agent · 2026-09-23T22:00:19.899Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:21.119Z { "code": "const r = await tools.write_stdin({\"session_id\":75633,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:00:51.129Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44bf0606087d2aaa76b022b09e948", "usage": { "input_tokens": 45501, "cached_input_tokens": 35328, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 45554 }, "turn_token_usage": { "input_tokens": 1555849, "cached_input_tokens": 1478912, "cache_write_input_tokens": 0, "output_tokens": 16901, "reasoning_output_tokens": 4995, "total_tokens": 1572750 }, "thread_token_usage": { "input_tokens": 1555849, "cached_input_tokens": 1478912, "cache_write_input_tokens": 0, "output_tokens": 16901, "reasoning_output_tokens": 4995, "total_tokens": 1572750 } } ## Step 57 · agent · 2026-09-23T22:00:54.499Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:55.579Z { "code": "const r = await tools.write_stdin({\"session_id\":75633,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:01:07.950Z Script completed Wall time 12.4 seconds Output: {(0, 0.25): np.float64(0.04177864322422572), (0, 0.3): np.float64(0.04242218678150414), (0, 0.35): np.float64(0.04278694932290147), (0, 0.4): np.float64(0.04254463853508151), (0, 0.45): np.float64(0.04272426859019755), (0, 0.5): np.float64(0.04255527341377238), (0, 0.6): np.float64(0.0420232784685573), (0.15, 0.25): np.float64(0.04172541732478755), (0.15, 0.3): np.float64(0.042106883545530015), (0.15, 0.35): np.float64(0.04311122919077662), (0.15, 0.4): np.float64(0.042854444942567856), (0.15, 0.45): np.float64(0.04282254328857891), (0.15, 0.5): np.float64(0.04269488443934665), (0.15, 0.6): np.float64(0.042432655679974174), (0.3, 0.25): np.float64(0.041286043742197), (0.3, 0.3): np.float64(0.04224751986781556), (0.3, 0.35): np.float64(0.04287394821259502), (0.3, 0.4): np.float64(0.04331218097102937), (0.3, 0.45): np.float64(0.04299939762134315), (0.3, 0.5): np.float64(0.04262172663706515), (0.3, 0.6): np.float64(0.04264570764885469), (0.5, 0.25): np.float64(0.04029712444714144), (0.5, 0.3): np.float64(0.04169076976161125), (0.5, 0.35): np.float64(0.042609450323925296), (0.5, 0.4): np.float64(0.04310468223644114), (0.5, 0.45): np.float64(0.04326422332886661), (0.5, 0.5): np.float64(0.04267258487260031), (0.5, 0.6): np.float64(0.04215249595702143), (0.7, 0.25): np.float64(0.03909175530196694), (0.7, 0.3): np.float64(0.040385066546918265), (0.7, 0.35): np.float64(0.04168337431612603), (0.7, 0.4): np.float64(0.04253136102075522), (0.7, 0.45): np.float64(0.04228626245983116), (0.7, 0.5): np.float64(0.042717525666815706), (0.7, 0.6): np.float64(0.04197646558527758), (1, 0.25): np.float64(0.03785824508770949), (1, 0.3): np.float64(0.038693113397033504), (1, 0.35): np.float64(0.03922769502633056), (1, 0.4): np.float64(0.03951803891885395), (1, 0.45): np.float64(0.04021993957097214), (1, 0.5): np.float64(0.03959999614879957), (1, 0.6): np.float64(0.039012342871551954)} BEST (0.3, 0.4) 0.04331218097102937 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44c13532c87d2a1d3bf233099733b", "usage": { "input_tokens": 45577, "cached_input_tokens": 45312, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 27, "total_tokens": 45651 }, "turn_token_usage": { "input_tokens": 1601426, "cached_input_tokens": 1524224, "cache_write_input_tokens": 0, "output_tokens": 16975, "reasoning_output_tokens": 5022, "total_tokens": 1618401 }, "thread_token_usage": { "input_tokens": 1601426, "cached_input_tokens": 1524224, "cache_write_input_tokens": 0, "output_tokens": 16975, "reasoning_output_tokens": 5022, "total_tokens": 1618401 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c7540ce5-8959-4efc-92b3-69d80526c263", "process_id": "75633", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/ensemble_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/ensemble_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{(0, 0.25): np.float64(0.04177864322422572), (0, 0.3): np.float64(0.04242218678150414), (0, 0.35): np.float64(0.04278694932290147), (0, 0.4): np.float64(0.04254463853508151), (0, 0.45): np.float64(0.04272426859019755), (0, 0.5): np.float64(0.04255527341377238), (0, 0.6): np.float64(0.0420232784685573), (0.15, 0.25): np.float64(0.04172541732478755), (0.15, 0.3): np.float64(0.042106883545530015), (0.15, 0.35): np.float64(0.04311122919077662), (0.15, 0.4): np.float64(0.042854444942567856), (0.15, 0.45): np.float64(0.04282254328857891), (0.15, 0.5): np.float64(0.04269488443934665), (0.15, 0.6): np.float64(0.042432655679974174), (0.3, 0.25): np.float64(0.041286043742197), (0.3, 0.3): np.float64(0.04224751986781556), (0.3, 0.35): np.float64(0.04287394821259502), (0.3, 0.4): np.float64(0.04331218097102937), (0.3, 0.45): np.float64(0.04299939762134315), (0.3, 0.5): np.float64(0.04262172663706515), (0.3, 0.6): np.float64(0.04264570764885469), (0.5, 0.25): np.float64(0.04029712444714144), (0.5, 0.3): np.float64(0.04169076976161125), (0.5, 0.35): np.float64(0.042609450323925296), (0.5, 0.4): np.float64(0.04310468223644114), (0.5, 0.45): np.float64(0.04326422332886661), (0.5, 0.5): np.float64(0.04267258487260031), (0.5, 0.6): np.float64(0.04215249595702143), (0.7, 0.25): np.float64(0.03909175530196694), (0.7, 0.3): np.float64(0.040385066546918265), (0.7, 0.35): np.float64(0.04168337431612603), (0.7, 0.4): np.float64(0.04253136102075522), (0.7, 0.45): np.float64(0.04228626245983116), (0.7, 0.5): np.float64(0.042717525666815706), (0.7, 0.6): np.float64(0.04197646558527758), (1, 0.25): np.float64(0.03785824508770949), (1, 0.3): np.float64(0.038693113397033504), (1, 0.35): np.float64(0.03922769502633056), (1, 0.4): np.float64(0.03951803891885395), (1, 0.45): np.float64(0.04021993957097214), (1, 0.5): np.float64(0.03959999614879957), (1, 0.6): np.float64(0.039012342871551954)}\nBEST (0.3, 0.4) 0.04331218097102937\n", "stderr": "", "aggregated_output": "{(0, 0.25): np.float64(0.04177864322422572), (0, 0.3): np.float64(0.04242218678150414), (0, 0.35): np.float64(0.04278694932290147), (0, 0.4): np.float64(0.04254463853508151), (0, 0.45): np.float64(0.04272426859019755), (0, 0.5): np.float64(0.04255527341377238), (0, 0.6): np.float64(0.0420232784685573), (0.15, 0.25): np.float64(0.04172541732478755), (0.15, 0.3): np.float64(0.042106883545530015), (0.15, 0.35): np.float64(0.04311122919077662), (0.15, 0.4): np.float64(0.042854444942567856), (0.15, 0.45): np.float64(0.04282254328857891), (0.15, 0.5): np.float64(0.04269488443934665), (0.15, 0.6): np.float64(0.042432655679974174), (0.3, 0.25): np.float64(0.041286043742197), (0.3, 0.3): np.float64(0.04224751986781556), (0.3, 0.35): np.float64(0.04287394821259502), (0.3, 0.4): np.float64(0.04331218097102937), (0.3, 0.45): np.float64(0.04299939762134315), (0.3, 0.5): np.float64(0.04262172663706515), (0.3, 0.6): np.float64(0.04264570764885469), (0.5, 0.25): np.float64(0.04029712444714144), (0.5, 0.3): np.float64(0.04169076976161125), (0.5, 0.35): np.float64(0.042609450323925296), (0.5, 0.4): np.float64(0.04310468223644114), (0.5, 0.45): np.float64(0.04326422332886661), (0.5, 0.5): np.float64(0.04267258487260031), (0.5, 0.6): np.float64(0.04215249595702143), (0.7, 0.25): np.float64(0.03909175530196694), (0.7, 0.3): np.float64(0.040385066546918265), (0.7, 0.35): np.float64(0.04168337431612603), (0.7, 