yambda-discovery-ranking-v01 / sol-high-0.147.0-exact-r04-2026-09-24 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T17:36:24.508Z 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 · 2026-09-24T17:36:26.437Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### 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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$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 · 2026-09-24T17:36:26.437Z 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 · 2026-09-24T17:36:26.437Z 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 · 2026-09-24T17:36:26.437Z 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 2026-09-24 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-24", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "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": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d47d-6bb2-7190-b9f8-63502a35856d", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "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 · 2026-09-24T17:36:26.447Z # 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 · 2026-09-24T17:36:28.236Z ### Thinking unavailable — no text recorded I’ll first profile the event volumes, catalog/eligibility shapes, and installed libraries. Then I’ll build a leakage-safe validation pipeline, iterate against the public evaluator, and refit the strongest approach with validation history before writing the final 100-per-user artifact. I’ll keep a valid baseline submission in place early. ### exec · 2026-09-24T17:36:33.122Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Profile data, environment, and evaluation mechanics\",\"status\":\"in_progress\"},\n {step:\"Build a valid baseline validation and test ranking\",\"status\":\"pending\"},\n {step:\"Develop and tune stronger ranking features on public validation\",\"status\":\"pending\"},\n {step:\"Refit on train plus validation and generate final submission\",\"status\":\"pending\"},\n {step:\"Verify schema, eligibility, reproducibility, and document approach\",\"status\":\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"ls -lah /task/data && sed -n '1,220p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:36:33.200Z Script completed Wall time 0.1 seconds Output: total 340M dr-xr-xr-x 1 root root 4.0K Sep 24 17:29 . dr-xr-xr-x 1 root root 4.0K Sep 24 17:33 .. -r--r--r-- 1 root root 342K Sep 24 17:29 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 24 17:29 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 24 17:29 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 24 17:29 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 24 17:29 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 24 17:29 train_likes.parquet -r--r--r-- 1 root root 305M Sep 24 17:29 train_listens.parquet -r--r--r-- 1 root root 153K Sep 24 17:29 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 24 17:29 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 24 17:29 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 24 17:29 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 24 17:29 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 24 17:29 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 24 17:29 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 24 17:29 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 24 17:29 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( #!/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 ## Step 8 · agent · 2026-09-24T17:36:36.288Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:43.148Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print(p.name, d.shape, d.dtypes.to_dict())\\n if 'timestamp' in d:\\n print(' range', int(d.timestamp.min()), int(d.timestamp.max()), 'users',d.uid.nunique(),'items',d.item_id.nunique(),'organic',d.is_organic.value_counts().to_dict())\\nprint('\\\\nNPZ')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n u=z['uids']; it=z['item_ids']; pack=z['packed_eligible']\\n cnt=np.unpackbits(pack,axis=1,count=len(it),bitorder='little').sum(1)\\n print(n,u.shape,it.shape,pack.shape,'eligible min/mean/max',cnt.min(),cnt.mean(),cnt.max(),'same catalog order',np.array_equal(it,pd.read_parquet(D/'candidate_catalog.parquet').item_id.values))\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nfor n in ['validation_preference_state.parquet','test_preference_state.parquet']:\\n d=pd.read_parquet(D/n); print(n,d.shape,d.head(),d.state.value_counts().to_dict(),d.groupby(['uid','item_id']).size().value_counts().head().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:36:43.303Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-09-24T17:36:45.523Z ### Thinking unavailable — no text recorded The default shell’s `python` is not the task’s pinned runtime, so I’m locating the provided environment before touching the modeling code. Once found, I’ll use that interpreter consistently for both building and public scoring. ### exec · 2026-09-24T17:36:48.290Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 uv || true\\nls -lah /opt /venv /task 2>/dev/null\\nfind / -path '*/site-packages/pandas' -type d 2>/dev/null | head -20\\nhead -1 /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:36:48.750Z Script completed Wall time 0.5 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 /opt: total 12K drwxr-xr-x 1 root root 4.0K Sep 24 17:32 . drwxr-xr-x 1 root root 4.0K Sep 24 17:36 .. dr-xr-xr-x 1 root root 4.0K Sep 24 17:33 task-venv /task: total 36K dr-xr-xr-x 1 root root 4.0K Sep 24 17:33 . drwxr-xr-x 1 root root 4.0K Sep 24 17:36 .. dr-xr-xr-x 1 root root 4.0K Sep 24 17:29 data -r--r--r-- 1 root root 5.7K Sep 24 17:28 evaluate_public.py -r--r--r-- 1 root root 8.1K Sep 24 17:28 ranking_evaluation.py -r--r--r-- 1 root root 1.3K Sep 24 17:28 requirements.txt /opt/task-venv/lib/python3.12/site-packages/pandas #!/usr/bin/env python3 ## Step 10 · agent · 2026-09-24T17:36:50.696Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:02.676Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print(p.name, d.shape, d.dtypes.to_dict())\\n if 'timestamp' in d:\\n print(' range', int(d.timestamp.min()), int(d.timestamp.max()), 'users',d.uid.nunique(),'items',d.item_id.nunique(),'organic',d.is_organic.value_counts().to_dict())\\nprint('\\\\nNPZ')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n u=z['uids']; it=z['item_ids']; pack=z['packed_eligible']\\n cnt=np.unpackbits(pack,axis=1,count=len(it),bitorder='little').sum(1)\\n print(n,u.shape,it.shape,pack.shape,'eligible min/mean/max',cnt.min(),cnt.mean(),cnt.max(),'same catalog order',np.array_equal(it,cat.item_id.values))\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nfor n in ['validation_preference_state.parquet','test_preference_state.parquet']:\\n d=pd.read_parquet(D/n); print(n,d.shape); print(d.head()); print(d.state.value_counts().to_dict(),d.groupby(['uid','item_id']).size().value_counts().head().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:05.712Z Script completed Wall time 3.0 seconds Output: candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 75 24786580 users 5732 items 50876 organic {1: 52278, 0: 48797} train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 80 24786790 users 8144 items 172303 organic {1: 461839, 0: 352992} train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} range 0 24786795 users 9238 items 845395 organic {1: 22477622, 0: 21006537} train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 15950 24785395 users 2769 items 14725 organic {1: 17751, 0: 2209} train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 250 24786570 users 6197 items 113072 organic {1: 277065, 0: 15600} val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 24789920 25393280 users 955 items 3135 organic {1: 1835, 0: 1728} val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 24788815 25393390 users 4114 items 22902 organic {1: 25181, 0: 12614} val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} range 24788600 25393395 users 8196 items 191747 organic {1: 776486, 0: 725211} val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 24810715 25393035 users 205 items 579 organic {1: 527, 0: 79} val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} range 24789005 25393385 users 1313 items 8080 organic {1: 8905, 0: 441} validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} NPZ validation_eligible_candidates.npz (8456,) (30000,) (8456, 3750) eligible min/mean/max 24657 29067.306291390727 30000 same catalog order True test_eligible_candidates.npz (8456,) (30000,) (8456, 3750) eligible min/mean/max 24552 29041.3178807947 30000 same catalog order True targets uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 (8456, 3) 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 {'liked': 418435, 'disliked': 50689} {1: 465704, 2: 1710} 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 {'liked': 432659, 'disliked': 52117} {1: 481224, 2: 1776} ## Step 11 · agent · 2026-09-24T17:37:14.218Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:30.915Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\ntruth=pd.concat([vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].assign(g=3)])\\ntruth=truth[truth.item_id.isin(cat)].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nz=np.load(D/'validation_eligible_candidates.npz'); col={x:i for i,x in enumerate(z['item_ids'])}; row={x:i for i,x in enumerate(z['uids'])}\\ndef elig(u,i):\\n c=col.get(i,-1); r=row.get(u,-1); return c>=0 and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\\nmask=np.fromiter((elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)),bool,len(truth)); truth=truth[mask]\\nprint('truth',len(truth),'users',truth.uid.nunique(),'grade',truth.g.value_counts().to_dict(),'peruser',truth.groupby('uid').size().describe().to_dict())\\n# Load candidate/target historical listens subset\\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(cat)]\\nprint('train target-catalog listens',tr.shape,'pairs',tr[['uid','item_id']].drop_duplicates().shape)\\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),pos=('played_ratio_pct',lambda x:(x>=50).sum()),strong=('played_ratio_pct',lambda x:(x>=80).sum()),org=('is_organic','sum')).reset_index()\\nx=truth.merge(pairs,on=['uid','item_id'],how='left')\\nprint('truth historically listened',x.n.notna().mean(),'by grade',x.groupby('g').n.apply(lambda s:s.notna().mean()).to_dict())\\nprint('hist count truth',x.n.describe().to_dict())\\n# preference state overlap\\nps=pd.read_parquet(D/'validation_preference_state.parquet')\\nfor st in ['liked','disliked']:\\n ss=ps[ps.state==st][['uid','item_id']].drop_duplicates().assign(**{st:1})\\n x=x.merge(ss,on=['uid','item_id'],how='left')\\n print(st,x[st].notna().mean(),x.groupby('g')[st].apply(lambda s:s.notna().mean()).to_dict())\\n# coverage by recency / popular top global\\nfor days in [7,14,30,60,90,180]:\\n cutoff=24786800-days*86400\\n