yambda-discovery-ranking-v01 / sol-high-0.147.0-exact-r02-2026-09-24 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T17:36:07.749Z 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 · 2026-09-24T17:36:09.602Z ## 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 · 2026-09-24T17:36:09.602Z 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 · 2026-09-24T17:36:09.602Z 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 · 2026-09-24T17:36:09.602Z 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-2a39-7ff2-a437-32d6af51d7c4", "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 · 2026-09-24T17:36:09.612Z # 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 · 2026-09-24T17:36:11.671Z ### Thinking unavailable — no text recorded I’ll first profile the event volumes, user/item coverage, eligibility density, and temporal windows, then build a leakage-safe validation pipeline. I’ll keep a valid fallback submission on disk early, evaluate several personalized ranking variants, and refit the strongest one using train plus validation history. ### exec · 2026-09-24T17:36:16.646Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data, schemas, distributions, and evaluator mechanics\",\"status\":\"in_progress\"},\n {step:\"Create a valid baseline submission and validation ranking\",\"status\":\"pending\"},\n {step:\"Develop and evaluate personalized temporal ranking models\",\"status\":\"pending\"},\n {step:\"Refit selected model on train + validation and build final submission\",\"status\":\"pending\"},\n {step:\"Validate artifact and document reproducible build\",\"status\":\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,240p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:16.716Z Script completed Wall time 0.1 seconds Output: /app /task/requirements.txt /task/data/val_dislikes.parquet /task/data/val_undislikes.parquet /task/data/target_users.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/validation_preference_state.parquet /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/validation_eligible_candidates.npz /task/data/candidate_catalog.parquet /task/data/val_likes.parquet /task/ranking_evaluation.py /task/evaluate_public.py /task/data/train_listens.parquet /task/data/val_dislikes.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/validation_eligible_candidates.npz /task/data/candidate_catalog.parquet /task/data/val_likes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/val_undislikes.parquet /task/data/target_users.parquet /task/data/val_unlikes.parquet /task/data/validation_preference_state.parquet # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. Python itself # is pinned by task.toml and both Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 idna==3.18 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } #!/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() ## Step 8 · agent · 2026-09-24T17:36:18.757Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:24.879Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, os\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n df=pd.read_parquet(p)\\n print(p.name, df.shape, df.dtypes.to_dict())\\n if 'timestamp' in df: print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\\n elif p.name.endswith('preference_state.parquet'): print(df.head(), df.state.value_counts().to_dict(), 'uids',df.uid.nunique(),'items',df.item_id.nunique())\\n else: print(df.head())\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/f)\\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print(' eligibility count',np.quantile(cnt,[0,.01,.1,.5,.9,.99,1]),'mean',cnt.mean(),'aligned uids head',z['uids'][:5], 'items head',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:25.012Z 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 · 2026-09-24T17:36:27.197Z ### Thinking unavailable — no text recorded The default `python` executable is a minimal interpreter rather than the task environment (it lacks even the pinned pandas build). I’m locating the prebuilt runtime now; no packages will be installed or modified. ### exec · 2026-09-24T17:36:30.979Z { "code": "const r = await tools.exec_command({cmd:\"command -v python python3 uv || true\\nls -la /task | sed -n '1,120p'\\nfind /opt /usr/local /venv /task -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | sed -n '1,100p'\\nfind / -maxdepth 4 -type d -name 'site-packages' 2>/dev/null | sed -n '1,100p'\\nenv | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:31.084Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python total 36 dr-xr-xr-x 1 root root 4096 Sep 24 17:33 . drwxr-xr-x 1 root root 4096 Sep 24 17:36 .. dr-xr-xr-x 1 root root 4096 Sep 24 17:29 data -r--r--r-- 1 root root 5774 Sep 24 17:28 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 24 17:28 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 24 17:28 requirements.txt CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_MANAGED_BY_NPM=1 CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex CODEX_THREAD_ID=01a0d47d-2a28-7d91-9317-eff5c56feeea COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=234846fb0c7e LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin NVM_CD_FLAGS= NVM_DIR=/home/agent/.nvm NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node PAGER=cat PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/home/agent/.nvm/versions/node/v22.23.3/bin/codex ## Step 10 · agent · 2026-09-24T17:36:33.688Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:35.738Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list --format=columns | sed -n '1,120p'; ls -la /usr/local/bin | sed -n '1,160p'; find / -type f -name 'pandas*' 2>/dev/null | sed -n '1,80p'\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:36.857Z Script completed Wall time 1.1 seconds Output: Package Version ------- ------- pip 25.0.1 total 52 drwxr-xr-x 1 root root 4096 Sep 24 17:36 . drwxr-xr-x 1 root root 4096 Aug 3 00:00 .. lrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12 -rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12 lrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3 lrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12 -rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12 lrwxrwxrwx 1 root root 13 Sep 24 17:36 node -> /usr/bin/node lrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12 lrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3 lrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12 -rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12 lrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3 lrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config lrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12 lrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config -rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12 -rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config /opt/task-venv/lib/python3.12/site-packages/pyarrow/__pycache__/pandas_compat.cpython-312.pyc /opt/task-venv/lib/python3.12/site-packages/pyarrow/pandas_compat.py /opt/task-venv/lib/python3.12/site-packages/pyarrow/pandas-shim.pxi /opt/task-venv/lib/python3.12/site-packages/pyarrow/tests/__pycache__/pandas_threaded_import.cpython-312.pyc /opt/task-venv/lib/python3.12/site-packages/pyarrow/tests/__pycache__/pandas_examples.cpython-312.pyc /opt/task-venv/lib/python3.12/site-packages/pyarrow/tests/pandas_threaded_import.py /opt/task-venv/lib/python3.12/site-packages/pyarrow/tests/pandas_examples.py /opt/task-venv/lib/python3.12/site-packages/pandas/_libs/pandas_datetime.cpython-312-x86_64-linux-gnu.so /opt/task-venv/lib/python3.12/site-packages/pandas/_libs/pandas_parser.cpython-312-x86_64-linux-gnu.so ## Step 11 · agent · 2026-09-24T17:36:39.186Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:41.092Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python - <<'PY'\\nimport pandas,numpy,sklearn,pyarrow\\nprint(pandas.__version__,numpy.__version__,sklearn.__version__,pyarrow.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:42.513Z Script completed Wall time 1.4 seconds Output: total 112 dr-xr-xr-x 1 root root 4096 Sep 24 17:32 . dr-xr-xr-x 1 root root 4096 Sep 24 17:32 .. -r--r--r-- 1 root root 9033 Sep 24 17:32 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 24 17:32 activate -r--r--r-- 1 root root 920 Sep 24 17:32 activate.csh -r--r--r-- 1 root root 2195 Sep 24 17:32 activate.fish -r-xr-xr-x 1 root root 219 Sep 24 17:32 chardetect -r-xr-xr-x 1 root root 221 Sep 24 17:32 dotenv -r-xr-xr-x 1 root root 225 Sep 24 17:32 f2py -r-xr-xr-x 1 root root 216 Sep 24 17:32 idna -r-xr-xr-x 1 root root 222 Sep 24 17:32 jsonschema -r-xr-xr-x 1 root root 228 Sep 24 17:32 jupyter -r-xr-xr-x 1 root root 228 Sep 24 17:32 jupyter-migrate -r-xr-xr-x 1 root root 233 Sep 24 17:32 jupyter-troubleshoot -r-xr-xr-x 1 root root 261 Sep 24 17:32 jupyter-trust -r-xr-xr-x 1 root root 228 Sep 24 17:32 jupytext -r-xr-xr-x 1 root root 232 Sep 24 17:32 