0.4): np.float64(0.04253136102075522), (0.7, 0.45): np.float64(0.04228626245983116), (0.7, 0.5): np.float64(0.042717525666815706), (0.7, 0.6): np.float64(0.04197646558527758), (1, 0.25): np.float64(0.03785824508770949), (1, 0.3): np.float64(0.038693113397033504), (1, 0.35): np.float64(0.03922769502633056), (1, 0.4): np.float64(0.03951803891885395), (1, 0.45): np.float64(0.04021993957097214), (1, 0.5): np.float64(0.03959999614879957), (1, 0.6): np.float64(0.039012342871551954)}\nBEST (0.3, 0.4) 0.04331218097102937\n", "exit_code": 0, "duration": { "secs": 52, "nanos": 978059916 }, "formatted_output": "{(0, 0.25): np.float64(0.04177864322422572), (0, 0.3): np.float64(0.04242218678150414), (0, 0.35): np.float64(0.04278694932290147), (0, 0.4): np.float64(0.04254463853508151), (0, 0.45): np.float64(0.04272426859019755), (0, 0.5): np.float64(0.04255527341377238), (0, 0.6): np.float64(0.0420232784685573), (0.15, 0.25): np.float64(0.04172541732478755), (0.15, 0.3): np.float64(0.042106883545530015), (0.15, 0.35): np.float64(0.04311122919077662), (0.15, 0.4): np.float64(0.042854444942567856), (0.15, 0.45): np.float64(0.04282254328857891), (0.15, 0.5): np.float64(0.04269488443934665), (0.15, 0.6): np.float64(0.042432655679974174), (0.3, 0.25): np.float64(0.041286043742197), (0.3, 0.3): np.float64(0.04224751986781556), (0.3, 0.35): np.float64(0.04287394821259502), (0.3, 0.4): np.float64(0.04331218097102937), (0.3, 0.45): np.float64(0.04299939762134315), (0.3, 0.5): np.float64(0.04262172663706515), (0.3, 0.6): np.float64(0.04264570764885469), (0.5, 0.25): np.float64(0.04029712444714144), (0.5, 0.3): np.float64(0.04169076976161125), (0.5, 0.35): np.float64(0.042609450323925296), (0.5, 0.4): np.float64(0.04310468223644114), (0.5, 0.45): np.float64(0.04326422332886661), (0.5, 0.5): np.float64(0.04267258487260031), (0.5, 0.6): np.float64(0.04215249595702143), (0.7, 0.25): np.float64(0.03909175530196694), (0.7, 0.3): np.float64(0.040385066546918265), (0.7, 0.35): np.float64(0.04168337431612603), (0.7, 0.4): np.float64(0.04253136102075522), (0.7, 0.45): np.float64(0.04228626245983116), (0.7, 0.5): np.float64(0.042717525666815706), (0.7, 0.6): np.float64(0.04197646558527758), (1, 0.25): np.float64(0.03785824508770949), (1, 0.3): np.float64(0.038693113397033504), (1, 0.35): np.float64(0.03922769502633056), (1, 0.4): np.float64(0.03951803891885395), (1, 0.45): np.float64(0.04021993957097214), (1, 0.5): np.float64(0.03959999614879957), (1, 0.6): np.float64(0.039012342871551954)}\nBEST (0.3, 0.4) 0.04331218097102937\n" } } ## Step 58 · agent · 2026-09-23T22:01:27.569Z ### Thinking unavailable — no text recorded ## Step 59 · agent · 2026-09-23T22:01:33.108Z ### Thinking unavailable — no text recorded The latent model adds substantial lift: the best recent-history factorization reaches ≈0.0428, and blending it with a longer-term factorization reaches ≈0.0433. I’m tightening the remaining high-impact choices now—how strongly explicit likes enter the factorization and whether extra latent dimensions help—then I’ll lock the validation recipe and refit exactly the same pipeline on `train + val`. ### exec · 2026-09-23T22:01:43.169Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed 's/for A,n,k in \\\\[(X+L,\\\\x27countlikes\\\\x27,128),(X+L,\\\\x27countlikes\\\\x27,256),(R+L,\\\\x27recentlikes\\\\x27,128)\\\\]:fit_eval(A,n,k)/for A,n,k in [(R,\\\\x27recent_l0\\\\x27,128),(R+.5*L,\\\\x27recent_l05\\\\x27,128),(R+2*L,\\\\x27recent_l2\\\\x27,128),(R+L,\\\\x27recentlikes\\\\x27,256)]:fit_eval(A,n,k)/' /app/solution/compare_models.py > /app/solution/compare_likes.py\\n/opt/task-venv/bin/python /app/solution/compare_likes.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:01:44.370Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"46539b","wall_time_seconds":1.001394273,"session_id":19624,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44c2444c087d28fd9fb90c781bdf1", "usage": { "input_tokens": 46633, "cached_input_tokens": 45184, "cache_write_input_tokens": 0, "output_tokens": 1005, "reasoning_output_tokens": 688, "total_tokens": 47638 }, "turn_token_usage": { "input_tokens": 1648059, "cached_input_tokens": 1569408, "cache_write_input_tokens": 0, "output_tokens": 17980, "reasoning_output_tokens": 5710, "total_tokens": 1666039 }, "thread_token_usage": { "input_tokens": 1648059, "cached_input_tokens": 1569408, "cache_write_input_tokens": 0, "output_tokens": 17980, "reasoning_output_tokens": 5710, "total_tokens": 1666039 } } ## Step 60 · agent · 2026-09-23T22:01:51.605Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:01:52.788Z { "code": "const r = await tools.write_stdin({\"session_id\":19624,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:02:22.797Z Script completed Wall time 30.0 seconds Output: fit recent_l0 128 recent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)} fit recent_l05 128 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44c4893c087d291c236b708946957", "usage": { "input_tokens": 47695, "cached_input_tokens": 46464, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 61, "total_tokens": 47803 }, "turn_token_usage": { "input_tokens": 1695754, "cached_input_tokens": 1615872, "cache_write_input_tokens": 0, "output_tokens": 18088, "reasoning_output_tokens": 5771, "total_tokens": 1713842 }, "thread_token_usage": { "input_tokens": 1695754, "cached_input_tokens": 1615872, "cache_write_input_tokens": 0, "output_tokens": 18088, "reasoning_output_tokens": 5771, "total_tokens": 1713842 } } ## Step 61 · agent · 2026-09-23T22:02:31.010Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:02:32.165Z { "code": "const r = await tools.write_stdin({\"session_id\":19624,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:03:02.271Z Script completed Wall time 30.1 seconds Output: recent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)} fit recent_l2 128 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44c6f053c87d2a75da520a7aaf212", "usage": { "input_tokens": 48154, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 31, "total_tokens": 48232 }, "turn_token_usage": { "input_tokens": 1743908, "cached_input_tokens": 1663360, "cache_write_input_tokens": 0, "output_tokens": 18166, "reasoning_output_tokens": 5802, "total_tokens": 1762074 }, "thread_token_usage": { "input_tokens": 1743908, "cached_input_tokens": 1663360, "cache_write_input_tokens": 0, "output_tokens": 18166, "reasoning_output_tokens": 5802, "total_tokens": 1762074 } } ## Step 62 · agent · 2026-09-23T22:03:07.075Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:08.423Z { "code": "const r = await tools.write_stdin({\"session_id\":19624,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:03:38.476Z Script completed Wall time 30.0 seconds Output: recent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)} fit recentlikes 256 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44c96b15c87d2979a81890655c574", "usage": { "input_tokens": 48576, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 48637 }, "turn_token_usage": { "input_tokens": 1792484, "cached_input_tokens": 1711360, "cache_write_input_tokens": 0, "output_tokens": 18227, "reasoning_output_tokens": 5816, "total_tokens": 1810711 }, "thread_token_usage": { "input_tokens": 1792484, "cached_input_tokens": 1711360, "cache_write_input_tokens": 0, "output_tokens": 