seen=set(zip(tr.loc[tr.timestamp>=cutoff,'uid'].astype(int),tr.loc[tr.timestamp>=cutoff,'item_id'].astype(int)))\\n print('truth seen days',days,np.mean([(int(u),int(i)) in seen for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]), 'pairs',len(seen))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id, output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:37:41.936Z Script running with cell ID 5 Wall time 11.0 seconds Output: ## Step 12 · agent · 2026-09-24T17:37:43.680Z ### Thinking unavailable — no text recorded The main profiling pass is now aggregating target-user history over the 43M listens. I’m specifically measuring how much validation relevance is repeat consumption versus genuinely unseen discovery, because that determines whether recurrence/recency, collaborative similarity, or popularity should dominate the top 10. ### wait · 2026-09-24T17:37:45.313Z { "cell_id": "5", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:38:01.135Z Script completed Wall time 15.8 seconds Output: {"session_id":84245,"output":""} ## Step 13 · agent · 2026-09-24T17:38:03.264Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:03.959Z { "code": "const r = await tools.write_stdin({session_id:84245,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:38:14.975Z Script running with cell ID 6 Wall time 11.0 seconds Output: ## Step 14 · agent · 2026-09-24T17:38:16.407Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:38:16.826Z { "cell_id": "6", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:38:33.979Z Script completed Wall time 17.2 seconds Output: ## Step 15 · agent · 2026-09-24T17:38:35.884Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:37.117Z { "code": "const r = await tools.write_stdin({session_id:84245,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:38:48.136Z Script running with cell ID 7 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:38:50.617Z { "cell_id": "7", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:39:07.140Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"ded7b3","wall_time_seconds":30.001020905,"session_id":84245,"original_token_count":0,"output":""} ## Step 16 · agent · 2026-09-24T17:39:08.993Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:09.664Z { "code": "const r = await tools.write_stdin({session_id:84245,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:39:20.683Z Script running with cell ID 8 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:39:23.829Z { "cell_id": "8", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:39:39.687Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"c32a1a","wall_time_seconds":30.001724984,"session_id":84245,"original_token_count":0,"output":""} ## Step 17 · agent · 2026-09-24T17:39:43.039Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:43.710Z { "code": "const r = await tools.write_stdin({session_id:84245,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:39:54.728Z Script running with cell ID 9 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:39:56.504Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:40:13.732Z Script completed Wall time 17.2 seconds Output: {"chunk_id":"fc4e47","wall_time_seconds":30.00164495,"session_id":84245,"original_token_count":0,"output":""} ## Step 18 · agent · 2026-09-24T17:40:14.754Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:15.505Z { "code": "const r = await tools.write_stdin({session_id:84245,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:40:15.602Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 12, in File "", line 12, in File "", line 11, in elig 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 ## Step 19 · agent · 2026-09-24T17:40:17.585Z ### Thinking unavailable — no text recorded I found an avoidable I/O trap in the first diagnostic: repeated access to a compressed NPZ member was decompressing the eligibility matrix for every label. I stopped it, will load the matrix once, and will use vectorized group-bys from here; no modeling conclusion was affected. ### exec · 2026-09-24T17:40:35.512Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(); catdf=pd.read_parquet(D/'candidate_catalog.parquet'); cats=catdf.item_id.to_numpy()\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\ntruth=pd.concat([vl[np.isin(vl.uid,targets)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[np.isin(vk.uid,targets)&(vk.is_organic==1)][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nwith np.load(D/'validation_eligible_candidates.npz') as z: zu=z['uids'].copy(); zi=z['item_ids'].copy(); zp=z['packed_eligible'].copy()\\nridx=pd.Series(np.arange(len(zu)),index=zu); cidx=pd.Series(np.arange(len(zi)),index=zi)\\nrr=ridx.reindex(truth.uid).to_numpy(); cc=cidx.reindex(truth.item_id).to_numpy(); ok=~np.isnan(rr)&~np.isnan(cc); ix=np.where(ok)[0]; ok[ix]=((zp[rr[ix].astype(int),cc[ix].astype(int)//8]>>(cc[ix].astype(int)%8))&1).astype(bool); truth=truth[ok]\\nprint('truth',len(truth),'users',truth.uid.nunique(),'grade',truth.g.value_counts().to_dict(),'peruser',truth.groupby('uid').size().describe().to_dict(),flush=True)\\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[np.isin(tr.uid,targets)&np.isin(tr.item_id,cats)]\\nprint('train subset',tr.shape,flush=True)\\ntr['pos']=(tr.played_ratio_pct>=50).astype('uint8'); tr['strong']=(tr.played_ratio_pct>=80).astype('uint8'); tr['orgstrong']=((tr.played_ratio_pct>=80)&(tr.is_organic==1)).astype('uint8')\\npairs=tr.groupby(['uid','item_id'],sort=False,observed=True).agg(n=('timestamp','size'),last=('timestamp','max'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum')).reset_index()\\nprint('pairs',pairs.shape,flush=True)\\nx=truth.merge(pairs,on=['uid','item_id'],how='left'); print('hist listened',x.n.notna().mean(),'bygrade',x.groupby('g').n.apply(lambda s:s.notna().mean()).to_dict(), 'counts',x.n.describe().to_dict(),flush=True)\\nps=pd.read_parquet(D/'validation_preference_state.parquet')\\nfor st in ['liked','disliked']:\\n ss=ps[ps.state==st][['uid','item_id']].drop_duplicates().assign(flag=1); y=truth.merge(ss,on=['uid','item_id'],how='left'); print(st,y.flag.notna().mean(),y.groupby('g').flag.apply(lambda s:s.notna().mean()).to_dict(),flush=True)\\nfor days in [7,14,30,60,90,180]:\\n cutoff=24786800-days*86400; recent=pairs[pairs['last']>=cutoff][['uid','item_id']]; y=truth.merge(recent,on=['uid','item_id'],how='left',indicator=True); print('seen',days,(y._merge=='both').mean(),'pairs',len(recent),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:40:46.532Z Script running with cell ID 11 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:40:48.762Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:40:50.373Z Script completed Wall time 1.6 seconds Output: truth 37769 users 4263 grade {1: 33352, 3: 4417} peruser {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, 'max': 442.0} train subset (31062540, 5) pairs (7868664, 8) hist listened 0.0 bygrade {1: 0.0, 3: 0.0} counts {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan} liked 0.0 {1: 0.0, 3: 0.0} disliked 0.0 {1: 0.0, 3: 0.0} seen 7 0.0 pairs 715074 seen 14 0.0 pairs 1240352 seen 30 0.0 pairs 2149455 seen 60 0.0 pairs 3394429 seen 90 0.0 pairs 4306717 seen 180 0.0 pairs 6222581 ## Step 20 · agent · 2026-09-24T17:41:01.926Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-09-24T17:41:04.100Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:11.468Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_baseline.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Create cutoff-correct catalog-popularity rankings.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def rank(npz_name: str, output_name: str) -> None:\\n+ with np.load(DATA / npz_name, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].copy()\\n+ items = z[\\\"item_ids\\\"].copy()\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ rows = []\\n+ for row, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[row], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[eligible][:100]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(np.int64(uid), 100),\\n+ \\\"item_id\\\": chosen.astype(np.int64),\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / output_name, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ rank(\\\"validation_eligible_candidates.npz\\\", \\\"validation_baseline.parquet\\\")\\n+ rank(\\\"test_eligible_candidates.npz\\\", \\\"submission.parquet\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_baseline.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:41:18.216Z Script completed Wall time 6.7 seconds Output: {} { "model": "/app/solution/validation_baseline.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 22 · agent · 2026-09-24T17:41:34.676Z ### Thinking unavailable — no text recorded ## Step 23 · agent · 2026-09-24T17:41:43.127Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-09-24T17:41:54.714Z ### Thinking unavailable — no text recorded The baseline is valid and scores 0.010710 NDCG@10. The diagnostic shows an important task property: every validation-relevant user–track pair is absent from that user’s training listens and explicit state. So memorizing repeats cannot help; I’m switching emphasis to (1) time-aware track priors and (2) collaborative taste transfer from similar listeners. ### exec · 2026-09-24T17:42:37.870Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_global.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Validation-only comparison of leakage-safe global track priors.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D = Path(\\\"/task/data\\\")\\n+CUTOFF = 24786800\\n+\\n+\\n+def truth_and_eligibility():\\n+ targets = pd.read_parquet(D / \\\"target_users.parquet\\\").uid.to_numpy()\\n+ listens = pd.read_parquet(D / \\\"val_listens.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ likes = pd.read_parquet(D / \\\"val_likes.