jupytext-config -r-xr-xr-x 1 root root 218 Sep 24 17:32 kaggle -r-xr-xr-x 1 root root 229 Sep 24 17:32 markdown-it -r-xr-xr-x 1 root root 242 Sep 24 17:32 normalizer -r-xr-xr-x 1 root root 225 Sep 24 17:32 numpy-config -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip3 -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip3.12 lrwxrwxrwx 1 root root 21 Sep 24 17:32 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 24 17:32 python3 -> python lrwxrwxrwx 1 root root 6 Sep 24 17:32 python3.12 -> python -r-xr-xr-x 1 root root 224 Sep 24 17:32 slugify -r-xr-xr-x 1 root root 216 Sep 24 17:32 tqdm 2.3.3 2.2.6 1.7.2 18.1.0 ## Step 12 · agent · 2026-09-24T17:36:43.940Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:51.480Z { "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 df=pd.read_parquet(p)\\n print('\\\\n',p.name, df.shape, df.dtypes.astype(str).to_dict())\\n if 'timestamp' in df:\\n print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\\n if 'played_ratio_pct' in df: print(' pct q',df.played_ratio_pct.quantile([0,.1,.25,.5,.75,.8,.9,.95,.99,1]).round(2).to_dict())\\n elif 'state' in df: print(df.head().to_dict('records'), df.state.value_counts().to_dict(), 'uids',df.uid.nunique(),'items',df.item_id.nunique())\\n else: print(df.head().to_dict('records'))\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/f)\\n print('\\\\n',f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print(' eligibility count',np.quantile(cnt,[0,.01,.1,.5,.9,.99,1]),'mean',cnt.mean(),'aligned uids head',z['uids'][:5], 'items head',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:36:54.875Z Script completed Wall time 3.4 seconds Output: candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'} [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}, {'item_id': 3542184, 'positive_listens': 24075, 'organic_positive_listens': 17699, 'popularity_rank': 4}, {'item_id': 9378983, 'positive_listens': 24036, 'organic_positive_listens': 18195, 'popularity_rank': 5}] target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'} [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}, {'uid': 600, 'recent_positive_listens': 1985.0, 'history_positive_listens': 2423}, {'uid': 700, 'recent_positive_listens': 1776.0, 'history_positive_listens': 6114}] test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 432659, 'disliked': 52117} uids 7092 items 28863 train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 75 24786580 uids 5732 items 50876 organic {1: 52278, 0: 48797} train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 80 24786790 uids 8144 items 172303 organic {1: 461839, 0: 352992} train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} ts 0 24786795 uids 9238 items 845395 organic {1: 22477622, 0: 21006537} pct q {0.0: 0.0, 0.1: 1.0, 0.25: 7.0, 0.5: 100.0, 0.75: 100.0, 0.8: 100.0, 0.9: 100.0, 0.95: 100.0, 0.99: 100.0, 1.0: 159.0} train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 15950 24785395 uids 2769 items 14725 organic {1: 17751, 0: 2209} train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 250 24786570 uids 6197 items 113072 organic {1: 277065, 0: 15600} val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 24789920 25393280 uids 955 items 3135 organic {1: 1835, 0: 1728} val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 24788815 25393390 uids 4114 items 22902 organic {1: 25181, 0: 12614} val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} ts 24788600 25393395 uids 8196 items 191747 organic {1: 776486, 0: 725211} pct q {0.0: 0.0, 0.1: 1.0, 0.25: 9.0, 0.5: 100.0, 0.75: 100.0, 0.8: 100.0, 0.9: 100.0, 0.95: 100.0, 0.99: 100.0, 1.0: 159.0} val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 24810715 25393035 uids 205 items 579 organic {1: 527, 0: 79} val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} ts 24789005 25393385 uids 1313 items 8080 organic {1: 8905, 0: 441} validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 418435, 'disliked': 50689} uids 7038 items 28814 validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligibility count [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 aligned uids head [100 200 300 600 700] items head [5862961 6901374 5635052 3542184 9378983] test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligibility count [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 aligned uids head [100 200 300 600 700] items head [5862961 6901374 5635052 3542184 9378983] ## Step 13 · agent · 2026-09-24T17:37:05.647Z ### Thinking unavailable — no text recorded The catalog is broad but each user remains eligible for about 29,000 of 30,000 tracks, so retrieval is not doing most of the personalization. The main signal will need to come from behavior. I’m saving an eligibility-filtered popularity fallback now, then I’ll quantify repeat consumption and collaborative signal before choosing the heavier model. ### exec · 2026-09-24T17:37:15.710Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build validation and final rankings for the Yambda discovery task.