18227, "reasoning_output_tokens": 5816, "total_tokens": 1810711 } } ## Step 63 · agent · 2026-09-23T22:03:40.981Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:42.165Z { "code": "const r = await tools.write_stdin({\"session_id\":19624,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:04:12.201Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44cbacc8c87d2a1085317840805d5", "usage": { "input_tokens": 48980, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 49033 }, "turn_token_usage": { "input_tokens": 1841464, "cached_input_tokens": 1759744, "cache_write_input_tokens": 0, "output_tokens": 18280, "reasoning_output_tokens": 5822, "total_tokens": 1859744 }, "thread_token_usage": { "input_tokens": 1841464, "cached_input_tokens": 1759744, "cache_write_input_tokens": 0, "output_tokens": 18280, "reasoning_output_tokens": 5822, "total_tokens": 1859744 } } ### exec · 2026-09-23T22:04:14.675Z { "code": "const r = await tools.write_stdin({\"session_id\":19624,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:04:15.967Z Script completed Wall time 1.3 seconds Output: recentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44cdc99e887d2a574245fd75f2576", "usage": { "input_tokens": 49056, "cached_input_tokens": 48768, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 49101 }, "turn_token_usage": { "input_tokens": 1890520, "cached_input_tokens": 1808512, "cache_write_input_tokens": 0, "output_tokens": 18325, "reasoning_output_tokens": 5822, "total_tokens": 1908845 }, "thread_token_usage": { "input_tokens": 1890520, "cached_input_tokens": 1808512, "cache_write_input_tokens": 0, "output_tokens": 18325, "reasoning_output_tokens": 5822, "total_tokens": 1908845 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0cddb0e1-cb3a-44ef-9ffc-998bf2d9e41e", "process_id": "19624", "command": [ "/bin/sh", "-lc", "sed 's/for A,n,k in \\[(X+L,\\x27countlikes\\x27,128),(X+L,\\x27countlikes\\x27,256),(R+L,\\x27recentlikes\\x27,128)\\]:fit_eval(A,n,k)/for A,n,k in [(R,\\x27recent_l0\\x27,128),(R+.5*L,\\x27recent_l05\\x27,128),(R+2*L,\\x27recent_l2\\x27,128),(R+L,\\x27recentlikes\\x27,256)]:fit_eval(A,n,k)/' /app/solution/compare_models.py > /app/solution/compare_likes.py\n/opt/task-venv/bin/python /app/solution/compare_likes.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed 's/for A,n,k in \\[(X+L,\\x27countlikes\\x27,128),(X+L,\\x27countlikes\\x27,256),(R+L,\\x27recentlikes\\x27,128)\\]:fit_eval(A,n,k)/for A,n,k in [(R,\\x27recent_l0\\x27,128),(R+.5*L,\\x27recent_l05\\x27,128),(R+2*L,\\x27recent_l2\\x27,128),(R+L,\\x27recentlikes\\x27,256)]:fit_eval(A,n,k)/' /app/solution/compare_models.py > /app/solution/compare_likes.py\n/opt/task-venv/bin/python /app/solution/compare_likes.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n", "stderr": "", "aggregated_output": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n", "exit_code": 0, "duration": { "secs": 152, "nanos": 596509868 }, "formatted_output": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n" } } ## Step 64 · agent · 2026-09-23T22:04:18.985Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:04:26.636Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/ensemble_validate.py\\n@@\\n-for f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\\n+for f in ['factors_recent_l05_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\\n@@\\n-weights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\\n+weights=[0,.1,.2,.3,.4,.5];alphas=[.35,.4,.45,.5,.55,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\\n*** Delete File: /app/solution/compare_likes.py\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T22:04:26.756Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-826bf746-e904-47b0-9923-fadaf7c0dd03", "changes": { "/app/solution/ensemble_validate.py": { "type": "update", "unified_diff": "@@ -6,3 +6,3 @@\n F=[]\n-for f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n+for f in ['factors_recent_l05_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n q=np.load(O/f);F.append((q['U'],q['V']))\n@@ -14,3 +14,3 @@\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\n-weights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\n+weights=[0,.1,.2,.3,.4,.5];alphas=[.35,.4,.45,.5,.55,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\n for s in range(0,len(targets),256):\n", "move_path": null }, "/app/solution/compare_likes.py": { "type": "delete", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32);R=sparse.load_npz(O/'train_catalog_recent.npz').astype(np.float32)\npref=pd.read_parquet(D/'validation_preference_state.parquet');pref=pref[pref.state.eq('liked')];rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc);L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsig=np.load(O/'global_signals.npz');Z={k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9) for k in sig.files};bases={'b1':Z['all7']+.75*Z['like7']+.1*Z['all3'],'b2':Z['all7']+.75*Z['like7']+.1*Z['all3']+Z['all10']}\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nalphas=[.15,.2,.25,.3,.35,.4,.5]\ndef fit_eval(A,name,k):\n print('fit',name,k,flush=True);sv=TruncatedSVD(n_components=k,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32);np.savez_compressed(O/f'factors_{name}_{k}.npz',U=U,V=V)\n vals={(bn,a):[] for bn in bases for a in alphas};pred={(bn,a):{} for bn in bases for a in alphas}\n for s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for bn,b in bases.items():\n for a in alphas:\n ss=b+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(bn,a)][u]=items[top].tolist()\n for q in vals: vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\n print(name,k,vals,flush=True)\nfor A,n,k in [(R,'recent_l0',128),(R+.5*L,'recent_l05',128),(R+2*L,'recent_l2',128),(R+L,'recentlikes',256)]:fit_eval(A,n,k)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/ensemble_validate.py\nD /app/solution/compare_likes.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44ce040fc87d2bbc30431907b7f95", "usage": { "input_tokens": 49437, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 301, "reasoning_output_tokens": 53, "total_tokens": 49738 }, "turn_token_usage": { "input_tokens": 1939957, "cached_input_tokens": 1857408, "cache_write_input_tokens": 0, "output_tokens": 18626, "reasoning_output_tokens": 5875, "total_tokens": 1958583 }, "thread_token_usage": { "input_tokens": 1939957, "cached_input_tokens": 1857408, "cache_write_input_tokens": 0, "output_tokens": 18626, "reasoning_output_tokens": 5875, "total_tokens": 1958583 } } ## Step 65 · agent · 2026-09-23T22:04:29.717Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:04:31.474Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/ensemble_validate.