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ truth = pd.concat([\\n+ listens[np.isin(listens.uid, targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)][[\\\"uid\\\", \\\"item_id\\\"]].assign(grade=1),\\n+ likes[np.isin(likes.uid, targets) & (likes.is_organic == 1)][[\\\"uid\\\", \\\"item_id\\\"]].assign(grade=3),\\n+ ]).sort_values(\\\"grade\\\").drop_duplicates([\\\"uid\\\", \\\"item_id\\\"], keep=\\\"last\\\")\\n+ with np.load(D / \\\"validation_eligible_candidates.npz\\\", allow_pickle=False) as z:\\n+ uids, items, packed = z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+ ur = pd.Series(np.arange(len(uids)), index=uids).reindex(truth.uid).to_numpy()\\n+ ic = pd.Series(np.arange(len(items)), index=items).reindex(truth.item_id).to_numpy()\\n+ ok = ~np.isnan(ur) & ~np.isnan(ic)\\n+ ii = np.where(ok)[0]\\n+ ok[ii] = ((packed[ur[ii].astype(int), ic[ii].astype(int)//8] >> (ic[ii].astype(int)%8)) & 1).astype(bool)\\n+ truth = truth[ok]\\n+ gold = {int(u): dict(zip(g.item_id.astype(int), g.grade.astype(int))) for u, g in truth.groupby(\\\"uid\\\")}\\n+ return uids, items, packed, gold, truth\\n+\\n+\\n+def score_order(order, uids, items, packed, gold):\\n+ discounts = 1 / np.log2(np.arange(2, 12))\\n+ total = 0.0\\n+ for r, u in enumerate(uids):\\n+ if int(u) not in gold:\\n+ continue\\n+ cols = order\\n+ elig = ((packed[r, cols//8] >> (cols%8)) & 1).astype(bool)\\n+ top = items[cols[elig][:10]]\\n+ grades = np.array([gold[int(u)].get(int(x), 0) for x in top])\\n+ dcg = ((2.0**grades - 1) * discounts[:len(grades)]).sum()\\n+ ideal = sorted(gold[int(u)].values(), reverse=True)[:10]\\n+ idcg = ((2.0**np.array(ideal)-1) * discounts[:len(ideal)]).sum()\\n+ total += dcg/idcg\\n+ return total/len(gold)\\n+\\n+\\n+def main():\\n+ uids, items, packed, gold, truth = truth_and_eligibility()\\n+ idx = pd.Index(items)\\n+ cat = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\").set_index(\\\"item_id\\\").reindex(items)\\n+ feats = {\\n+ \\\"catalog_positive\\\": cat.positive_listens.to_numpy(float),\\n+ \\\"catalog_organic_positive\\\": cat.organic_positive_listens.to_numpy(float),\\n+ }\\n+ li = pd.read_parquet(D / \\\"train_listens.parquet\\\", columns=[\\\"uid\\\",\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\",\\\"played_ratio_pct\\\"])\\n+ col = idx.get_indexer(li.item_id)\\n+ li = li[col >= 0].copy(); col = col[col >= 0]\\n+ print(\\\"candidate listen events\\\", len(li), flush=True)\\n+ for days in [7, 14, 30, 60, 90, 180, 1000]:\\n+ age = CUTOFF - days*86400\\n+ recent = li.timestamp.to_numpy() >= age\\n+ for label, cond in {\\n+ \\\"pos\\\": li.played_ratio_pct.to_numpy() >= 50,\\n+ \\\"strong\\\": li.played_ratio_pct.to_numpy() >= 80,\\n+ \\\"orgpos\\\": (li.played_ratio_pct.to_numpy() >= 50) & (li.is_organic.to_numpy() == 1),\\n+ \\\"orgstrong\\\": (li.played_ratio_pct.to_numpy() >= 80) & (li.is_organic.to_numpy() == 1),\\n+ }.items():\\n+ m = recent & cond\\n+ feats[f\\\"{label}_{days}d\\\"] = np.bincount(col[m], minlength=len(items)).astype(float)\\n+ del li\\n+ lk = pd.read_parquet(D / \\\"train_likes.parquet\\\", columns=[\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\"])\\n+ col = idx.get_indexer(lk.item_id); lk=lk[col>=0].copy(); col=col[col>=0]\\n+ for days in [7,14,30,60,90,180,1000]:\\n+ age=CUTOFF-days*86400\\n+ for label,cond in {\\\"like\\\":np.ones(len(lk),bool),\\\"orglike\\\":lk.is_organic.to_numpy()==1}.items():\\n+ m=(lk.timestamp.to_numpy()>=age)&cond\\n+ feats[f\\\"{label}_{days}d\\\"]=np.bincount(col[m],minlength=len(items)).astype(float)\\n+ # Oracle diagnostic, never a candidate feature.\\n+ tc = truth.groupby(\\\"item_id\\\").size().reindex(items, fill_value=0).to_numpy(float)\\n+ print(\\\"oracle_global\\\", score_order(np.argsort(-tc, kind=\\\"stable\\\"),uids,items,packed,gold), flush=True)\\n+ results=[]\\n+ for name, values in feats.items():\\n+ s=score_order(np.argsort(-values,kind=\\\"stable\\\"),uids,items,packed,gold)\\n+ results.append((s,name))\\n+ for s,name in sorted(results,reverse=True): print(f\\\"{s:.6f} {name}\\\")\\n+ # Smoothed trend/rate combinations around strongest families.\\n+ combos={}\\n+ for base in [\\\"orgstrong\\\",\\\"orgpos\\\",\\\"pos\\\",\\\"strong\\\",\\\"orglike\\\",\\\"like\\\"]:\\n+ for short in [7,14,30,60]:\\n+ a=feats[f\\\"{base}_{short}d\\\"]\\n+ for long in [90,180,1000]:\\n+ b=feats[f\\\"{base}_{long}d\\\"]\\n+ for alpha in [0.05,0.1,0.2,0.5,1,2,5,10]:\\n+ combos[f\\\"{base}_{short}+{alpha}*{long}\\\"]=a+alpha*b\\n+ # popularity-normalized trend\\n+ for prior in [1,3,10,30,100]:\\n+ combos[f\\\"{base}_rate_{short}_{long}_p{prior}\\\"]=(a+prior*(short/long))/(b+prior)\\n+ res=[]\\n+ for name,v in combos.items(): res.append((score_order(np.argsort(-v,kind=\\\"stable\\\"),uids,items,packed,gold),name))\\n+ print(\\\"COMBOS\\\")\\n+ for s,name in sorted(res,reverse=True)[:30]: print(f\\\"{s:.6f} {name}\\\")\\n+\\n+if __name__ == \\\"__main__\\\": main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:42:48.889Z Script running with cell ID 13 Wall time 11.0 seconds Output: {} ### wait · 2026-09-24T17:42:50.376Z { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:43:08.053Z Script completed Wall time 17.7 seconds Output: {"session_id":40189,"output":"candidate listen events 31249613\noracle_global 0.033611406540695485\n"} ## Step 25 · agent · 2026-09-24T17:43:09.755Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:10.482Z { "code": "const r = await tools.write_stdin({session_id:40189,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:43:21.499Z Script running with cell ID 14 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:25.418Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:43:40.502Z Script completed Wall time 15.1 seconds Output: {"chunk_id":"7fd630","wall_time_seconds":30.0005803,"session_id":40189,"original_token_count":0,"output":""} ## Step 26 · agent · 2026-09-24T17:43:44.312Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:44.613Z { "code": "const r = await tools.write_stdin({session_id:40189,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:43:44.771Z Script completed Wall time 0.2 seconds Output: 0.030933 pos_7d 0.030187 like_7d 0.030107 strong_7d 0.029297 orglike_7d 0.028456 orgpos_7d 0.028131 orgstrong_7d 0.026849 orgpos_14d 0.026726 orglike_14d 0.025124 pos_14d 0.022878 orgstrong_14d 0.022238 strong_14d 0.020652 like_14d 0.018429 orglike_30d 0.017182 pos_30d 0.016953 strong_60d 0.016892 pos_60d 0.016812 like_30d 0.016741 orglike_60d 0.016688 strong_30d 0.016217 orglike_90d 0.015980 strong_90d 0.015972 pos_90d 0.015967 orgpos_60d 0.015822 orgstrong_90d 0.015802 orgstrong_60d 0.015801 orgpos_90d 0.015481 like_60d 0.015093 like_90d 0.014310 orgpos_30d 0.013314 orgstrong_30d 0.013240 like_180d 0.013000 orglike_180d 0.012135 like_1000d 0.012116 pos_180d 0.012054 orglike_1000d 0.012040 strong_180d 0.011913 orgpos_180d 0.011908 orgstrong_180d 0.010727 strong_1000d 0.010710 pos_1000d 0.010710 catalog_positive 0.010577 orgpos_1000d 0.010577 catalog_organic_positive 0.010551 orgstrong_1000d Traceback (most recent call last): File "/app/solution/explore_global.py", line 104, in if __name__ == "__main__": main() ^^^^^^ File "/app/solution/explore_global.py", line 100, in main for name,v in combos.items(): res.append((score_order(np.argsort(-v,kind="stable"),uids,items,packed,gold),name)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/explore_global.py", line 38, in score_order elig = ((packed[r, cols//8] >> (cols%8)) & 1).astype(bool) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ## Step 27 · agent · 2026-09-24T17:43:56.492Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-09-24T17:44:07.105Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-09-24T17:44:18.354Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-09-24T17:44:21.676Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:56.369Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_knn.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Leakage-safe listener-neighborhood validation experiments.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import gc\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from sklearn.preprocessing import normalize\\n+\\n+D=Path('/task/data'); CUTOFF=24786800\\n+\\n+\\n+def load_eval():\\n+ vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+ vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+ targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy()\\n+ t=pd.concat([vl[np.isin(vl.uid,targets)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[np.isin(vk.uid,targets)&(vk.is_organic==1)][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\n+ with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); items=z['item_ids'].copy(); pack=z['packed_eligible'].copy()\\n+ ur=pd.Series(np.arange(len(eu)),index=eu).reindex(t.uid).to_numpy(); ic=pd.Series(np.arange(len(items)),index=items).reindex(t.item_id).to_numpy(); ok=~np.isnan(ur)&~np.isnan(ic); q=np.where(ok)[0]; ok[q]=((pack[ur[q].astype(int),ic[q].astype(int)//8]>>(ic[q].astype(int)%8))&1).astype(bool); t=t[ok]\\n+ gold={int(u):dict(zip(x.item_id.astype(int),x.g.astype(int))) for u,x in t.groupby('uid')}\\n+ return eu,items,pack,gold\\n+\\n+\\n+def evaluate_orders(orders, eu, items, pack, gold):\\n+ disc=1/np.log2(np.arange(2,12)); total=0.\\n+ for r,u in enumerate(eu):\\n+ gd=gold.get(int(u));\\n+ if gd is None: continue\\n+ cols=orders[r]\\n+ el=((pack[r,cols//8]>>(cols%8))&1).astype(bool); top=items[cols[el][:10]]\\n+ gr=np.array([gd.get(int(i),0) for i in top]); dcg=((2.**gr-1)*disc[:len(gr)]).sum(); ideal=np.array(sorted(gd.values(),reverse=True)[:10]); idcg=((2.**ideal-1)*disc[:len(ideal)]).sum(); total+=dcg/idcg\\n+ return total/len(gold)\\n+\\n+\\n+def candidate_orders(P, global_score, formulas, top_global=500):\\n+ global_cols=np.argsort(-global_score,kind='stable')[:top_global]\\n+ ans={name:[] for name in formulas}\\n+ for r in range(P.shape[0]):\\n+ a,b=P.indptr[r:r+2]; pc=P.indices[a:b]; pv=P.data[a:b]\\n+ cols=np.unique(np.r_[pc,global_cols]); lookup=np.zeros(len(cols),np.float32)\\n+ ix=np.searchsorted(cols,pc); lookup[ix]=pv\\n+ g=global_score[cols]\\n+ for name,func in formulas.items():\\n+ s=func(lookup,g)\\n+ take=np.argpartition(-s,min(119,len(s)-1))[:120]; take=take[np.argsort(-s[take],kind='stable')]\\n+ ans[name].append(cols[take])\\n+ return {k:v for k,v in ans.items()}\\n+\\n+\\n+def main():\\n+ eu,items,pack,gold=load_eval(); cat_index=pd.Index(items)\\n+ li=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ all_u=np.sort(li.uid.unique()); uidx=pd.Index(all_u); rows=uidx.get_indexer(li.uid)\\n+ # Broad, positive, fairly recent taste profiles; exact repeats are excluded by eligibility.\\n+ m=(li.timestamp>=CUTOFF-180*86400)&(li.played_ratio_pct>=50)\\n+ prof=li.loc[m,['uid','item_id']].drop_duplicates(); del m\\n+ rr=uidx.get_indexer(prof.uid); codes,uniques=pd.factorize(prof.item_id,sort=False); nusers=len(all_u)\\n+ df=np.bincount(codes,minlength=len(uniques)); keep=(df[codes]>=2)&(df[codes]<=1500)\\n+ rr=rr[keep]; cc=codes[keep]; dfk=df[cc]\\n+ val=np.log((nusers+10)/(dfk+10)).astype(np.float32)\\n+ X=sparse.csr_matrix((val,(rr,cc)),shape=(nusers,len(uniques)),dtype=np.float32); X=normalize(X,norm='l2',copy=False)\\n+ print('profile',X.shape,X.nnz,flush=True); del prof,rr,cc,dfk,val,codes,uniques; gc.collect()\\n+ target_rows=uidx.get_indexer(eu)\\n+ nn=NearestNeighbors(n_neighbors=301,metric='cosine',algorithm='brute',n_jobs=-1).fit(X)\\n+ dist,nbr=nn.kneighbors(X[target_rows],return_distance=True); sim=(1-dist).astype(np.float32)\\n+ # Remove self (normally first) robustly.\\n+ keep_n=nbr!=target_rows[:,None]; nbr2=np.empty((len(eu),300),np.int32); sim2=np.empty((len(eu),300),np.float32)\\n+ for r in range(len(eu)):\\n+ n=nbr[r][keep_n[r]][:300]; s=sim[r][keep_n[r]][:300]; nbr2[r,:len(n)]=n; sim2[r,:len(s)]=s\\n+ print('neighbors sim quantiles',np.quantile(sim2,[0,.1,.5,.9,1]),flush=True); del X,dist,nbr,sim,keep_n; gc.collect()\\n+ # Last-week candidate events: the strongest global prior and neighbor outcomes.