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TOP_N = 100\\n+\\n+\\n+def popularity_ranking(eligibility_path: Path) -> pd.DataFrame:\\n+ \\\"\\\"\\\"Return the catalog-popularity baseline, restricted to eligibility.\\\"\\\"\\\"\\n+ with np.load(eligibility_path, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n+ rows = []\\n+ for row, uid in enumerate(uids):\\n+ mask = np.unpackbits(\\n+ packed[row], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[np.flatnonzero(mask)[:TOP_N]]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": uid,\\n+ \\\"item_id\\\": chosen,\\n+ \\\"rank\\\": np.arange(1, TOP_N + 1, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ return pd.concat(rows, ignore_index=True).astype(\\n+ {\\\"uid\\\": \\\"int64\\\", \\\"item_id\\\": \\\"int64\\\", \\\"rank\\\": \\\"int64\\\"}\\n+ )\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--baseline\\\", action=\\\"store_true\\\")\\n+ args = parser.parse_args()\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ if args.baseline:\\n+ popularity_ranking(DATA / \\\"validation_eligible_candidates.npz\\\").to_parquet(\\n+ OUT / \\\"validation.parquet\\\", index=False\\n+ )\\n+ popularity_ranking(DATA / \\\"test_eligible_candidates.npz\\\").to_parquet(\\n+ OUT / \\\"submission.parquet\\\", index=False\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:37:15.763Z Script completed Wall time 0.0 seconds Output: {} ## Step 14 · agent · 2026-09-24T17:37:17.162Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:20.782Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/build.py --baseline && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /task/data/train_listens.parquet /app/solution/*.parquet && /opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nprint('rowgroups',p.num_row_groups,'metadata rows',p.metadata.num_rows)\\nprint([(i,p.metadata.row_group(i).num_rows,p.metadata.row_group(i).total_byte_size) for i in range(min(5,p.num_row_groups))])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:37:27.101Z Script completed Wall time 6.3 seconds Output: total 16 drwxr-xr-x 1 agent agent 4096 Sep 24 17:37 . drwxr-xr-x 1 agent agent 4096 Sep 24 17:33 .. -rw-r--r-- 1 agent agent 1801 Sep 24 17:37 build.py { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 719K Sep 24 17:37 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 712K Sep 24 17:37 /app/solution/validation.parquet -r--r--r-- 1 root root 305M Sep 24 17:29 /task/data/train_listens.parquet rowgroups 42 metadata rows 43484159 [(0, 1048576, 9384750), (1, 1048576, 9426848), (2, 1048576, 9378670), (3, 1048576, 9382164), (4, 1048576, 9378969)] ## Step 15 · agent · 2026-09-24T17:37:39.901Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:53.453Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\\nfrom pathlib import Path\\nD=Path('/task/data')\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nz=np.load(D/'validation_eligible_candidates.npz')\\nurow={int(u):i for i,u in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,x):\\n c=icol.get(int(x)); r=urow.get(int(u)); return c is not None and r is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\na=l[(l.uid.isin(target))&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nb=k[(k.uid.isin(target))&(k.is_organic==1)][['uid','item_id']].drop_duplicates()\\ng=pd.concat([a.assign(grade=1),b.