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:04:32.670Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"8418ed","wall_time_seconds":1.001260681,"session_id":23338,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44ceb183887d2855d2c31fe711eba", "usage": { "input_tokens": 49762, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 6, "total_tokens": 49832 }, "turn_token_usage": { "input_tokens": 1989719, "cached_input_tokens": 1902848, "cache_write_input_tokens": 0, "output_tokens": 18696, "reasoning_output_tokens": 5881, "total_tokens": 2008415 }, "thread_token_usage": { "input_tokens": 1989719, "cached_input_tokens": 1902848, "cache_write_input_tokens": 0, "output_tokens": 18696, "reasoning_output_tokens": 5881, "total_tokens": 2008415 } } ### exec · 2026-09-23T22:04:35.189Z { "code": "const r = await tools.write_stdin({\"session_id\":23338,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:05:05.210Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44cf0e5e087d2847f9a2c24dfab19", "usage": { "input_tokens": 49889, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 49934 }, "turn_token_usage": { "input_tokens": 2039608, "cached_input_tokens": 1952384, "cache_write_input_tokens": 0, "output_tokens": 18741, "reasoning_output_tokens": 5881, "total_tokens": 2058349 }, "thread_token_usage": { "input_tokens": 2039608, "cached_input_tokens": 1952384, "cache_write_input_tokens": 0, "output_tokens": 18741, "reasoning_output_tokens": 5881, "total_tokens": 2058349 } } ### exec · 2026-09-23T22:05:10.375Z { "code": "const r = await tools.write_stdin({\"session_id\":23338,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:05:18.621Z Script completed Wall time 8.2 seconds Output: {(0, 0.35): np.float64(0.04275588699037706), (0, 0.4): np.float64(0.04280163750926149), (0, 0.45): np.float64(0.0425826261541442), (0, 0.5): np.float64(0.04324971563141847), (0, 0.55): np.float64(0.042807566562421434), (0, 0.6): np.float64(0.04273802368393976), (0.1, 0.35): np.float64(0.04296136496880433), (0.1, 0.4): np.float64(0.04301601566143784), (0.1, 0.45): np.float64(0.04271901432805338), (0.1, 0.5): np.float64(0.04324204767506915), (0.1, 0.55): np.float64(0.043275035357398746), (0.1, 0.6): np.float64(0.043138913448161405), (0.2, 0.35): np.float64(0.04296221500622499), (0.2, 0.4): np.float64(0.04305515693432684), (0.2, 0.45): np.float64(0.043005050127946906), (0.2, 0.5): np.float64(0.04341144028048732), (0.2, 0.55): np.float64(0.04352778304967383), (0.2, 0.6): np.float64(0.04333223350441557), (0.3, 0.35): np.float64(0.04334030088443875), (0.3, 0.4): np.float64(0.04340494210468909), (0.3, 0.45): np.float64(0.043007339683662715), (0.3, 0.5): np.float64(0.04327162519372053), (0.3, 0.55): np.float64(0.04343292934308344), (0.3, 0.6): np.float64(0.04345659361026105), (0.4, 0.35): np.float64(0.043360838000089594), (0.4, 0.4): np.float64(0.04338550642617269), (0.4, 0.45): np.float64(0.04310840553478589), (0.4, 0.5): np.float64(0.04332280668969803), (0.4, 0.55): np.float64(0.043270468751082904), (0.4, 0.6): np.float64(0.04290689777539133), (0.5, 0.35): np.float64(0.043069387955519324), (0.5, 0.4): np.float64(0.043509871421907854), (0.5, 0.45): np.float64(0.04313998201142284), (0.5, 0.5): np.float64(0.0428035231858546), (0.5, 0.55): np.float64(0.04271530926700752), (0.5, 0.6): np.float64(0.04232220620423817)} BEST (0.2, 0.55) 0.04352778304967383 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d11915087d2bd07c90d7c3278ba", "usage": { "input_tokens": 49957, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 50002 }, "turn_token_usage": { "input_tokens": 2089565, "cached_input_tokens": 2002048, "cache_write_input_tokens": 0, "output_tokens": 18786, "reasoning_output_tokens": 5881, "total_tokens": 2108351 }, "thread_token_usage": { "input_tokens": 2089565, "cached_input_tokens": 2002048, "cache_write_input_tokens": 0, "output_tokens": 18786, "reasoning_output_tokens": 5881, "total_tokens": 2108351 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fcd6d732-eb63-4ba5-b17a-7602b2914b74", "process_id": "23338", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/ensemble_validate.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/ensemble_validate.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{(0, 0.35): np.float64(0.04275588699037706), (0, 0.4): np.float64(0.04280163750926149), (0, 0.45): np.float64(0.0425826261541442), (0, 0.5): np.float64(0.04324971563141847), (0, 0.55): np.float64(0.042807566562421434), (0, 0.6): np.float64(0.04273802368393976), (0.1, 0.35): np.float64(0.04296136496880433), (0.1, 0.4): np.float64(0.04301601566143784), (0.1, 0.45): np.float64(0.04271901432805338), (0.1, 0.5): np.float64(0.04324204767506915), (0.1, 0.55): np.float64(0.043275035357398746), (0.1, 0.6): np.float64(0.043138913448161405), (0.2, 0.35): np.float64(0.04296221500622499), (0.2, 0.4): np.float64(0.04305515693432684), (0.2, 0.45): np.float64(0.043005050127946906), (0.2, 0.5): np.float64(0.04341144028048732), (0.2, 0.55): np.float64(0.04352778304967383), (0.2, 0.6): np.float64(0.04333223350441557), (0.3, 0.35): np.float64(0.04334030088443875), (0.3, 0.4): np.float64(0.04340494210468909), (0.3, 0.45): np.float64(0.043007339683662715), (0.3, 0.5): np.float64(0.04327162519372053), (0.3, 0.55): np.float64(0.04343292934308344), (0.3, 0.6): np.float64(0.04345659361026105), (0.4, 0.35): np.float64(0.043360838000089594), (0.4, 0.4): np.float64(0.04338550642617269), (0.4, 0.45): np.float64(0.04310840553478589), (0.4, 0.5): np.float64(0.04332280668969803), (0.4, 0.55): np.float64(0.043270468751082904), (0.4, 0.6): np.float64(0.04290689777539133), (0.5, 0.35): np.float64(0.043069387955519324), (0.5, 0.4): np.float64(0.043509871421907854), (0.5, 0.45): np.float64(0.04313998201142284), (0.5, 0.5): np.float64(0.0428035231858546), (0.5, 0.55): np.float64(0.04271530926700752), (0.5, 0.6): np.float64(0.04232220620423817)}\nBEST (0.2, 0.55) 0.04352778304967383\n", "stderr": "", "aggregated_output": "{(0, 0.35): np.float64(0.04275588699037706), (0, 0.4): np.float64(0.04280163750926149), (0, 0.45): np.float64(0.0425826261541442), (0, 0.5): np.float64(0.04324971563141847), (0, 0.55): np.float64(0.042807566562421434), (0, 0.6): np.float64(0.04273802368393976), (0.1, 0.35): np.float64(0.04296136496880433), (0.1, 0.4): np.float64(0.04301601566143784), (0.1, 0.45): np.float64(0.04271901432805338), (0.1, 0.5): np.float64(0.04324204767506915), (0.1, 0.55): np.float64(0.043275035357398746), (0.1, 0.6): np.float64(0.043138913448161405), (0.2, 0.35): np.float64(0.04296221500622499), (0.2, 0.4): np.float64(0.04305515693432684), (0.2, 0.45): np.float64(0.043005050127946906), (0.2, 0.5): np.float64(0.04341144028048732), (0.2, 0.55): np.float64(0.04352778304967383), (0.2, 0.6): np.float64(0.04333223350441557), (0.3, 0.35): np.float64(0.04334030088443875), (0.3, 0.4): np.float64(0.04340494210468909), (0.3, 0.45): np.float64(0.043007339683662715), (0.3, 0.5): np.float64(0.04327162519372053), (0.3, 0.55): np.float64(0.04343292934308344), (0.3, 0.6): np.float64(0.04345659361026105), (0.4, 0.35): np.float64(0.043360838000089594), (0.4, 0.4): np.float64(0.04338550642617269), (0.4, 0.45): np.float64(0.04310840553478589), (0.4, 0.5): np.float64(0.04332280668969803), (0.4, 0.55): np.float64(0.043270468751082904), (0.4, 0.6): np.float64(0.04290689777539133), (0.5, 0.35): np.float64(0.043069387955519324), (0.5, 0.4): np.float64(0.043509871421907854), (0.5, 0.45): np.float64(0.04313998201142284), (0.5, 0.5): np.float64(0.0428035231858546), (0.5, 0.55): np.float64(0.04271530926700752), (0.5, 0.6): np.float64(0.04232220620423817)}\nBEST (0.2, 0.55) 0.04352778304967383\n", "exit_code": 0, "duration": { "secs": 46, "nanos": 950338112 }, "formatted_output": "{(0, 0.35): np.float64(0.04275588699037706), (0, 0.4): np.float64(0.04280163750926149), (0, 0.45): np.float64(0.0425826261541442), (0, 0.5): np.float64(0.04324971563141847), (0, 0.55): np.float64(0.042807566562421434), (0, 0.6): np.float64(0.04273802368393976), (0.1, 