\\n+ col=cat_index.get_indexer(li.item_id)\\n+ m=(col>=0)&(li.timestamp.to_numpy()>=CUTOFF-7*86400)&(li.played_ratio_pct.to_numpy()>=50)\\n+ cy=pd.DataFrame({'r':rows[m],'c':col[m]})\\n+ cnt=cy.groupby(['r','c'],sort=False).size().rename('n').reset_index()\\n+ g=np.bincount(cy.c,minlength=len(items)).astype(np.float32)\\n+ Ybin=sparse.csr_matrix((np.ones(len(cnt),np.float32),(cnt.r,cnt.c)),shape=(nusers,len(items)))\\n+ Ycnt=sparse.csr_matrix((np.log1p(cnt.n.to_numpy()).astype(np.float32),(cnt.r,cnt.c)),shape=(nusers,len(items)))\\n+ print('recent candidate pairs/events',len(cnt),len(cy),flush=True); del li,cy,cnt,rows; gc.collect()\\n+ formulas={\\n+ 'p':lambda p,g:p,\\n+ 'p_g005':lambda p,g:p+.05*np.log1p(g),\\n+ 'p_g01':lambda p,g:p+.1*np.log1p(g),\\n+ 'p_g02':lambda p,g:p+.2*np.log1p(g),\\n+ 'p_g05':lambda p,g:p+.5*np.log1p(g),\\n+ 'p_g1':lambda p,g:p+np.log1p(g),\\n+ 'lift05':lambda p,g:p/(g+3)**.5+.1*np.log1p(g),\\n+ 'lift025':lambda p,g:p/(g+3)**.25+.1*np.log1p(g),\\n+ }\\n+ for kval in [25,50,100,200,300]:\\n+ rr=np.repeat(np.arange(len(eu)),kval); cc=nbr2[:,:kval].ravel(); vv=sim2[:,:kval].ravel()\\n+ W=sparse.csr_matrix((vv,(rr,cc)),shape=(len(eu),nusers))\\n+ for yn,Y in [('bin',Ybin),('cnt',Ycnt)]:\\n+ P=(W@Y).tocsr(); print('P',kval,yn,P.nnz,flush=True)\\n+ orders=candidate_orders(P,g,formulas)\\n+ for name,o in orders.items(): print(f'SCORE k{kval}_{yn}_{name} {evaluate_orders(o,eu,items,pack,gold):.6f}',flush=True)\\n+ del P,orders; gc.collect()\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_knn.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":40000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:45:07.389Z Script running with cell ID 16 Wall time 11.0 seconds Output: {} ### wait · 2026-09-24T17:45:08.842Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:45:26.553Z Script completed Wall time 17.7 seconds Output: {"session_id":39254,"output":"profile (9238, 516265) 6034683\nneighbors sim quantiles [0. 0.03062069 0.06833315 0.13189456 0.80884701]\nrecent candidate pairs/events 486301 674579\nP 25 bin 14192389\nSCORE k25_bin_p 0.029385\nSCORE k25_bin_p_g005 0.032350\nSCORE k25_bin_p_g01 0.033700\nSCORE k25_bin_p_g02 0.037055\nSCORE k25_bin_p_g05 0.039659\nSCORE k25_bin_p_g1 0.038833\nSCORE k25_bin_lift05 0.036081\nSCORE k25_bin_lift025 0.037615\nP 25 cnt 14192389\nSCORE k25_cnt_p 0.029863\nSCORE k25_cnt_p_g005 0.032347\nSCORE k25_cnt_p_g01 0.034430\nSCORE k25_cnt_p_g02 0.036495\nSCORE k25_cnt_p_g05 0.039520\nSCORE k25_cnt_p_g1 0.038579\nSCORE k25_cnt_lift05 0.035871\nSCORE k25_cnt_lift025 0.037754\nP 50 bin 24294791\nSCORE k50_bin_p 0.034223\nSCORE k50_bin_p_g005 0.035674\nSCORE k50_bin_p_g01 0.036555\nSCORE k50_bin_p_g02 0.038135\nSCORE k50_bin_p_g05 0.040145\nSCORE k50_bin_p_g1 0.040742\nSCORE k50_bin_lift05 0.038131\nSCORE k50_bin_lift025 0.037704\nP 50 cnt 24294791\n"} ## Step 31 · agent · 2026-09-24T17:45:29.017Z ### Thinking unavailable — no text recorded The first collaborative results are materially better: 0.040742 NDCG@10 versus 0.010710 for catalog popularity and about 0.031 for a recent-popularity model. A 50-neighbor taste model blended with the last-week prior is currently best; I’m finishing the neighbor-count and weighting sweep before locking the architecture. ### exec · 2026-09-24T17:45:31.222Z { "code": "const r = await tools.write_stdin({session_id:39254,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:45:42.242Z Script running with cell ID 17 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:45:45.907Z { "cell_id": "17", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:46:01.245Z Script completed Wall time 15.3 seconds Output: {"chunk_id":"e56467","wall_time_seconds":30.001268508,"session_id":39254,"original_token_count":192,"output":"SCORE k50_cnt_p 0.035348\nSCORE k50_cnt_p_g005 0.037145\nSCORE k50_cnt_p_g01 0.038195\nSCORE k50_cnt_p_g02 0.039437\nSCORE k50_cnt_p_g05 0.041011\nSCORE k50_cnt_p_g1 0.041624\nSCORE k50_cnt_lift05 0.038445\nSCORE k50_cnt_lift025 0.039302\nP 100 bin 39772992\nSCORE k100_bin_p 0.037967\nSCORE k100_bin_p_g005 0.038887\nSCORE k100_bin_p_g01 0.039362\nSCORE k100_bin_p_g02 0.040960\nSCORE k100_bin_p_g05 0.042319\nSCORE k100_bin_p_g1 0.042608\nSCORE k100_bin_lift05 0.039960\nSCORE k100_bin_lift025 0.038362\nP 100 cnt 39772992\nSCORE k100_cnt_p 0.039556\nSCORE k100_cnt_p_g005 0.040341\nSCORE k100_cnt_p_g01 0.041097\nSCORE k100_cnt_p_g02 0.042234\nSCORE k100_cnt_p_g05 0.043637\nSCORE k100_cnt_p_g1 0.043116\nSCORE k100_cnt_lift05 0.041177\nSCORE k100_cnt_lift025 0.040976\nP 200 bin 62140013\n"} ## Step 32 · agent · 2026-09-24T17:46:02.437Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:03.209Z { "code": "const r = await tools.write_stdin({session_id:39254,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:46:14.225Z Script running with cell ID 18 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:15.752Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:46:33.227Z Script completed Wall time 17.5 seconds Output: {"chunk_id":"951df9","wall_time_seconds":30.000250219,"session_id":39254,"original_token_count":129,"output":"SCORE k200_bin_p 0.041097\nSCORE k200_bin_p_g005 0.041079\nSCORE k200_bin_p_g01 0.041570\nSCORE k200_bin_p_g02 0.042061\nSCORE k200_bin_p_g05 0.042730\nSCORE k200_bin_p_g1 0.043195\nSCORE k200_bin_lift05 0.041303\nSCORE k200_bin_lift025 0.039154\nP 200 cnt 62140013\nSCORE k200_cnt_p 0.043246\nSCORE k200_cnt_p_g005 0.043518\nSCORE k200_cnt_p_g01 0.044061\nSCORE k200_cnt_p_g02 0.044540\nSCORE k200_cnt_p_g05 0.044862\nSCORE k200_cnt_p_g1 0.044087\nSCORE k200_cnt_lift05 0.043010\nSCORE k200_cnt_lift025 0.042286\nP 300 bin 78830032\n"} ## Step 33 · agent · 2026-09-24T17:46:34.597Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:35.339Z { "code": "const r = await tools.write_stdin({session_id:39254,chars:\"\",yield_time_ms\":30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:46:35.418Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ## Step 34 · agent · 2026-09-24T17:46:36.856Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:37.560Z { "code": "const r = await tools.write_stdin({session_id:39254,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:46:48.576Z Script running with cell ID 20 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:50.318Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:46:58.390Z Script completed Wall time 8.1 seconds Output: SCORE k300_bin_p 0.041837 SCORE k300_bin_p_g005 0.042261 SCORE k300_bin_p_g01 0.042310 SCORE k300_bin_p_g02 0.042498 SCORE k300_bin_p_g05 0.042706 SCORE k300_bin_p_g1 0.043193 SCORE k300_bin_lift05 0.040922 SCORE k300_bin_lift025 0.039834 P 300 cnt 78830032 SCORE k300_cnt_p 0.044355 SCORE k300_cnt_p_g005 0.044627 SCORE k300_cnt_p_g01 0.044579 SCORE k300_cnt_p_g02 0.044226 SCORE k300_cnt_p_g05 0.044885 SCORE k300_cnt_p_g1 0.044717 SCORE k300_cnt_lift05 0.043500 SCORE k300_cnt_lift025 0.043217 ## Step 35 · agent · 2026-09-24T17:47:12.599Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-09-24T17:47:30.103Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-09-24T17:47:42.867Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-24T17:47:44.652Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:46.465Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/explore_global.py\\n@@\\n- cols = order\\n+ # At least 82% of the catalog is eligible for every user, so the first\\n+ # 500 globally ordered columns are ample to recover an exact top ten.\\n+ cols = order[:500]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:47:46.544Z Script completed Wall time 0.0 seconds Output: {} ## Step 39 · agent · 2026-09-24T17:47:48.205Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:49.661Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:48:00.677Z Script running with cell ID 22 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:02.306Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:48:19.841Z Script completed Wall time 17.5 seconds Output: {"session_id":42519,"output":"candidate listen events 31249613\noracle_global 0.033611406540695485\n"} ### exec · 2026-09-24T17:48:23.807Z { "code": "const r = await tools.write_stdin({session_id:42519,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:48:34.826Z Script running with cell ID 23 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:37.234Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:48:53.830Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"8f056e","wall_time_seconds":30.001276442,"session_id":42519,"original_token_count":0,"output":""} ## Step 40 · agent · 2026-09-24T17:48:55.126Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:55.844Z { "code": "const r = await tools.write_stdin({session_id:42519,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:49:06.860Z Script running with cell ID 24 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:08.629Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:49:25.864Z Script completed Wall time 17.2 seconds Output: {"chunk_id":"4d1991","wall_time_seconds":30.001059884,"session_id":42519,"original_token_count":0,"output":""} ## Step 41 · agent · 2026-09-24T17:49:29.136Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:29.802Z { "code": "const r = await tools.write_stdin({session_id:42519,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:49:40.819Z Script running with cell ID 25 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:42.530Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:49:59.823Z Script completed Wall time 17.3 seconds Output: {"chunk_id":"1f74e3","wall_time_seconds":30.001133794,"session_id":42519,"original_token_count":0,"output":""} ## Step 42 · agent · 2026-09-24T17:50:01.438Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:02.436Z { "code": "const r = await tools.write_stdin({session_id:42519,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:50:02.615Z Script completed Wall time 0.2 seconds Output: 0.030933 pos_7d 0.030187 like_7d 0.030107 strong_7d 0.029297 orglike_7d 0.028456 orgpos_7d 0.028131 orgstrong_7d 0.026849 orgpos_14d 0.026726 orglike_14d 0.025124 pos_14d 0.022878 orgstrong_14d 0.022238 strong_14d 0.020652 like_14d 0.018429 orglike_30d 0.017182 pos_30d 0.016953 strong_60d 0.016892 pos_60d 0.016812 like_30d 0.016741 orglike_60d 0.016688 strong_30d 0.016217 orglike_90d 0.015980 strong_90d 0.015972 pos_90d 0.015967 orgpos_60d 0.015822 orgstrong_90d 0.015802 orgstrong_60d 0.015801 orgpos_90d 0.015481 like_60d 0.015093 like_90d 0.014310 orgpos_30d 0.013314 orgstrong_30d 0.013240 like_180d 0.013000 orglike_180d 0.012135 like_1000d 0.012116 pos_180d 0.012054 orglike_1000d 0.012040 strong_180d 0.011913 orgpos_180d 0.011908 orgstrong_180d 0.010727 strong_1000d 0.010710 pos_1000d 0.010710 catalog_positive 0.010577 orgpos_1000d 0.010577 catalog_organic_positive 0.010551 orgstrong_1000d Traceback (most recent call last): File "/app/solution/explore_global.py", line 106, in if __name__ == "__main__": main() ^^^^^^ File "/app/solution/explore_global.py", line 102, in main for name,v in combos.items(): res.append((score_order(np.argsort(-v,kind="stable"),uids,items,packed,gold),name)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/explore_global.py", line 43, in score_order dcg = ((2.0**grades - 1) * discounts[:len(grades)]).sum() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py", line 52, in _sum return umr_sum(a, axis, dtype, out, keepdims, initial, where) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ## Step 43 · agent · 2026-09-24T17:50:09.318Z ### Thinking unavailable — no text recorded The collaborative gain is stable up to 200–300 neighbors, with a best public score of 0.044885. I’m now testing whether cutoff-correct active likes give a cleaner taste neighborhood than playback history; this is a distinct signal and is cheap enough to evaluate before finalizing. ### exec · 2026-09-24T17:50:27.009Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_like_knn.