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\ng['eligible']=[elig(u,i) for u,i in g[['uid','item_id']].itertuples(index=False)]\\ng=g[g.eligible].copy(); g['poprank']=g.item_id.map(rank)\\nprint('gold',len(g),'users',g.uid.nunique(),'grades',g.grade.value_counts().to_dict(),'items',g.item_id.nunique(),'pop ranks',g.poprank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\n# encode truth pairs; scan each history family for overlap and counts/last\\nbase=10_000_000\\nkeys=(g.uid.astype(np.int64)*base+g.item_id.astype(np.int64)).to_numpy(); keyset=set(keys.tolist())\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n p=pq.ParquetFile(D/f'train_{fam}.parquet')\\n seen={}; last={}; org={}\\n cols=['uid','item_id','timestamp','is_organic'] + (['played_ratio_pct'] if fam=='listens' else [])\\n total=0; pos=0\\n for batch in p.iter_batches(batch_size=1<<20,columns=cols):\\n d=batch.to_pandas(); kk=d.uid.to_numpy(np.int64)*base+d.item_id.to_numpy(np.int64)\\n m=np.isin(kk,keys)\\n if m.any():\\n for q,t,o,*rest in zip(kk[m],d.timestamp.to_numpy()[m],d.is_organic.to_numpy()[m],*(([d.played_ratio_pct.to_numpy()[m]]) if fam=='listens' else [])):\\n if fam=='listens' and rest[0]<50: continue\\n seen[q]=seen.get(q,0)+1; last[q]=max(last.get(q,0),int(t)); org[q]=org.get(q,0)+int(o)\\n print(fam,'truth-pair repeat',len(seen),f'{len(seen)/len(keys):.3f}','events',sum(seen.values()),'org',sum(org.values()),'last q',np.quantile(list(last.values()),[0,.25,.5,.75,.9,.99,1]) if last else None)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});text(rest);text(output);\n" } ### Tool output · 2026-09-24T17:38:04.469Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-09-24T17:38:05.662Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:38:06.057Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:38:23.667Z Script completed Wall time 17.6 seconds Output: {"chunk_id":"8c46cf","wall_time_seconds":30.00060271,"session_id":53072,"original_token_count":0} ## Step 17 · agent · 2026-09-24T17:38:25.426Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:26.111Z { "code": "const r = await tools.write_stdin({session_id:53072,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:38:37.124Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 18 · agent · 2026-09-24T17:38:38.447Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:38:39.084Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:38:56.127Z Script completed Wall time 17.0 seconds Output: ## Step 19 · agent · 2026-09-24T17:38:57.414Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:58.320Z { "code": "const r = await tools.exec_command({cmd:\"ps aux | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:38:58.389Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: ps: not found ## Step 20 · agent · 2026-09-24T17:39:00.362Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:01.054Z { "code": "const r = await tools.write_stdin({session_id:53072,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:39:12.069Z Script running with cell ID 12 Wall time 11.0 seconds Output: ## Step 21 · agent · 2026-09-24T17:39:13.364Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:39:13.752Z { "cell_id": "12", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:39:31.073Z Script completed Wall time 17.3 seconds Output: {"chunk_id":"645c76","wall_time_seconds":30.00100254,"session_id":53072,"original_token_count":0,"output":""} ## Step 22 · agent · 2026-09-24T17:39:33.074Z ### Thinking unavailable — no text recorded The first scan is still running because it is checking history against each future-relevant pair exactly; that diagnostic is more expensive than useful at this point. I’m stopping it and switching to a single vectorized aggregation pass that will both answer the repeat question and produce the matrices needed for modeling. ### exec · 2026-09-24T17:39:34.982Z { "code": "const r = await tools.write_stdin({session_id:53072,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:39:35.067Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 16, in File "", line 10, 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 23 · agent · 2026-09-24T17:39:47.379Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-09-24T17:39:59.148Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-09-24T17:40:11.658Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-09-24T17:40:25.154Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-09-24T17:40:25.240Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:36.390Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\\nfrom pathlib import Path\\nD=Path('/task/data'); base=10_000_000\\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); targetset=set(targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nwith np.load(D/'validation_eligible_candidates.npz') as zz:\\n zu=zz['uids'].copy(); zi=zz['item_ids'].copy(); zp=zz['packed_eligible'].copy()\\nurow={int(u):i for i,u in enumerate(zu)}; icol={int(x):i for i,x in enumerate(zi)}\\ndef ev(df):\\n cs=np.array([icol.get(int(x),-1) for x in df.item_id],dtype=int); rs=np.array([urow.get(int(x),-1) for x in df.uid],dtype=int)\\n ok=(cs>=0)&(rs>=0); out=np.zeros(len(df),bool); out[ok]=((zp[rs[ok],cs[ok]//8]>>(cs[ok]%8))&1).astype(bool); return out\\nl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); a=l[(l.uid.isin(targetset))&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic']); b=k[(k.uid.isin(targetset))&(k.is_organic==1)][['uid','item_id']].drop_duplicates()\\ng=pd.concat([a.assign(grade=1),b.