0.35): np.float64(0.04296136496880433), (0.1, 0.4): np.float64(0.04301601566143784), (0.1, 0.45): np.float64(0.04271901432805338), (0.1, 0.5): np.float64(0.04324204767506915), (0.1, 0.55): np.float64(0.043275035357398746), (0.1, 0.6): np.float64(0.043138913448161405), (0.2, 0.35): np.float64(0.04296221500622499), (0.2, 0.4): np.float64(0.04305515693432684), (0.2, 0.45): np.float64(0.043005050127946906), (0.2, 0.5): np.float64(0.04341144028048732), (0.2, 0.55): np.float64(0.04352778304967383), (0.2, 0.6): np.float64(0.04333223350441557), (0.3, 0.35): np.float64(0.04334030088443875), (0.3, 0.4): np.float64(0.04340494210468909), (0.3, 0.45): np.float64(0.043007339683662715), (0.3, 0.5): np.float64(0.04327162519372053), (0.3, 0.55): np.float64(0.04343292934308344), (0.3, 0.6): np.float64(0.04345659361026105), (0.4, 0.35): np.float64(0.043360838000089594), (0.4, 0.4): np.float64(0.04338550642617269), (0.4, 0.45): np.float64(0.04310840553478589), (0.4, 0.5): np.float64(0.04332280668969803), (0.4, 0.55): np.float64(0.043270468751082904), (0.4, 0.6): np.float64(0.04290689777539133), (0.5, 0.35): np.float64(0.043069387955519324), (0.5, 0.4): np.float64(0.043509871421907854), (0.5, 0.45): np.float64(0.04313998201142284), (0.5, 0.5): np.float64(0.0428035231858546), (0.5, 0.55): np.float64(0.04271530926700752), (0.5, 0.6): np.float64(0.04232220620423817)}\nBEST (0.2, 0.55) 0.04352778304967383\n" } } ## Step 66 · agent · 2026-09-23T22:05:35.132Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:06:07.227Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/knn_dev.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\n+from scipy import sparse\\n+sys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets]);R=sparse.load_npz(O/'train_catalog_recent.npz')\\n+f=np.load(O/'factors_recent_l05_128.npz');U=f['U'];Un=U/(np.linalg.norm(U,axis=1,keepdims=True)+1e-8)\\n+sig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\\n+li=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+Ks=[10,25,50,100];alphas=[.05,.1,.2,.3,.4,.5,1];pred={(k,a):{} for k in Ks for a in alphas}\\n+for s in range(0,len(targets),128):\\n+ ids=np.arange(s,min(s+128,len(targets)));S=Un[tr[ids]]@Un.T;S[np.arange(len(ids)),tr[ids]]=-np.inf; top=np.argpartition(S,-max(Ks),axis=1)[:,-max(Ks):]\\n+ # order descending so first k are best\\n+ top=np.take_along_axis(top,np.argsort(-np.take_along_axis(S,top,axis=1),axis=1),axis=1)\\n+ for k in Ks:\\n+ tt=top[:,:k];rr=np.repeat(np.arange(len(ids)),k);cc=tt.ravel();vv=np.maximum(S[np.arange(len(ids))[:,None],tt],0).ravel();W=sparse.coo_matrix((vv,(rr,cc)),shape=(len(ids),len(users))).tocsr();P=(W@R).toarray();P=np.log1p(P);P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\\n+ for j,k0 in enumerate(ids):\\n+ u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\\n+ for a in alphas:\\n+ ss=base+a*P[j];ss[~bits]=-np.inf;q=np.argpartition(ss,-10)[-10:];q=q[np.argsort(-ss[q])];pred[(k,a)][u]=items[q].tolist()\\n+for q in pred:print(q,np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()]))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T22:06:07.334Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f38fd009-2551-46d9-b5c8-069242d06839", "changes": { "/app/solution/knn_dev.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets]);R=sparse.load_npz(O/'train_catalog_recent.npz')\nf=np.load(O/'factors_recent_l05_128.npz');U=f['U'];Un=U/(np.linalg.norm(U,axis=1,keepdims=True)+1e-8)\nsig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nKs=[10,25,50,100];alphas=[.05,.1,.2,.3,.4,.5,1];pred={(k,a):{} for k in Ks for a in alphas}\nfor s in range(0,len(targets),128):\n ids=np.arange(s,min(s+128,len(targets)));S=Un[tr[ids]]@Un.T;S[np.arange(len(ids)),tr[ids]]=-np.inf; top=np.argpartition(S,-max(Ks),axis=1)[:,-max(Ks):]\n # order descending so first k are best\n top=np.take_along_axis(top,np.argsort(-np.take_along_axis(S,top,axis=1),axis=1),axis=1)\n for k in Ks:\n tt=top[:,:k];rr=np.repeat(np.arange(len(ids)),k);cc=tt.ravel();vv=np.maximum(S[np.arange(len(ids))[:,None],tt],0).ravel();W=sparse.coo_matrix((vv,(rr,cc)),shape=(len(ids),len(users))).tocsr();P=(W@R).toarray();P=np.log1p(P);P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for a in alphas:\n ss=base+a*P[j];ss[~bits]=-np.inf;q=np.argpartition(ss,-10)[-10:];q=q[np.argsort(-ss[q])];pred[(k,a)][u]=items[q].tolist()\nfor q in pred:print(q,np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()]))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/knn_dev.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d1ed86c87d293c7a98481f4fc0d", "usage": { "input_tokens": 50863, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 1450, "reasoning_output_tokens": 438, "total_tokens": 52313 }, "turn_token_usage": { "input_tokens": 2140428, "cached_input_tokens": 2051840, "cache_write_input_tokens": 0, "output_tokens": 20236, "reasoning_output_tokens": 6319, "total_tokens": 2160664 }, "thread_token_usage": { "input_tokens": 2140428, "cached_input_tokens": 2051840, "cache_write_input_tokens": 0, "output_tokens": 20236, "reasoning_output_tokens": 6319, "total_tokens": 2160664 } } ## Step 67 · agent · 2026-09-23T22:06:10.487Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:06:12.349Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/knn_dev.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:06:13.517Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"6afb69","wall_time_seconds":1.00166425,"session_id":12314,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d4fb3d087d2ab0109c617e42714", "usage": { "input_tokens": 52337, "cached_input_tokens": 49280, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 6, "total_tokens": 52408 }, "turn_token_usage": { "input_tokens": 2192765, "cached_input_tokens": 2101120, "cache_write_input_tokens": 0, "output_tokens": 20307, "reasoning_output_tokens": 6325, "total_tokens": 2213072 }, "thread_token_usage": { "input_tokens": 2192765, "cached_input_tokens": 2101120, "cache_write_input_tokens": 0, "output_tokens": 20307, "reasoning_output_tokens": 6325, "total_tokens": 2213072 } } ## Step 68 · agent · 2026-09-23T22:06:16.162Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:06:17.377Z { "code": "const r = await tools.write_stdin({\"session_id\":12314,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:06:47.386Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d55cd5887d297f75c884401c385", "usage": { "input_tokens": 52465, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 52520 }, "turn_token_usage": { "input_tokens": 2245230, "cached_input_tokens": 2151808, "cache_write_input_tokens": 0, "output_tokens": 20362, "reasoning_output_tokens": 6333, "total_tokens": 2265592 }, "thread_token_usage": { "input_tokens": 2245230, "cached_input_tokens": 2151808, "cache_write_input_tokens": 0, "output_tokens": 20362, "reasoning_output_tokens": 6333, "total_tokens": 2265592 } } ### exec · 2026-09-23T22:06:49.684Z { "code": "const r = await tools.write_stdin({\"session_id\":12314,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:07:08.905Z Script completed Wall time 19.2 seconds Output: (10, 0.05) 0.03465434470328169 (10, 0.1) 0.036715402725943015 (10, 0.2) 0.03950738485536375 (10, 0.3) 0.040681184464971466 (10, 0.4) 0.040299721215947756 (10, 0.5) 0.03943160034042955 (10, 1) 