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Compare explicit-like neighborhoods on the public cutoff.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import sys, gc\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from sklearn.preprocessing import normalize\\n+\\n+sys.path.insert(0, str(Path(__file__).parent))\\n+from explore_knn import load_eval, candidate_orders, evaluate_orders\\n+\\n+D=Path('/task/data'); CUTOFF=24786800\\n+\\n+def main():\\n+ eu,items,pack,gold=load_eval(); all_u=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique()); ui=pd.Index(all_u); n=len(all_u)\\n+ ps=pd.read_parquet(D/'validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=ui.get_indexer(ps.uid); cc,un=pd.factorize(ps.item_id); df=np.bincount(cc); keep=(rr>=0)&(df[cc]>=2)&(df[cc]<=1500)\\n+ vals=np.log((n+10)/(df[cc[keep]]+10)).astype('float32'); X=sparse.csr_matrix((vals,(rr[keep],cc[keep])),shape=(n,len(un))); X=normalize(X,copy=False)\\n+ tr=ui.get_indexer(eu); nn=NearestNeighbors(n_neighbors=301,metric='cosine',algorithm='brute',n_jobs=-1).fit(X); d,nb=nn.kneighbors(X[tr]); sm=(1-d).astype('float32'); nb2=np.empty((len(eu),300),np.int32); sm2=np.empty((len(eu),300),np.float32)\\n+ for r in range(len(eu)):\\n+ k=nb[r]!=tr[r]; nb2[r]=nb[r][k][:300]; sm2[r]=sm[r][k][:300]\\n+ print('X',X.shape,X.nnz,'sim',np.quantile(sm2,[0,.1,.5,.9,1]),flush=True); del X,d,nb,sm,ps; gc.collect()\\n+ li=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','played_ratio_pct']); col=pd.Index(items).get_indexer(li.item_id); row=ui.get_indexer(li.uid); m=(col>=0)&(li.timestamp>=CUTOFF-7*86400)&(li.played_ratio_pct>=50); cy=pd.DataFrame({'r':row[m],'c':col[m]}); cnt=cy.groupby(['r','c'],sort=False).size().reset_index(name='n'); g=np.bincount(cy.c,minlength=len(items)).astype('float32'); Y=sparse.csr_matrix((np.log1p(cnt.n).astype('float32'),(cnt.r,cnt.c)),shape=(n,len(items))); del li,cy,cnt; gc.collect()\\n+ formulas={f'g{a}':(lambda p,g,a=a:p+a*np.log1p(g)) for a in [.1,.2,.3,.4,.5,.7,1.,1.5]}\\n+ for k in [50,100,200,300]:\\n+ W=sparse.csr_matrix((sm2[:,:k].ravel(),(np.repeat(np.arange(len(eu)),k),nb2[:,:k].ravel())),shape=(len(eu),n)); P=(W@Y).tocsr(); print('P',k,P.nnz,flush=True); orders=candidate_orders(P,g,formulas)\\n+ for name,o in orders.items(): print('SCORE',k,name,f'{evaluate_orders(o,eu,items,pack,gold):.6f}',flush=True)\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_like_knn.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:50:38.028Z Script running with cell ID 27 Wall time 11.0 seconds Output: {} ### wait · 2026-09-24T17:50:40.351Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:50:57.191Z Script completed Wall time 16.8 seconds Output: {"session_id":6539,"output":"X (9238, 28422) 416606 sim [0. 0. 0.03117287 0.06994438 1. ]\nP 50 15820124\nSCORE 50 g0.1 0.034375\nSCORE 50 g0.2 0.036274\nSCORE 50 g0.3 0.036804\nSCORE 50 g0.4 0.037022\nSCORE 50 g0.5 0.036946\nSCORE 50 g0.7 0.036339\nSCORE 50 g1.0 0.035745\nSCORE 50 g1.5 0.035293\nP 100 27520162\nSCORE 100 g0.1 0.038145\nSCORE 100 g0.2 0.038867\nSCORE 100 g0.3 0.038638\nSCORE 100 g0.4 0.038760\nSCORE 100 g0.5 0.038893\nSCORE 100 g0.7 0.038451\nSCORE 100 g1.0 0.037880\nSCORE 100 g1.5 0.036874\nP 200 44483203\nSCORE 200 g0.1 0.040075\nSCORE 200 g0.2 0.040754\nSCORE 200 g0.3 0.040371\nSCORE 200 g0.4 0.040285\nSCORE 200 g0.5 0.040211\nSCORE 200 g0.7 0.040281\nSCORE 200 g1.0 0.039303\nSCORE 200 g1.5 0.038490\n"} ## Step 44 · agent · 2026-09-24T17:50:58.406Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:59.103Z { "code": "const r = await tools.write_stdin({session_id:6539,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:51:10.121Z Script running with cell ID 28 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:51:11.820Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:51:11.913Z Script completed Wall time 0.0 seconds Output: P 300 56761944 SCORE 300 g0.1 0.040414 SCORE 300 g0.2 0.040840 SCORE 300 g0.3 0.041032 SCORE 300 g0.4 0.040762 SCORE 300 g0.5 0.040930 SCORE 300 g0.7 0.040356 SCORE 300 g1.0 0.039831 SCORE 300 g1.5 0.039097 ## Step 45 · agent · 2026-09-24T17:51:26.578Z ### Thinking unavailable — no text recorded ## Step 46 · agent · 2026-09-24T17:51:35.324Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:40.946Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/explore_knn.py\\n@@\\n Ybin=sparse.csr_matrix((np.ones(len(cnt),np.float32),(cnt.r,cnt.c)),shape=(nusers,len(items)))\\n Ycnt=sparse.csr_matrix((np.log1p(cnt.n.to_numpy()).astype(np.float32),(cnt.r,cnt.c)),shape=(nusers,len(items)))\\n+ Yraw=sparse.csr_matrix((cnt.n.to_numpy().astype(np.float32),(cnt.r,cnt.c)),shape=(nusers,len(items)))\\n print('recent candidate pairs/events',len(cnt),len(cy),flush=True); del li,cy,cnt,rows; gc.collect()\\n+ likes=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\\n+ lc=cat_index.get_indexer(likes.item_id); lr=uidx.get_indexer(likes.uid)\\n+ lm=(lc>=0)&(likes.timestamp.to_numpy()>=CUTOFF-7*86400)\\n+ lg=pd.DataFrame({'r':lr[lm],'c':lc[lm]}).groupby(['r','c'],sort=False).size().reset_index(name='n')\\n+ Ylike=sparse.csr_matrix((np.log1p(lg.n.to_numpy()).astype(np.float32),(lg.r,lg.c)),shape=(nusers,len(items)))\\n+ del likes,lg; gc.collect()\\n@@\\n- for kval in [25,50,100,200,300]:\\n+ for kval in [200,300]:\\n@@\\n- for yn,Y in [('bin',Ybin),('cnt',Ycnt)]:\\n+ for yn,Y in [('cnt',Ycnt),('raw',Yraw),('cnt_like05',Ycnt+.5*Ylike),('cnt_like1',Ycnt+Ylike),('cnt_like2',Ycnt+2*Ylike)]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_knn.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":40000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:51:50.860Z Script completed Wall time 9.9 seconds Output: {} profile (9238, 516265) 6034683 neighbors sim quantiles [0. 0.03062069 0.06833315 0.13189456 0.80884701] recent candidate pairs/events 486301 674579 Traceback (most recent call last): File "/app/solution/explore_knn.py", line 107, in if __name__=='__main__': main() ^^^^^^ File "/app/solution/explore_knn.py", line 86, in main Ylike=sparse.csr_matrix((np.log1p(lg.n.to_numpy()).astype(np.float32),(lg.r,lg.c)),shape=(nusers,len(items))) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py", line 56, in __init__ coo = self._coo_container(arg1, shape=shape, dtype=dtype) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 101, in __init__ self._check() File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 225, in _check raise ValueError(f'negative axis {i} index: {idx.min()}') ValueError: negative axis 0 index: -1 ## Step 47 · agent · 2026-09-24T17:51:52.171Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:55.476Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/explore_knn.py\\n@@\\n- lm=(lc>=0)&(likes.timestamp.to_numpy()>=CUTOFF-7*86400)\\n+ lm=(lc>=0)&(lr>=0)&(likes.timestamp.to_numpy()>=CUTOFF-7*86400)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/explore_knn.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":40000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:52:06.493Z Script running with cell ID 30 Wall time 11.0 seconds Output: {} ### wait · 2026-09-24T17:52:08.251Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:52:25.656Z Script completed Wall time 17.4 seconds Output: {"session_id":72793,"output":"profile (9238, 516265) 6034683\nneighbors sim quantiles [0. 0.03062069 0.06833315 0.13189456 0.80884701]\nrecent candidate pairs/events 486301 674579\nP 200 cnt 62140013\nSCORE k200_cnt_p 0.043246\nSCORE k200_cnt_p_g005 0.043518\nSCORE k200_cnt_p_g01 0.044061\nSCORE k200_cnt_p_g02 0.044540\nSCORE k200_cnt_p_g05 0.044862\nSCORE k200_cnt_p_g1 0.044087\nSCORE k200_cnt_lift05 0.043010\nSCORE k200_cnt_lift025 0.042286\nP 200 raw 62140013\n"} ## Step 48 · agent · 2026-09-24T17:52:26.928Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:27.619Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:52:38.636Z Script running with cell ID 31 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:40.286Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:52:57.640Z Script completed Wall time 17.4 seconds Output: {"chunk_id":"9a7287","wall_time_seconds":30.001744718,"session_id":72793,"original_token_count":147,"output":"SCORE k200_raw_p 0.036678\nSCORE k200_raw_p_g005 0.037039\nSCORE k200_raw_p_g01 0.037331\nSCORE k200_raw_p_g02 0.038188\nSCORE k200_raw_p_g05 0.039191\nSCORE k200_raw_p_g1 0.040389\nSCORE k200_raw_lift05 0.034104\nSCORE k200_raw_lift025 0.033774\nP 200 cnt_like05 62319150\nSCORE k200_cnt_like05_p 0.044082\nSCORE k200_cnt_like05_p_g005 0.044121\nSCORE k200_cnt_like05_p_g01 0.044404\nSCORE k200_cnt_like05_p_g02 0.044910\nSCORE k200_cnt_like05_p_g05 0.045343\nSCORE k200_cnt_like05_p_g1 0.044369\nSCORE k200_cnt_like05_lift05 0.043667\nSCORE k200_cnt_like05_lift025 0.042694\nP 200 cnt_like1 62319150\n"} ## Step 49 · agent · 2026-09-24T17:52:58.985Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:59.686Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms\":30000,max_output_tokens\":40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:52:59.810Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Unexpected string ## Step 50 · agent · 2026-09-24T17:53:01.343Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:02.064Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:53:13.084Z Script running with cell ID 33 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:14.617Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:53:32.089Z Script completed Wall time 17.5 seconds Output: {"chunk_id":"082afc","wall_time_seconds":30.001543044,"session_id":72793,"original_token_count":155,"output":"SCORE k200_cnt_like1_p 0.044385\nSCORE k200_cnt_like1_p_g005 0.044633\nSCORE k200_cnt_like1_p_g01 0.044710\nSCORE k200_cnt_like1_p_g02 0.045090\nSCORE k200_cnt_like1_p_g05 0.045207\nSCORE k200_cnt_like1_p_g1 