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); g=g[ev(g)].copy(); g['poprank']=g.item_id.map(rank)\\nprint('gold',len(g),'users',g.uid.nunique(),'grades',g.grade.value_counts().to_dict(),'poprank',g.poprank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).round(1).to_dict(),flush=True)\\nkeys=np.sort((g.uid.astype(np.int64)*base+g.item_id.astype(np.int64)).to_numpy()); found=np.zeros(len(keys),bool); posfound=np.zeros(len(keys),bool); last=np.zeros(len(keys),np.int64); cnt=np.zeros(len(keys),np.int32)\\np=pq.ParquetFile(D/'train_listens.parquet'); st=time.time(); totalcat=totalpos=0\\nfor bi,batch in enumerate(p.iter_batches(batch_size=1<<20,columns=['uid','item_id','timestamp','played_ratio_pct'])):\\n d=batch.to_pandas(); kk=d.uid.to_numpy(np.int64)*base+d.item_id.to_numpy(np.int64); ix=np.searchsorted(keys,kk); m=(ix=50\\n np.logical_or.at(posfound,jj[pm],True); np.maximum.at(last,jj[pm],d.timestamp.to_numpy()[m][pm]); np.add.at(cnt,jj[pm],1)\\n if bi%10==9: print('batch',bi,'elapsed',round(time.time()-st,1),flush=True)\\nprint('anyrepeat',found.sum(),found.mean(),'posrepeat',posfound.sum(),posfound.mean(),'count q',np.quantile(cnt[posfound],[0,.25,.5,.75,.9,.99,1]),'last age days q',np.quantile((24786800-last[posfound])/86400,[0,.25,.5,.75,.9,.99,1]),flush=True)\\n# state overlap/eligibility\\nfor fn in ['validation_preference_state.parquet','test_preference_state.parquet']:\\n s=pd.read_parquet(D/fn); print(fn,s.state.value_counts().to_dict(),'eligible frac validation',ev(s).mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:40:39.294Z Script completed Wall time 2.9 seconds Output: gold 37769 users 4263 grades {1: 33352, 3: 4417} poprank {'count': 37769.0, 'mean': 8463.1, 'std': 8118.0, 'min': 1.0, '10%': 428.0, '25%': 1733.0, '50%': 5622.0, '75%': 13374.0, '90%': 21680.2, '95%': 25693.6, '99%': 29101.0, 'max': 30000.0} batch 9 elapsed 0.4 batch 19 elapsed 0.8 batch 29 elapsed 1.2 batch 39 elapsed 1.5 Traceback (most recent call last): File "", line 24, in File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py", line 4537, in quantile return _quantile_unchecked( ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py", line 4550, in _quantile_unchecked return _ureduce(a, ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py", line 3894, in _ureduce r = func(a, **kwargs) ^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py", line 4727, in _quantile_ureduce_func result = _quantile(arr, ^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py", line 4853, in _quantile previous = arr[previous_indexes] ~~~^^^^^^^^^^^^^^^^^^ IndexError: index -1 is out of bounds for axis 0 with size 0 ## Step 28 · agent · 2026-09-24T17:40:44.225Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:56.069Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data'); base=10_000_001\\ntargetset=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nwith np.load(D/'validation_eligible_candidates.npz') as zz: zu=zz['uids'].copy(); zi=zz['item_ids'].copy(); zp=zz['packed_eligible'].copy()\\nurow={int(u):i for i,u in enumerate(zu)}; icol={int(x):i for i,x in enumerate(zi)}\\ndef ev(df):\\n cs=np.array([icol.get(int(x),-1) for x in df.item_id]); rs=np.array([urow.get(int(x),-1) for x in df.uid]); ok=(cs>=0)&(rs>=0); out=np.zeros(len(df),bool); out[ok]=((zp[rs[ok],cs[ok]//8]>>(cs[ok]%8))&1).astype(bool); return out\\nl=pd.read_parquet(D/'val_listens.parquet'); a=l[(l.uid.isin(targetset))&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); k=pd.read_parquet(D/'val_likes.parquet'); b=k[(k.uid.isin(targetset))&(k.is_organic==1)][['uid','item_id']].drop_duplicates(); g=pd.concat([a.assign(grade=1),b.