0.03626193513011586 (25, 0.05) 0.034519583110816224 (25, 0.1) 0.03622027970763311 (25, 0.2) 0.03992271612718069 (25, 0.3) 0.041957582443468255 (25, 0.4) 0.04223714625806228 (25, 0.5) 0.04237298263214348 (25, 1) 0.040581470642788844 (50, 0.05) 0.034461702723219745 (50, 0.1) 0.03603546660408875 (50, 0.2) 0.039253691898145884 (50, 0.3) 0.04196273636689094 (50, 0.4) 0.04308357270081034 (50, 0.5) 0.0436310687427562 (50, 1) 0.041788231674374995 (100, 0.05) 0.03451180068900301 (100, 0.1) 0.035506550466723456 (100, 0.2) 0.038774263318461705 (100, 0.3) 0.04086417533987509 (100, 0.4) 0.042955478696419365 (100, 0.5) 0.044178202019656186 (100, 1) 0.04333608772234988 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d778f6087d2a269658e820a2916", "usage": { "input_tokens": 52543, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 52588 }, "turn_token_usage": { "input_tokens": 2297773, "cached_input_tokens": 2204032, "cache_write_input_tokens": 0, "output_tokens": 20407, "reasoning_output_tokens": 6333, "total_tokens": 2318180 }, "thread_token_usage": { "input_tokens": 2297773, "cached_input_tokens": 2204032, "cache_write_input_tokens": 0, "output_tokens": 20407, "reasoning_output_tokens": 6333, "total_tokens": 2318180 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-18e93d9d-1df6-40ff-a117-5f097e62ebf9", "process_id": "12314", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/knn_dev.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/knn_dev.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(10, 0.05) 0.03465434470328169\n(10, 0.1) 0.036715402725943015\n(10, 0.2) 0.03950738485536375\n(10, 0.3) 0.040681184464971466\n(10, 0.4) 0.040299721215947756\n(10, 0.5) 0.03943160034042955\n(10, 1) 0.03626193513011586\n(25, 0.05) 0.034519583110816224\n(25, 0.1) 0.03622027970763311\n(25, 0.2) 0.03992271612718069\n(25, 0.3) 0.041957582443468255\n(25, 0.4) 0.04223714625806228\n(25, 0.5) 0.04237298263214348\n(25, 1) 0.040581470642788844\n(50, 0.05) 0.034461702723219745\n(50, 0.1) 0.03603546660408875\n(50, 0.2) 0.039253691898145884\n(50, 0.3) 0.04196273636689094\n(50, 0.4) 0.04308357270081034\n(50, 0.5) 0.0436310687427562\n(50, 1) 0.041788231674374995\n(100, 0.05) 0.03451180068900301\n(100, 0.1) 0.035506550466723456\n(100, 0.2) 0.038774263318461705\n(100, 0.3) 0.04086417533987509\n(100, 0.4) 0.042955478696419365\n(100, 0.5) 0.044178202019656186\n(100, 1) 0.04333608772234988\n", "stderr": "", "aggregated_output": "(10, 0.05) 0.03465434470328169\n(10, 0.1) 0.036715402725943015\n(10, 0.2) 0.03950738485536375\n(10, 0.3) 0.040681184464971466\n(10, 0.4) 0.040299721215947756\n(10, 0.5) 0.03943160034042955\n(10, 1) 0.03626193513011586\n(25, 0.05) 0.034519583110816224\n(25, 0.1) 0.03622027970763311\n(25, 0.2) 0.03992271612718069\n(25, 0.3) 0.041957582443468255\n(25, 0.4) 0.04223714625806228\n(25, 0.5) 0.04237298263214348\n(25, 1) 0.040581470642788844\n(50, 0.05) 0.034461702723219745\n(50, 0.1) 0.03603546660408875\n(50, 0.2) 0.039253691898145884\n(50, 0.3) 0.04196273636689094\n(50, 0.4) 0.04308357270081034\n(50, 0.5) 0.0436310687427562\n(50, 1) 0.041788231674374995\n(100, 0.05) 0.03451180068900301\n(100, 0.1) 0.035506550466723456\n(100, 0.2) 0.038774263318461705\n(100, 0.3) 0.04086417533987509\n(100, 0.4) 0.042955478696419365\n(100, 0.5) 0.044178202019656186\n(100, 1) 0.04333608772234988\n", "exit_code": 0, "duration": { "secs": 56, "nanos": 388153866 }, "formatted_output": "(10, 0.05) 0.03465434470328169\n(10, 0.1) 0.036715402725943015\n(10, 0.2) 0.03950738485536375\n(10, 0.3) 0.040681184464971466\n(10, 0.4) 0.040299721215947756\n(10, 0.5) 0.03943160034042955\n(10, 1) 0.03626193513011586\n(25, 0.05) 0.034519583110816224\n(25, 0.1) 0.03622027970763311\n(25, 0.2) 0.03992271612718069\n(25, 0.3) 0.041957582443468255\n(25, 0.4) 0.04223714625806228\n(25, 0.5) 0.04237298263214348\n(25, 1) 0.040581470642788844\n(50, 0.05) 0.034461702723219745\n(50, 0.1) 0.03603546660408875\n(50, 0.2) 0.039253691898145884\n(50, 0.3) 0.04196273636689094\n(50, 0.4) 0.04308357270081034\n(50, 0.5) 0.0436310687427562\n(50, 1) 0.041788231674374995\n(100, 0.05) 0.03451180068900301\n(100, 0.1) 0.035506550466723456\n(100, 0.2) 0.038774263318461705\n(100, 0.3) 0.04086417533987509\n(100, 0.4) 0.042955478696419365\n(100, 0.5) 0.044178202019656186\n(100, 1) 0.04333608772234988\n" } } ## Step 69 · agent · 2026-09-23T22:07:11.812Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:16.038Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/knn_dev.py\\n@@\\n-Ks=[10,25,50,100];alphas=[.05,.1,.2,.3,.4,.5,1];pred={(k,a):{} for k in Ks for a in alphas}\\n+Ks=[75,100,150,200,300];alphas=[.45,.5,.55,.6,.7,.8,1];pred={(k,a):{} for k in Ks for a in alphas}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T22:07:16.132Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ef08e734-4fde-4ebe-b1e6-bdff1d76c866", "changes": { "/app/solution/knn_dev.py": { "type": "update", "unified_diff": "@@ -13,3 +13,3 @@\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\n-Ks=[10,25,50,100];alphas=[.05,.1,.2,.3,.4,.5,1];pred={(k,a):{} for k in Ks for a in alphas}\n+Ks=[75,100,150,200,300];alphas=[.45,.5,.55,.6,.7,.8,1];pred={(k,a):{} for k in Ks for a in alphas}\n for s in range(0,len(targets),128):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/knn_dev.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d8d1d3087d2a1e58ce47b867b99", "usage": { "input_tokens": 53107, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 192, "reasoning_output_tokens": 50, "total_tokens": 53299 }, "turn_token_usage": { "input_tokens": 2350880, "cached_input_tokens": 2256384, "cache_write_input_tokens": 0, "output_tokens": 20599, "reasoning_output_tokens": 6383, "total_tokens": 2371479 }, "thread_token_usage": { "input_tokens": 2350880, "cached_input_tokens": 2256384, "cache_write_input_tokens": 0, "output_tokens": 20599, "reasoning_output_tokens": 6383, "total_tokens": 2371479 } } ### exec · 2026-09-23T22:07:19.347Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/knn_dev.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:07:20.569Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"196dfd","wall_time_seconds":1.001814728,"session_id":12764,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d948db887d2a61b342b112ad4c7", "usage": { "input_tokens": 53323, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 0, "total_tokens": 53386 }, "turn_token_usage": { "input_tokens": 2404203, "cached_input_tokens": 2308480, "cache_write_input_tokens": 0, "output_tokens": 20662, "reasoning_output_tokens": 6383, "total_tokens": 2424865 }, "thread_token_usage": { "input_tokens": 2404203, "cached_input_tokens": 2308480, "cache_write_input_tokens": 0, "output_tokens": 20662, "reasoning_output_tokens": 6383, "total_tokens": 2424865 } } ## Step 70 · agent · 2026-09-23T22:07:22.215Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:23.431Z { "code": "const r = await tools.write_stdin({\"session_id\":12764,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:07:53.441Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44d98d56887d292a1ec3fd133bbba", "usage": { "input_tokens": 53443, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 53496 }, "turn_token_usage": { "input_tokens": 2457646, "cached_input_tokens": 2361472, "cache_write_input_tokens": 0, "output_tokens": 20715, "reasoning_output_tokens": 