0.044570\nSCORE k200_cnt_like1_lift05 0.043971\nSCORE k200_cnt_like1_lift025 0.043118\nP 200 cnt_like2 62319150\nSCORE k200_cnt_like2_p 0.044618\nSCORE k200_cnt_like2_p_g005 0.044772\nSCORE k200_cnt_like2_p_g01 0.044743\nSCORE k200_cnt_like2_p_g02 0.044811\nSCORE k200_cnt_like2_p_g05 0.045409\nSCORE k200_cnt_like2_p_g1 0.045135\nSCORE k200_cnt_like2_lift05 0.044254\nSCORE k200_cnt_like2_lift025 0.043203\nP 300 cnt 78830032\n"} ## Step 51 · agent · 2026-09-24T17:53:33.437Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:34.158Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms\":30000,max_output_tokens\":40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:53:34.280Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Unexpected string ### exec · 2026-09-24T17:53:36.531Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:53:47.549Z Script running with cell ID 35 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:51.934Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:54:06.552Z Script completed Wall time 14.6 seconds Output: SCORE k300_cnt_p 0.044355 SCORE k300_cnt_p_g005 0.044627 SCORE k300_cnt_p_g01 0.044579 SCORE k300_cnt_p_g02 0.044226 SCORE k300_cnt_p_g05 0.044885 SCORE k300_cnt_p_g1 0.044717 SCORE k300_cnt_lift05 0.043500 SCORE k300_cnt_lift025 0.043217 P 300 raw 78830032 SCORE k300_raw_p 0.038408 SCORE k300_raw_p_g005 0.038544 SCORE k300_raw_p_g01 0.038877 SCORE k300_raw_p_g02 0.039202 SCORE k300_raw_p_g05 0.040165 SCORE k300_raw_p_g1 0.040606 SCORE k300_raw_lift05 0.035465 SCORE k300_raw_lift025 0.036236 P 300 cnt_like05 79047723 ## Step 52 · agent · 2026-09-24T17:54:07.721Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:08.451Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms\":30000,max_output_tokens:40000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:54:08.588Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ### exec · 2026-09-24T17:54:10.694Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:54:21.713Z Script running with cell ID 37 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:54:23.439Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:54:40.717Z Script completed Wall time 17.3 seconds Output: SCORE k300_cnt_like05_p 0.044376 SCORE k300_cnt_like05_p_g005 0.044674 SCORE k300_cnt_like05_p_g01 0.044580 SCORE k300_cnt_like05_p_g02 0.044581 SCORE k300_cnt_like05_p_g05 0.045185 SCORE k300_cnt_like05_p_g1 0.045151 SCORE k300_cnt_like05_lift05 0.043908 SCORE k300_cnt_like05_lift025 0.043295 P 300 cnt_like1 79047723 SCORE k300_cnt_like1_p 0.044839 SCORE k300_cnt_like1_p_g005 0.044905 SCORE k300_cnt_like1_p_g01 0.044942 SCORE k300_cnt_like1_p_g02 0.045157 SCORE k300_cnt_like1_p_g05 0.045209 SCORE k300_cnt_like1_p_g1 0.045158 SCORE k300_cnt_like1_lift05 0.044526 SCORE k300_cnt_like1_lift025 0.043935 P 300 cnt_like2 79047723 ## Step 53 · agent · 2026-09-24T17:54:42.021Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:42.778Z { "code": "const r = await tools.write_stdin({session_id:72793,chars:\"\",yield_time_ms:30000,max_output_tokens:40000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:54:53.794Z Script running with cell ID 38 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:54:55.392Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 40000 } ### Tool output · 2026-09-24T17:54:55.512Z Script completed Wall time 0.0 seconds Output: SCORE k300_cnt_like2_p 0.045376 SCORE k300_cnt_like2_p_g005 0.045414 SCORE k300_cnt_like2_p_g01 0.045553 SCORE k300_cnt_like2_p_g02 0.045459 SCORE k300_cnt_like2_p_g05 0.045564 SCORE k300_cnt_like2_p_g1 0.045432 SCORE k300_cnt_like2_lift05 0.044930 SCORE k300_cnt_like2_lift025 0.044546 ## Step 54 · agent · 2026-09-24T17:55:08.718Z ### Thinking unavailable — no text recorded ## Step 55 · agent · 2026-09-24T17:55:20.911Z ### Thinking unavailable — no text recorded ## Step 56 · agent · 2026-09-24T17:55:22.042Z ### Thinking unavailable — no text recorded Active-like neighborhoods alone are weaker, but recent likes are valuable as candidate evidence inside the playback-based neighborhood. Weighting a neighbor’s recent like on top of their listen raises the best validation score to 0.045564. I’m locking that leakage-safe configuration now and refitting it on train plus validation history; the existing valid baseline remains untouched until the stronger final file is fully built. ### exec · 2026-09-24T17:56:05.962Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Build the leakage-safe collaborative Made-for-You ranking.\\n+\\n+The validation stage uses only train events. The default test stage refits the\\n+same selected model on train + validation events and uses test eligibility.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import gc\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from sklearn.preprocessing import normalize\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+SOLUTION = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+TEST_CUTOFF = 25_393_400\\n+DAY = 86_400\\n+\\n+\\n+def load_events(stage: str) -> tuple[pd.DataFrame, pd.DataFrame, int]:\\n+ listen_cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"played_ratio_pct\\\"]\\n+ action_cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"]\\n+ listens = [pd.read_parquet(DATA / \\\"train_listens.parquet\\\", columns=listen_cols)]\\n+ likes = [pd.read_parquet(DATA / \\\"train_likes.parquet\\\", columns=action_cols)]\\n+ cutoff = TRAIN_CUTOFF\\n+ if stage == \\\"test\\\":\\n+ listens.append(pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=listen_cols))\\n+ likes.append(pd.read_parquet(DATA / \\\"val_likes.parquet\\\", columns=action_cols))\\n+ cutoff = TEST_CUTOFF\\n+ return (\\n+ pd.concat(listens, ignore_index=True),\\n+ pd.concat(likes, ignore_index=True),\\n+ cutoff,\\n+ )\\n+\\n+\\n+def build(stage: str, output: Path) -> None:\\n+ archive = (\\n+ \\\"validation_eligible_candidates.npz\\\"\\n+ if stage == \\\"validation\\\"\\n+ else \\\"test_eligible_candidates.npz\\\"\\n+ )\\n+ with np.load(DATA / archive, allow_pickle=False) as z:\\n+ target_uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ candidate_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+\\n+ listens, likes, cutoff = load_events(stage)\\n+ all_uids = np.sort(listens.uid.unique())\\n+ user_index = pd.Index(all_uids)\\n+ target_rows = user_index.get_indexer(target_uids)\\n+ if (target_rows < 0).any():\\n+ raise RuntimeError(\\\"a target user has no fitted listener row\\\")\\n+ candidate_index = pd.Index(candidate_items)\\n+\\n+ # Candidate evidence is deliberately very recent: this captured changing\\n+ # catalog demand much better than the lifetime popularity fields.\\n+ listen_cols = candidate_index.get_indexer(listens.item_id)\\n+ listen_rows = user_index.get_indexer(listens.uid)\\n+ recent = (\\n+ (listen_cols >= 0)\\n+ & (listens.timestamp.to_numpy() >= cutoff - 7 * DAY)\\n+ & (listens.played_ratio_pct.to_numpy() >= 50)\\n+ )\\n+ recent_pairs = pd.DataFrame(\\n+ {\\\"row\\\": listen_rows[recent], \\\"col\\\": listen_cols[recent]}\\n+ ).groupby([\\\"row\\\", \\\"col\\\"], sort=False).size().reset_index(name=\\\"count\\\")\\n+ global_score = np.bincount(\\n+ listen_cols[recent], minlength=len(candidate_items)\\n+ ).astype(np.float32)\\n+\\n+ # Broad recent taste profiles. IDF emphasizes discriminative shared tracks;\\n+ # cosine normalization prevents very active listeners from dominating.\\n+ profile = listens.loc[\\n+ (listens.timestamp >= cutoff - 180 * DAY)\\n+ & (listens.played_ratio_pct >= 50),\\n+ [\\\"uid\\\", \\\"item_id\\\"],\\n+ ].drop_duplicates()\\n+ profile_rows = user_index.get_indexer(profile.uid)\\n+ profile_cols, unique_profile_items = pd.factorize(profile.item_id, sort=False)\\n+ item_df = np.bincount(profile_cols, minlength=len(unique_profile_items))\\n+ useful = (item_df[profile_cols] >= 2) & (item_df[profile_cols] <= 1500)\\n+ values = np.log(\\n+ (len(all_uids) + 10.0) / (item_df[profile_cols[useful]] + 10.0)\\n+ ).astype(np.float32)\\n+ user_profile = sparse.csr_matrix(\\n+ (values, (profile_rows[useful], profile_cols[useful])),\\n+ shape=(len(all_uids), len(unique_profile_items)),\\n+ dtype=np.float32,\\n+ )\\n+ user_profile = normalize(user_profile, norm=\\\"l2\\\", copy=False)\\n+ del profile, profile_rows, profile_cols, unique_profile_items, item_df, values\\n+ del listen_cols, listen_rows, recent, listens\\n+ gc.collect()\\n+\\n+ # Find nearest listeners using only history available at the cutoff.\\n+ knn = NearestNeighbors(\\n+ n_neighbors=301, metric=\\\"cosine\\\", algorithm=\\\"brute\\\", n_jobs=-1\\n+ ).fit(user_profile)\\n+ distances, raw_neighbors = knn.kneighbors(\\n+ user_profile[target_rows], return_distance=True\\n+ )\\n+ raw_similarity = (1.0 - distances).astype(np.float32)\\n+ neighbors = np.empty((len(target_uids), 300), dtype=np.int32)\\n+ similarities = np.empty((len(target_uids), 300), dtype=np.float32)\\n+ for row in range(len(target_uids)):\\n+ not_self = raw_neighbors[row] != target_rows[row]\\n+ neighbors[row] = raw_neighbors[row][not_self][:300]\\n+ similarities[row] = raw_similarity[row][not_self][:300]\\n+ del user_profile, knn, distances, raw_neighbors, raw_similarity\\n+ gc.collect()\\n+\\n+ # Neighbor candidate strength: saturating playback count plus an explicit\\n+ # positive-action boost. The factor 2 was selected on public validation.\\n+ listen_matrix = sparse.csr_matrix(\\n+ (\\n+ np.log1p(recent_pairs[\\\"count\\\"].to_numpy()).astype(np.float32),\\n+ (recent_pairs.row, recent_pairs.col),\\n+ ),\\n+ shape=(len(all_uids), len(candidate_items)),\\n+ dtype=np.float32,\\n+ )\\n+ like_cols = candidate_index.get_indexer(likes.item_id)\\n+ like_rows = user_index.get_indexer(likes.uid)\\n+ recent_like = (\\n+ (like_cols >= 0)\\n+ & (like_rows >= 0)\\n+ & (likes.timestamp.to_numpy() >= cutoff - 7 * DAY)\\n+ )\\n+ like_pairs = pd.DataFrame(\\n+ {\\\"row\\\": like_rows[recent_like], \\\"col\\\": like_cols[recent_like]}\\n+ ).groupby([\\\"row\\\", \\\"col\\\"], sort=False).size().reset_index(name=\\\"count\\\")\\n+ like_matrix = sparse.csr_matrix(\\n+ (\\n+ np.log1p(like_pairs[\\\"count\\\"].to_numpy()).astype(np.float32),\\n+ (like_pairs.row, like_pairs.col),\\n+ ),\\n+ shape=(len(all_uids), len(candidate_items)),\\n+ dtype=np.float32,\\n+ )\\n+ candidate_strength = listen_matrix + 2.0 * like_matrix\\n+ del likes, recent_pairs, like_pairs, listen_matrix, like_matrix\\n+ gc.collect()\\n+\\n+ weights = sparse.csr_matrix(\\n+ (\\n+ similarities.ravel(),\\n+ (\\n+ np.repeat(np.arange(len(target_uids)), neighbors.shape[1]),\\n+ neighbors.ravel(),\\n+ ),\\n+ ),\\n+ shape=(len(target_uids), len(all_uids)),\\n+ dtype=np.float32,\\n+ )\\n+ collaborative = (weights @ candidate_strength).tocsr()\\n+ del weights, candidate_strength, neighbors, similarities\\n+ gc.collect()\\n+\\n+ # Rank the union of collaborative candidates and a global safety net.