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); g=g[ev(g)]\\nkeys=np.sort(g.uid.to_numpy(np.int64)*base+g.item_id.to_numpy(np.int64)); anyf=set(); posf=set();\\nfor batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct']):\\n d=batch.to_pandas(); kk=d.uid.to_numpy(np.int64)*base+d.item_id.to_numpy(np.int64); ix=np.searchsorted(keys,kk); valid=ix=50)].tolist())\\nprint('gold',len(keys),'any prior',len(anyf),'positive prior',len(posf))\\nfor fn,ef in [('validation_preference_state.parquet','validation'),('test_preference_state.parquet','test')]:\\n s=pd.read_parquet(D/fn); print(fn,s.state.value_counts().to_dict(),'eligible under validation',round(ev(s).mean(),5),pd.crosstab(s.state,ev(s)).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:41:00.000Z Script completed Wall time 3.9 seconds Output: gold 37769 any prior 0 positive prior 0 validation_preference_state.parquet {'liked': 418435, 'disliked': 50689} eligible under validation 0.0 {False: {'disliked': 50689, 'liked': 418435}} test_preference_state.parquet {'liked': 432659, 'disliked': 52117} eligible under validation 0.01998 {False: {'disliked': 51442, 'liked': 423649}, True: {'disliked': 675, 'liked': 9010}} ## Step 29 · agent · 2026-09-24T17:41:12.199Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-09-24T17:41:24.928Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-24T17:41:30.806Z ### Thinking unavailable — no text recorded The key structural finding is now clear: validation eligibility removes every track the user had previously listened to, liked, or disliked. This is a true new-item discovery problem—repeat/recency heuristics cannot score it. I’m therefore building a collaborative model over user taste histories, with popularity normalization, nearest-listener aggregation, and a low-rank taste component; only unseen eligible tracks are ranked. ### exec · 2026-09-24T17:42:32.081Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_cf.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Collaborative ranker experiments and final builder.\\n+\\n+The candidate service excludes a user's prior interactions. We therefore learn\\n+user taste from positive catalog interactions and transfer unseen tracks from\\n+similar listeners. The script can use train-only history for public validation\\n+or train+validation history for the final cutoff.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from sklearn.decomposition import TruncatedSVD\\n+from sklearn.preprocessing import normalize\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+VAL_CUTOFF = 25_393_400\\n+DAY = 86_400.0\\n+\\n+\\n+def positions(values: np.ndarray, sorted_values: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Return positions in a sorted vocabulary and a membership mask.\\\"\\\"\\\"\\n+ pos = np.searchsorted(sorted_values, values)\\n+ ok = pos < len(sorted_values)\\n+ ok[ok] = sorted_values[pos[ok]] == values[ok]\\n+ return pos, ok\\n+\\n+\\n+def add_events(\\n+ mat: sparse.csr_matrix,\\n+ rows: np.ndarray,\\n+ cols: np.ndarray,\\n+ vals: np.ndarray,\\n+) -> sparse.csr_matrix:\\n+ if not len(rows):\\n+ return mat\\n+ delta = sparse.coo_matrix((vals, (rows, cols)), shape=mat.shape).tocsr()\\n+ return mat + delta\\n+\\n+\\n+def history_matrices(final: bool) -> dict[str, sparse.csr_matrix]:\\n+ targets = np.sort(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64))\\n+ items = np.sort(pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").item_id.to_numpy(np.int64))\\n+ shape = (len(targets), len(items))\\n+ mats = {name: sparse.csr_matrix(shape, dtype=np.float32) for name in [\\\"pos\\\", \\\"organic\\\", \\\"recent\\\", \\\"like\\\"]}\\n+ families = [\\\"train\\\"] + ([\\\"val\\\"] if final else [])\\n+ cutoff = VAL_CUTOFF if final else TRAIN_CUTOFF\\n+ started = time.time()\\n+\\n+ for split in families:\\n+ pf = pq.ParquetFile(DATA / f\\\"{split}_listens.parquet\\\")\\n+ for batch in pf.iter_batches(\\n+ batch_size=1 << 20,\\n+ columns=[\\\"uid\\\", \\\"item_id\\\", \\\"timestamp\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ d = batch.to_pandas()\\n+ u = d.uid.to_numpy(np.int64)\\n+ it = d.item_id.to_numpy(np.int64)\\n+ rr, oku = positions(u, targets)\\n+ cc, oki = positions(it, items)\\n+ good = oku & oki & (d.played_ratio_pct.to_numpy() >= 50)\\n+ if not good.any():\\n+ continue\\n+ r, c = rr[good], cc[good]\\n+ ones = np.ones(len(r), np.float32)\\n+ mats[\\\"pos\\\"] = add_events(mats[\\\"pos\\\"], r, c, ones)\\n+ organic = d.is_organic.to_numpy()[good] == 1\\n+ mats[\\\"organic\\\"] = add_events(mats[\\\"organic\\\"], r[organic], c[organic], ones[organic])\\n+ age = np.maximum(0, cutoff - d.timestamp.to_numpy(np.float64)[good]) / DAY\\n+ rec = np.exp(-age / 120.0).astype(np.float32)\\n+ mats[\\\"recent\\\"] = add_events(mats[\\\"recent\\\"], r, c, rec)\\n+\\n+ # Exact active positive state is cleaner than reconstructing likes/unlikes.