6389, "total_tokens": 2478361 }, "thread_token_usage": { "input_tokens": 2457646, "cached_input_tokens": 2361472, "cache_write_input_tokens": 0, "output_tokens": 20715, "reasoning_output_tokens": 6389, "total_tokens": 2478361 } } ### exec · 2026-09-23T22:07:56.422Z { "code": "const r = await tools.write_stdin({\"session_id\":12764,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:08:26.433Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44db9b03487d28cd795ce359bf0d0", "usage": { "input_tokens": 53519, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 53564 }, "turn_token_usage": { "input_tokens": 2511165, "cached_input_tokens": 2414720, "cache_write_input_tokens": 0, "output_tokens": 20760, "reasoning_output_tokens": 6389, "total_tokens": 2531925 }, "thread_token_usage": { "input_tokens": 2511165, "cached_input_tokens": 2414720, "cache_write_input_tokens": 0, "output_tokens": 20760, "reasoning_output_tokens": 6389, "total_tokens": 2531925 } } ### exec · 2026-09-23T22:08:29.909Z { "code": "const r = await tools.write_stdin({\"session_id\":12764,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:08:46.099Z Script completed Wall time 16.2 seconds Output: (75, 0.45) 0.04364232838835271 (75, 0.5) 0.0438363380901075 (75, 0.55) 0.04388978012514264 (75, 0.6) 0.044278833874293294 (75, 0.7) 0.04365293508636747 (75, 0.8) 0.04314777325895937 (75, 1) 0.04277207217298763 (100, 0.45) 0.043862180011523325 (100, 0.5) 0.044178202019656186 (100, 0.55) 0.04417208342852022 (100, 0.6) 0.04422597842599453 (100, 0.7) 0.0440342011101129 (100, 0.8) 0.043937842685466855 (100, 1) 0.04333608772234988 (150, 0.45) 0.042765013002109754 (150, 0.5) 0.043483684955246585 (150, 0.55) 0.04386093522679404 (150, 0.6) 0.04376099854217901 (150, 0.7) 0.04417167149723526 (150, 0.8) 0.043403655278331794 (150, 1) 0.04299063887058207 (200, 0.45) 0.04180146123535964 (200, 0.5) 0.042551034633634244 (200, 0.55) 0.043271445709535214 (200, 0.6) 0.04371902441226777 (200, 0.7) 0.043701939438996106 (200, 0.8) 0.04351276124508602 (200, 1) 0.042946132748604475 (300, 0.45) 0.04084752941513144 (300, 0.5) 0.041555156207404235 (300, 0.55) 0.04192996047008835 (300, 0.6) 0.0422360554912812 (300, 0.7) 0.04295145664216197 (300, 0.8) 0.042858591039782026 (300, 1) 0.04237114799861671 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44ddaaa1487d2847fdbd87134e6be", "usage": { "input_tokens": 53587, "cached_input_tokens": 53376, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 53632 }, "turn_token_usage": { "input_tokens": 2564752, "cached_input_tokens": 2468096, "cache_write_input_tokens": 0, "output_tokens": 20805, "reasoning_output_tokens": 6389, "total_tokens": 2585557 }, "thread_token_usage": { "input_tokens": 2564752, "cached_input_tokens": 2468096, "cache_write_input_tokens": 0, "output_tokens": 20805, "reasoning_output_tokens": 6389, "total_tokens": 2585557 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4e2dbbf9-6ead-494b-aa81-801c8cf7168f", "process_id": "12764", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/knn_dev.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/knn_dev.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(75, 0.45) 0.04364232838835271\n(75, 0.5) 0.0438363380901075\n(75, 0.55) 0.04388978012514264\n(75, 0.6) 0.044278833874293294\n(75, 0.7) 0.04365293508636747\n(75, 0.8) 0.04314777325895937\n(75, 1) 0.04277207217298763\n(100, 0.45) 0.043862180011523325\n(100, 0.5) 0.044178202019656186\n(100, 0.55) 0.04417208342852022\n(100, 0.6) 0.04422597842599453\n(100, 0.7) 0.0440342011101129\n(100, 0.8) 0.043937842685466855\n(100, 1) 0.04333608772234988\n(150, 0.45) 0.042765013002109754\n(150, 0.5) 0.043483684955246585\n(150, 0.55) 0.04386093522679404\n(150, 0.6) 0.04376099854217901\n(150, 0.7) 0.04417167149723526\n(150, 0.8) 0.043403655278331794\n(150, 1) 0.04299063887058207\n(200, 0.45) 0.04180146123535964\n(200, 0.5) 0.042551034633634244\n(200, 0.55) 0.043271445709535214\n(200, 0.6) 0.04371902441226777\n(200, 0.7) 0.043701939438996106\n(200, 0.8) 0.04351276124508602\n(200, 1) 0.042946132748604475\n(300, 0.45) 0.04084752941513144\n(300, 0.5) 0.041555156207404235\n(300, 0.55) 0.04192996047008835\n(300, 0.6) 0.0422360554912812\n(300, 0.7) 0.04295145664216197\n(300, 0.8) 0.042858591039782026\n(300, 1) 0.04237114799861671\n", "stderr": "", "aggregated_output": "(75, 0.45) 0.04364232838835271\n(75, 0.5) 0.0438363380901075\n(75, 0.55) 0.04388978012514264\n(75, 0.6) 0.044278833874293294\n(75, 0.7) 0.04365293508636747\n(75, 0.8) 0.04314777325895937\n(75, 1) 0.04277207217298763\n(100, 0.45) 0.043862180011523325\n(100, 0.5) 0.044178202019656186\n(100, 0.55) 0.04417208342852022\n(100, 0.6) 0.04422597842599453\n(100, 0.7) 0.0440342011101129\n(100, 0.8) 0.043937842685466855\n(100, 1) 0.04333608772234988\n(150, 0.45) 0.042765013002109754\n(150, 0.5) 0.043483684955246585\n(150, 0.55) 0.04386093522679404\n(150, 0.6) 0.04376099854217901\n(150, 0.7) 0.04417167149723526\n(150, 0.8) 0.043403655278331794\n(150, 1) 0.04299063887058207\n(200, 0.45) 0.04180146123535964\n(200, 0.5) 0.042551034633634244\n(200, 0.55) 0.043271445709535214\n(200, 0.6) 0.04371902441226777\n(200, 0.7) 0.043701939438996106\n(200, 0.8) 0.04351276124508602\n(200, 1) 0.042946132748604475\n(300, 0.45) 0.04084752941513144\n(300, 0.5) 0.041555156207404235\n(300, 0.55) 0.04192996047008835\n(300, 0.6) 0.0422360554912812\n(300, 0.7) 0.04295145664216197\n(300, 0.8) 0.042858591039782026\n(300, 1) 0.04237114799861671\n", "exit_code": 0, "duration": { "secs": 86, "nanos": 529880417 }, "formatted_output": "(75, 0.45) 0.04364232838835271\n(75, 0.5) 0.0438363380901075\n(75, 0.55) 0.04388978012514264\n(75, 0.6) 0.044278833874293294\n(75, 0.7) 0.04365293508636747\n(75, 0.8) 0.04314777325895937\n(75, 1) 0.04277207217298763\n(100, 0.45) 0.043862180011523325\n(100, 0.5) 0.044178202019656186\n(100, 0.55) 0.04417208342852022\n(100, 0.6) 0.04422597842599453\n(100, 0.7) 0.0440342011101129\n(100, 0.8) 0.043937842685466855\n(100, 1) 0.04333608772234988\n(150, 0.45) 0.042765013002109754\n(150, 0.5) 0.043483684955246585\n(150, 0.55) 0.04386093522679404\n(150, 0.6) 0.04376099854217901\n(150, 0.7) 0.04417167149723526\n(150, 0.8) 0.043403655278331794\n(150, 1) 0.04299063887058207\n(200, 0.45) 0.04180146123535964\n(200, 0.5) 0.042551034633634244\n(200, 0.55) 0.043271445709535214\n(200, 0.6) 0.04371902441226777\n(200, 0.7) 0.043701939438996106\n(200, 0.8) 0.04351276124508602\n(200, 1) 0.042946132748604475\n(300, 0.45) 0.04084752941513144\n(300, 0.5) 0.041555156207404235\n(300, 0.55) 0.04192996047008835\n(300, 0.6) 0.0422360554912812\n(300, 0.7) 0.04295145664216197\n(300, 0.8) 0.042858591039782026\n(300, 1) 0.04237114799861671\n" } } ## Step 71 · agent · 2026-09-23T22:08:54.782Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:30.418Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/final_blend_dev.py\\n+from pathlib import Path\\n+import sys,numpy as np,pandas as pd,pyarrow.parquet as pq\\n+from scipy import sparse\\n+sys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\\n+D=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+users=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets]);R=sparse.load_npz(O/'train_catalog_recent.npz')\\n+f0=np.load(O/'factors_recent_l05_128.npz');U0,V0=f0['U'],f0['V'];f1=np.load(O/'factors_countlikes_256.npz');U1,V1=f1['U'],f1['V'];Un=U0/(np.linalg.norm(U0,axis=1,keepdims=True)+1e-8)\\n+sig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\\n+li=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\\n+for