\\n+ global_top = np.argsort(-global_score, kind=\\\"stable\\\")[:1000]\\n+ frames: list[pd.DataFrame] = []\\n+ for row, uid in enumerate(target_uids):\\n+ start, end = collaborative.indptr[row : row + 2]\\n+ personal_cols = collaborative.indices[start:end]\\n+ personal_values = collaborative.data[start:end]\\n+ cols = np.unique(np.concatenate((personal_cols, global_top)))\\n+ personal = np.zeros(len(cols), dtype=np.float32)\\n+ personal[np.searchsorted(cols, personal_cols)] = personal_values\\n+ score = personal + 0.5 * np.log1p(global_score[cols])\\n+ eligible = (\\n+ (packed[row, cols // 8] >> (cols % 8)) & 1\\n+ ).astype(bool)\\n+ cols = cols[eligible]\\n+ score = score[eligible]\\n+ if len(cols) < 100:\\n+ raise RuntimeError(f\\\"insufficient eligible scored candidates for uid={uid}\\\")\\n+ chosen = np.argpartition(-score, 99)[:100]\\n+ chosen = chosen[np.argsort(-score[chosen], kind=\\\"stable\\\")]\\n+ ranked_items = candidate_items[cols[chosen]]\\n+ frames.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(uid, 100),\\n+ \\\"item_id\\\": ranked_items,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+\\n+ result = pd.concat(frames, ignore_index=True).astype(\\n+ {\\\"uid\\\": \\\"int64\\\", \\\"item_id\\\": \\\"int64\\\", \\\"rank\\\": \\\"int64\\\"}\\n+ )\\n+ temporary = output.with_name(output.stem + \\\".tmp.parquet\\\")\\n+ result.to_parquet(temporary, index=False)\\n+ temporary.replace(output)\\n+ print(f\\\"wrote {len(result):,} rows to {output}\\\")\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), default=\\\"test\\\")\\n+ parser.add_argument(\\\"--output\\\", type=Path, default=SOLUTION / \\\"submission.parquet\\\")\\n+ args = parser.parse_args()\\n+ build(args.stage, args.output)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:56:16.982Z Script running with cell ID 39 Wall time 11.0 seconds Output: {} ## Step 57 · agent · 2026-09-24T17:56:18.181Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:56:18.657Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:56:27.450Z Script completed Wall time 8.8 seconds Output: wrote 845,600 rows to /app/solution/submission.parquet ## Step 58 · agent · 2026-09-24T17:56:29.342Z ### Thinking unavailable — no text recorded The refit completed successfully and wrote all 845,600 final rows. I’m now running the same builder at the validation cutoff for an end-to-end public check, then I’ll validate every final row against test eligibility and add the concise reproducibility notes. ### exec · 2026-09-24T17:56:32.401Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:56:43.418Z Script running with cell ID 40 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:44.936Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:56:56.489Z Script completed Wall time 11.6 seconds Output: wrote 845,600 rows to /app/solution/validation.parquet { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.045564, "quality_users": 4263 } } ## Step 59 · agent · 2026-09-24T17:57:07.043Z ### Thinking unavailable — no text recorded ## Step 60 · agent · 2026-09-24T17:57:07.636Z ### Thinking unavailable — no text recorded The exact end-to-end validation score matches the selected experiment: 0.045564 NDCG@10. Since a full rebuild is fast in this environment, I’m using the remaining time for a narrow robustness sweep over profile horizon and neighborhood size—parameters that can change personalized coverage without changing the model family. ### exec · 2026-09-24T17:57:25.438Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n-def build(stage: str, output: Path) -> None:\\n+def build(\\n+ stage: str,\\n+ output: Path,\\n+ profile_days: int = 180,\\n+ neighbor_count: int = 300,\\n+ candidate_days: int = 7,\\n+ like_weight: float = 2.0,\\n+ global_weight: float = 0.5,\\n+) -> None:\\n@@\\n- & (listens.timestamp.to_numpy() >= cutoff - 7 * DAY)\\n+ & (listens.timestamp.to_numpy() >= cutoff - candidate_days * DAY)\\n@@\\n- (listens.timestamp >= cutoff - 180 * DAY)\\n+ (listens.timestamp >= cutoff - profile_days * DAY)\\n@@\\n- n_neighbors=301, metric=\\\"cosine\\\", algorithm=\\\"brute\\\", n_jobs=-1\\n+ n_neighbors=neighbor_count + 1,\\n+ metric=\\\"cosine\\\",\\n+ algorithm=\\\"brute\\\",\\n+ n_jobs=-1,\\n@@\\n- neighbors = np.empty((len(target_uids), 300), dtype=np.int32)\\n- similarities = np.empty((len(target_uids), 300), dtype=np.float32)\\n+ neighbors = np.empty((len(target_uids), neighbor_count), dtype=np.int32)\\n+ similarities = np.empty((len(target_uids), neighbor_count), dtype=np.float32)\\n@@\\n- neighbors[row] = raw_neighbors[row][not_self][:300]\\n- similarities[row] = raw_similarity[row][not_self][:300]\\n+ neighbors[row] = raw_neighbors[row][not_self][:neighbor_count]\\n+ similarities[row] = raw_similarity[row][not_self][:neighbor_count]\\n@@\\n- & (likes.timestamp.to_numpy() >= cutoff - 7 * DAY)\\n+ & (likes.timestamp.to_numpy() >= cutoff - candidate_days * DAY)\\n@@\\n- candidate_strength = listen_matrix + 2.0 * like_matrix\\n+ candidate_strength = listen_matrix + like_weight * like_matrix\\n@@\\n- score = personal + 0.5 * np.log1p(global_score[cols])\\n+ score = personal + global_weight * np.log1p(global_score[cols])\\n@@\\n parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), default=\\\"test\\\")\\n parser.add_argument(\\\"--output\\\", type=Path, default=SOLUTION / \\\"submission.parquet\\\")\\n+ parser.add_argument(\\\"--profile-days\\\", type=int, default=180)\\n+ parser.add_argument(\\\"--neighbors\\\", type=int, default=300)\\n+ parser.add_argument(\\\"--candidate-days\\\", type=int, default=7)\\n+ parser.add_argument(\\\"--like-weight\\\", type=float, default=2.0)\\n+ parser.add_argument(\\\"--global-weight\\\", type=float, default=0.5)\\n args = parser.parse_args()\\n- build(args.stage, args.output)\\n+ build(\\n+ args.stage,\\n+ args.output,\\n+ profile_days=args.profile_days,\\n+ neighbor_count=args.neighbors,\\n+ candidate_days=args.candidate_days,\\n+ like_weight=args.like_weight,\\n+ global_weight=args.global_weight,\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"for d in 60 90 270; do /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days \\\"$d\\\" --output \\\"/app/solution/validation_p${d}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_p${d}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:57:36.459Z Script running with cell ID 41 Wall time 11.0 seconds Output: {} ### wait · 2026-09-24T17:57:38.059Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:57:55.624Z Script completed Wall time 17.6 seconds Output: {"session_id":84772,"output":"wrote 845,600 rows to /app/solution/validation_p60.parquet\n{\n \"model\": \"/app/solution/validation_p60.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045815,\n \"quality_users\": 4263\n }\n}\n"} ## Step 61 · agent · 2026-09-24T17:57:57.251Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:58.037Z { "code": "const r = await tools.write_stdin({session_id:84772,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:58:09.053Z Script running with cell ID 42 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:10.475Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:58:28.055Z Script completed Wall time 17.6 seconds Output: {"chunk_id":"bdbb53","wall_time_seconds":30.000508287,"session_id":84772,"original_token_count":48,"output":"wrote 845,600 rows to /app/solution/validation_p90.parquet\n{\n \"model\": \"/app/solution/validation_p90.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046373,\n \"quality_users\": 4263\n }\n}\n"} ## Step 62 · agent · 2026-09-24T17:58:29.838Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:30.572Z { "code": "const r = await tools.write_stdin({session_id:84772,chars:\"\",yield_time_ms\":30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:58:30.665Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ### exec · 2026-09-24T17:58:33.576Z { "code": "const r = await tools.write_stdin({session_id:84772,chars:\"\",yield_time_ms\":30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:58:33.786Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ## Step 63 · agent · 2026-09-24T17:58:35.220Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:36.067Z { "code": "const r = await tools.write_stdin({session_id:84772,yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:58:36.184Z Script completed Wall time 0.0 seconds Output: wrote 845,600 rows to /app/solution/validation_p270.parquet { "model": "/app/solution/validation_p270.parquet", "scores": { "quality_ndcg@10": 0.046153, "quality_users": 4263 } } ## Step 64 · agent · 2026-09-24T17:58:38.908Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:41.675Z { "code": "const r = await tools.exec_command({cmd:\"for k in 200 400 500; do /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors \\\"$k\\\" --output \\\"/app/solution/validation_p90_k${k}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_p90_k${k}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T17:58:52.694Z Script running with cell ID 46 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:54.367Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:59:11.855Z Script completed Wall time 17.5 seconds Output: {"session_id":92353,"output":"wrote 845,600 rows to /app/solution/validation_p90_k200.parquet\n{\n \"model\": \"/app/solution/validation_p90_k200.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046189,\n \"quality_users\": 4263\n }\n}\n"} ## Step 65 · agent · 2026-09-24T17:59:14.810Z ### Thinking unavailable — no text recorded The tighter history horizon improves the model: 90-day profiles score 0.046373, ahead of 60, 180, and 270 days. The 200-neighbor variant is slightly worse, so I’m completing the 400/500-neighbor side of the sweep and will refit only the winner. ### exec · 2026-09-24T17:59:16.908Z { "code": "const r = await tools.write_stdin({session_id:92353,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:59:27.925Z Script running with cell ID 47 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:59:29.453Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:59:46.929Z Script completed Wall time 17.5 seconds Output: {"chunk_id":"0510f6","wall_time_seconds":30.001105869,"session_id":92353,"original_token_count":67,"output":"wrote 845,600 rows to /app/solution/validation_p90_k400.parquet\n{\n \"model\": \"/app/solution/validation_p90_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046971,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/validation_p90_k500.parquet\n"} ## Step 66 · agent · 2026-09-24T17:59:52.633Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:59:52.637Z { "code": "const r = await tools.write_stdin({session_id:92353,yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:59:52.765Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation_p90_k500.parquet", "scores": { "quality_ndcg@10": 0.046776, "quality_users": 4263 } } ## Step 67 · agent · 2026-09-24T17:59:57.170Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:59:59.953Z { "code": "const r = await tools.exec_command({cmd:\"for d in 3 5 10 14; do /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors 400 --candidate-days \\\"$d\\\" --output \\\"/app/solution/validation_c${d}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_c${d}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T18:00:10.971Z Script running with cell ID 49 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:14.078Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:00:30.131Z Script completed Wall time 16.1 seconds Output: {"session_id":85127,"output":"wrote 845,600 rows to /app/solution/validation_c3.parquet\n{\n \"model\": \"/app/solution/validation_c3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.044534,\n \"quality_users\": 4263\n }\n}\n"} ## Step 68 · agent · 2026-09-24T18:00:31.443Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:00:32.128Z { "code": "const r = await tools.write_stdin({session_id:85127,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:00:43.143Z Script running with cell ID 50 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:45.401Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:01:02.146Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"b14174","wall_time_seconds":30.001343542,"session_id":85127,"original_token_count":62,"output":"wrote 845,600 rows to /app/solution/validation_c5.parquet\n{\n \"model\": \"/app/solution/validation_c5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.04524,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/validation_c10.parquet\n"} ## Step 69 · agent · 2026-09-24T18:01:03.676Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:04.367Z { "code": "const r = await tools.write_stdin({session_id:85127,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:01:15.382Z Script running with cell ID 51 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:17.466Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:01:30.208Z Script completed Wall time 12.7 seconds Output: {"chunk_id":"fdef10","wall_time_seconds":25.821578841,"exit_code":0,"original_token_count":81,"output":"{\n \"model\": \"/app/solution/validation_c10.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045114,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/validation_c14.parquet\n{\n \"model\": \"/app/solution/validation_c14.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.041019,\n \"quality_users\": 4263\n }\n}\n"} ## Step 70 · agent · 2026-09-24T18:01:32.566Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:35.454Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0 1 3 4; do /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors 400 --like-weight \\\"$w\\\" --output \\\"/app/solution/validation_lw${w}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_lw${w}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T18:01:46.472Z Script running with cell ID 52 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:48.286Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:02:05.633Z Script completed Wall time 17.3 seconds Output: {"session_id":42087,"output":"wrote 845,600 rows to /app/solution/validation_lw0.parquet\n{\n \"model\": \"/app/solution/validation_lw0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045372,\n \"quality_users\": 4263\n }\n}\n"} ## Step 71 · agent · 2026-09-24T18:02:08.156Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:08.797Z { "code": "const r = await tools.write_stdin({session_id:42087,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:19.818Z Script running with cell ID 53 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:21.703Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:02:38.820Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"c4019f","wall_time_seconds":30.001044672,"session_id":42087,"original_token_count":48,"output":"wrote 845,600 rows to /app/solution/validation_lw1.parquet\n{\n \"model\": \"/app/solution/validation_lw1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046054,\n \"quality_users\": 4263\n }\n}\n"} ## Step 72 · agent · 2026-09-24T18:02:40.104Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:40.775Z { "code": "const r = await tools.write_stdin({session_id:42087,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:51.792Z Script running with cell ID 54 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:53.338Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:03:07.736Z Script completed Wall time 14.4 seconds Output: {"chunk_id":"9f3357","wall_time_seconds":26.940940736,"exit_code":0,"original_token_count":96,"output":"wrote 845,600 rows to /app/solution/validation_lw3.parquet\n{\n \"model\": \"/app/solution/validation_lw3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046997,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/validation_lw4.parquet\n{\n \"model\": \"/app/solution/validation_lw4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046979,\n \"quality_users\": 4263\n }\n}\n"} ## Step 73 · agent · 2026-09-24T18:03:10.627Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:15.293Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.1 0.25 0.75 1.0; do tag=$(printf '%s' \\\"$w\\\" | tr '.' '_'); /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors 400 --like-weight 3 --global-weight \\\"$w\\\" --output \\\"/app/solution/validation_g${tag}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_g${tag}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T18:03:26.310Z Script running with cell ID 55 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:03:28.154Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:03:45.470Z Script completed Wall time 17.3 seconds Output: {"session_id":84657,"output":"wrote 845,600 rows to /app/solution/validation_g0_1.parquet\n{\n \"model\": \"/app/solution/validation_g0_1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.047857,\n \"quality_users\": 4263\n }\n}\n"} ## Step 74 · agent · 2026-09-24T18:03:48.079Z ### Thinking unavailable — no text recorded Recent-like weight 3 gives a small additional lift, and reducing the global-popularity blend is helping more: the first test at weight 0.1 reaches 0.047857. This suggests the 400-neighbor signal is strong enough that popularity should mainly resolve sparse/tied cases rather than drive the rank. ### exec · 2026-09-24T18:03:50.257Z { "code": "const r = await tools.write_stdin({session_id:84657,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:01.272Z Script running with cell ID 56 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:04:03.320Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:04:20.276Z Script completed Wall time 17.0 seconds Output: {"chunk_id":"9dd09b","wall_time_seconds":30.001069734,"session_id":84657,"original_token_count":49,"output":"wrote 845,600 rows to /app/solution/validation_g0_25.parquet\n{\n \"model\": \"/app/solution/validation_g0_25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.047309,\n \"quality_users\": 4263\n }\n}\n"} ## Step 75 · agent · 2026-09-24T18:04:21.932Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:04:22.660Z { "code": "const r = await tools.write_stdin({session_id:84657,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:33.678Z Script running with cell ID 57 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:04:37.093Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:04:48.994Z Script completed Wall time 11.9 seconds Output: {"chunk_id":"fb4c9b","wall_time_seconds":26.311933694,"exit_code":0,"original_token_count":97,"output":"wrote 845,600 rows to /app/solution/validation_g0_75.parquet\n{\n \"model\": \"/app/solution/validation_g0_75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.046639,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/validation_g1_0.parquet\n{\n \"model\": \"/app/solution/validation_g1_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.04643,\n \"quality_users\": 4263\n }\n}\n"} ## Step 76 · agent · 2026-09-24T18:04:53.783Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:04:56.982Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0 0.05 0.15; do tag=$(printf '%s' \\\"$w\\\" | tr '.' '_'); /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors 400 --like-weight 3 --global-weight \\\"$w\\\" --output \\\"/app/solution/validation_g${tag}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_g${tag}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T18:05:07.999Z Script running with cell ID 58 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:05:10.342Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:05:27.159Z Script completed Wall time 16.8 seconds Output: {"session_id":35639,"output":"wrote 845,600 rows to /app/solution/validation_g0.parquet\n{\n \"model\": \"/app/solution/validation_g0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.047942,\n \"quality_users\": 4263\n }\n}\n"} ## Step 77 · agent · 2026-09-24T18:05:28.608Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:05:29.285Z { "code": "const r = await tools.write_stdin({session_id:35639,yield_time_ms:30000,max_output_tokens:30000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:05:40.301Z Script running with cell ID 59 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:05:41.858Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:05:59.303Z Script completed Wall time 17.4 seconds Output: {"chunk_id":"f75380","wall_time_seconds":30.000987134,"session_id":35639,"original_token_count":49,"output":"wrote 845,600 rows to /app/solution/validation_g0_05.parquet\n{\n \"model\": \"/app/solution/validation_g0_05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.048001,\n \"quality_users\": 4263\n }\n}\n"} ## Step 78 · agent · 2026-09-24T18:06:00.839Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:06:01.528Z { "code": "const r = await tools.write_stdin({session_id:35639,yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T18:06:05.016Z Script completed Wall time 3.5 seconds Output: wrote 845,600 rows to /app/solution/validation_g0_15.parquet { "model": "/app/solution/validation_g0_15.parquet", "scores": { "quality_ndcg@10": 0.047499, "quality_users": 4263 } } ## Step 79 · agent · 2026-09-24T18:06:07.379Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:06:10.283Z { "code": "const r = await tools.exec_command({cmd:\"for w in 2 4 6; do /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --profile-days 90 --neighbors 400 --like-weight \\\"$w\\\" --global-weight 0.05 --output \\\"/app/solution/validation_best_lw${w}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_best_lw${w}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\nif (r.session_id) text(JSON.stringify({session_id:r.session_id,output:r.output})); else text(r.output);" } ### Tool output · 2026-09-24T18:06:21.302Z Script running with cell ID 61 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:06:23.046Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · Timestamp not recorded No response recorded.