u,i,g in rel.itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=ic.get(int(i));\\n+ if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\\n+AA=[.2,.3,.4,.5,.6,.7];BB=[0,.1,.2,.3,.4,.5];pred={(a,b):{} for a in AA for b in BB};K=75\\n+for s in range(0,len(targets),128):\\n+ ids=np.arange(s,min(s+128,len(targets)));S=Un[tr[ids]]@Un.T;S[np.arange(len(ids)),tr[ids]]=-np.inf;top=np.argpartition(S,-K,axis=1)[:,-K:];rr=np.repeat(np.arange(len(ids)),K);cc=top.ravel();vv=np.maximum(S[np.arange(len(ids))[:,None],top],0).ravel();W=sparse.coo_matrix((vv,(rr,cc)),shape=(len(ids),len(users))).tocsr();PK=np.log1p((W@R).toarray());PK=(PK-PK.mean(1,keepdims=True))/(PK.std(1,keepdims=True)+1e-8)\\n+ P0=U0[tr[ids]]@V0;P0=(P0-P0.mean(1,keepdims=True))/(P0.std(1,keepdims=True)+1e-8);P1=U1[tr[ids]]@V1;P1=(P1-P1.mean(1,keepdims=True))/(P1.std(1,keepdims=True)+1e-8);PF=.8*P0+.2*P1\\n+ for j,k0 in enumerate(ids):\\n+ u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\\n+ for a in AA:\\n+ for b in BB:\\n+ ss=base+a*PK[j]+b*PF[j];ss[~bits]=-np.inf;q=np.argpartition(ss,-10)[-10:];q=q[np.argsort(-ss[q])];pred[(a,b)][u]=items[q].tolist()\\n+vals={q:np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()]) for q in pred};print(vals);print('BEST',max(vals,key=vals.get),max(vals.values()))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-23T22:09:30.486Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-618215f7-a07b-445f-8510-e9ad37ea3793", "changes": { "/app/solution/final_blend_dev.py": { "type": "add", "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets]);R=sparse.load_npz(O/'train_catalog_recent.npz')\nf0=np.load(O/'factors_recent_l05_128.npz');U0,V0=f0['U'],f0['V'];f1=np.load(O/'factors_countlikes_256.npz');U1,V1=f1['U'],f1['V'];Un=U0/(np.linalg.norm(U0,axis=1,keepdims=True)+1e-8)\nsig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nAA=[.2,.3,.4,.5,.6,.7];BB=[0,.1,.2,.3,.4,.5];pred={(a,b):{} for a in AA for b in BB};K=75\nfor s in range(0,len(targets),128):\n ids=np.arange(s,min(s+128,len(targets)));S=Un[tr[ids]]@Un.T;S[np.arange(len(ids)),tr[ids]]=-np.inf;top=np.argpartition(S,-K,axis=1)[:,-K:];rr=np.repeat(np.arange(len(ids)),K);cc=top.ravel();vv=np.maximum(S[np.arange(len(ids))[:,None],top],0).ravel();W=sparse.coo_matrix((vv,(rr,cc)),shape=(len(ids),len(users))).tocsr();PK=np.log1p((W@R).toarray());PK=(PK-PK.mean(1,keepdims=True))/(PK.std(1,keepdims=True)+1e-8)\n P0=U0[tr[ids]]@V0;P0=(P0-P0.mean(1,keepdims=True))/(P0.std(1,keepdims=True)+1e-8);P1=U1[tr[ids]]@V1;P1=(P1-P1.mean(1,keepdims=True))/(P1.std(1,keepdims=True)+1e-8);PF=.8*P0+.2*P1\n for j,k0 in enumerate(ids):\n u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n for a in AA:\n for b in BB:\n ss=base+a*PK[j]+b*PF[j];ss[~bits]=-np.inf;q=np.argpartition(ss,-10)[-10:];q=q[np.argsort(-ss[q])];pred[(a,b)][u]=items[q].tolist()\nvals={q:np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()]) for q in pred};print(vals);print('BEST',max(vals,key=vals.get),max(vals.values()))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/final_blend_dev.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44dee52c487d2abeb27ac04640f73", "usage": { "input_tokens": 54275, "cached_input_tokens": 53120, "cache_write_input_tokens": 0, "output_tokens": 1324, "reasoning_output_tokens": 202, "total_tokens": 55599 }, "turn_token_usage": { "input_tokens": 2619027, "cached_input_tokens": 2521216, "cache_write_input_tokens": 0, "output_tokens": 22129, "reasoning_output_tokens": 6591, "total_tokens": 2641156 }, "thread_token_usage": { "input_tokens": 2619027, "cached_input_tokens": 2521216, "cache_write_input_tokens": 0, "output_tokens": 22129, "reasoning_output_tokens": 6591, "total_tokens": 2641156 } } ### exec · 2026-09-23T22:09:34.068Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/final_blend_dev.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" } ### Tool output · 2026-09-23T22:09:35.268Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5dab5a","wall_time_seconds":1.001529357,"session_id":67679,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44e1ad75887d29d57623529713959", "usage": { "input_tokens": 55623, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 55687 }, "turn_token_usage": { "input_tokens": 2674650, "cached_input_tokens": 2575360, "cache_write_input_tokens": 0, "output_tokens": 22193, "reasoning_output_tokens": 6591, "total_tokens": 2696843 }, "thread_token_usage": { "input_tokens": 2674650, "cached_input_tokens": 2575360, "cache_write_input_tokens": 0, "output_tokens": 22193, "reasoning_output_tokens": 6591, "total_tokens": 2696843 } } ## Step 72 · agent · 2026-09-23T22:09:38.048Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:39.306Z { "code": "const r = await tools.write_stdin({\"session_id\":67679,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:10:09.314Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44e1f89ac87d2b4fd9cc04aaf36e1", "usage": { "input_tokens": 55745, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 55798 }, "turn_token_usage": { "input_tokens": 2730395, "cached_input_tokens": 2630784, "cache_write_input_tokens": 0, "output_tokens": 22246, "reasoning_output_tokens": 6597, "total_tokens": 2752641 }, "thread_token_usage": { "input_tokens": 2730395, "cached_input_tokens": 2630784, "cache_write_input_tokens": 0, "output_tokens": 22246, "reasoning_output_tokens": 6597, "total_tokens": 2752641 } } ### exec · 2026-09-23T22:10:12.062Z { "code": "const r = await tools.write_stdin({\"session_id\":67679,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-09-23T22:10:40.263Z Script completed Wall time 28.2 seconds Output: {(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)} BEST (0.4, 0.3) 0.0458792467708756 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "session_id": "01a0d037-7158-7670-9f20-e7a2602bb363", "root_turn_id": "01a0d037-7167-72d2-9d55-cae3533120d0", "response_id": "resp_06040fb8d608de6e016ab44e417d5887d29705806ec75b7b6c", "usage": { "input_tokens": 55821, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 55866 }, "turn_token_usage": { "input_tokens": 2786216, "cached_input_tokens": 2686336, "cache_write_input_tokens": 0, "output_tokens": 22291, "reasoning_output_tokens": 6597, "total_tokens": 2808507 }, "thread_token_usage": { "input_tokens": 2786216, "cached_input_tokens": 2686336, "cache_write_input_tokens": 0, "output_tokens": 22291, "reasoning_output_tokens": 6597, "total_tokens": 2808507 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8ebbcc2c-4503-4d72-8899-9f61972a07f6", "process_id": "67679", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/final_blend_dev.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/final_blend_dev.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n", "stderr": "", "aggregated_output": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n", "exit_code": 0, "duration": { "secs": 65, "nanos": 995183635 }, "formatted_output": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n" } } ## Step 73 · agent · 2026-09-23T22:10:59.660Z ### Thinking unavailable — no text recorded ## Step 74 · agent · 2026-09-23T22:11:03.596Z ### Thinking unavailable — no text recorded