\\n+ state_name = \\\"test_preference_state.parquet\\\" if final else \\\"validation_preference_state.parquet\\\"\\n+ state = pd.read_parquet(DATA / state_name)\\n+ state = state[state.state == \\\"liked\\\"]\\n+ rr, oku = positions(state.uid.to_numpy(np.int64), targets)\\n+ cc, oki = positions(state.item_id.to_numpy(np.int64), items)\\n+ good = oku & oki\\n+ mats[\\\"like\\\"] = add_events(\\n+ mats[\\\"like\\\"], rr[good], cc[good], np.ones(good.sum(), np.float32)\\n+ )\\n+\\n+ # Presence, rather than play count, prevents a few looped tracks dominating taste.\\n+ for key in [\\\"pos\\\", \\\"organic\\\", \\\"like\\\"]:\\n+ mats[key].sum_duplicates()\\n+ mats[key].data[:] = 1.0\\n+ mats[key].eliminate_zeros()\\n+ # Multiple recent plays are useful but clipped.\\n+ mats[\\\"recent\\\"].data = np.minimum(mats[\\\"recent\\\"].data, 3.0)\\n+ print(\\n+ \\\"history\\\", \\\"final\\\" if final else \\\"validation\\\",\\n+ {k: (v.nnz, round(float(v.nnz / v.shape[0]), 1)) for k, v in mats.items()},\\n+ \\\"seconds\\\", round(time.time() - started, 1), flush=True,\\n+ )\\n+ return mats\\n+\\n+\\n+def eligibility(final: bool, sorted_items: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\\n+ name = \\\"test_eligible_candidates.npz\\\" if final else \\\"validation_eligible_candidates.npz\\\"\\n+ with np.load(DATA / name, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ source_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+ # Matrices use item-id order, while eligibility uses catalog popularity order.\\n+ order = np.searchsorted(source_items[np.argsort(source_items)], sorted_items)\\n+ sort_idx = np.argsort(source_items)\\n+ source_cols = sort_idx[order]\\n+ return uids, packed, source_cols\\n+\\n+\\n+def make_profile(mats: dict[str, sparse.csr_matrix], idf_power: float) -> sparse.csr_matrix:\\n+ x = mats[\\\"pos\\\"] + 0.8 * mats[\\\"organic\\\"] + 1.8 * mats[\\\"like\\\"] + 0.25 * mats[\\\"recent\\\"]\\n+ x = x.tocsr().astype(np.float32)\\n+ df = np.asarray((mats[\\\"pos\\\"] > 0).sum(axis=0)).ravel().astype(np.float32)\\n+ weight = np.power(np.log1p(x.shape[0] / (1.0 + df)), idf_power).astype(np.float32)\\n+ x = x.multiply(weight).tocsr()\\n+ return normalize(x, norm=\\\"l2\\\", axis=1, copy=False)\\n+\\n+\\n+def neighbor_graph(profile: sparse.csr_matrix, max_neighbors: int = 300) -> sparse.csr_matrix:\\n+ started = time.time()\\n+ model = NearestNeighbors(\\n+ n_neighbors=max_neighbors + 1, metric=\\\"cosine\\\", algorithm=\\\"brute\\\", n_jobs=-1\\n+ ).fit(profile)\\n+ dist, ind = model.kneighbors(profile, return_distance=True)\\n+ sim = np.maximum(0.0, 1.0 - dist[:, 1:]).astype(np.float32)\\n+ ind = ind[:, 1:]\\n+ rows = np.repeat(np.arange(profile.shape[0]), max_neighbors)\\n+ graph = sparse.csr_matrix((sim.ravel(), (rows, ind.ravel())), shape=(profile.shape[0], profile.shape[0]))\\n+ print(\\\"neighbors\\\", graph.shape, graph.nnz, \\\"seconds\\\", round(time.time() - started, 1), flush=True)\\n+ return graph\\n+\\n+\\n+def response_matrix(mats: dict[str, sparse.csr_matrix], pop_power: float) -> sparse.csr_matrix:\\n+ y = mats[\\\"pos\\\"] + 1.0 * mats[\\\"organic\\\"] + 2.5 * mats[\\\"like\\\"] + 0.35 * mats[\\\"recent\\\"]\\n+ y = y.tocsr().astype(np.float32)\\n+ df = np.asarray((mats[\\\"pos\\\"] > 0).sum(axis=0)).ravel().astype(np.float32)\\n+ y = y.multiply(np.power(1.0 + df, -pop_power).astype(np.float32)).tocsr()\\n+ return y\\n+\\n+\\n+def top_ranking(\\n+ final: bool,\\n+ mats: dict[str, sparse.csr_matrix],\\n+ graph: sparse.csr_matrix,\\n+ k_neighbors: int,\\n+ pop_power: float,\\n+ pop_blend: float,\\n+ svd_blend: float,\\n+ name: str,\\n+) -> Path:\\n+ targets = np.sort(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64))\\n+ items = np.sort(pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").item_id.to_numpy(np.int64))\\n+ euids, packed, source_cols = eligibility(final, items)\\n+ assert np.array_equal(targets, euids)\\n+\\n+ # Keep the k strongest pre-sorted neighbours from each row.\\n+ if k_neighbors < graph.getnnz(axis=1).max():\\n+ g = graph.copy()\\n+ for r in range(g.shape[0]):\\n+ a, b = g.indptr[r], g.indptr[r + 1]\\n+ if b - a > k_neighbors:\\n+ keep = np.argpartition(g.data[a:b], -k_neighbors)[-k_neighbors:]\\n+ mask = np.ones(b - a, bool); mask[keep] = False\\n+ g.data[a:b][mask] = 0\\n+ g.eliminate_zeros()\\n+ else:\\n+ g = graph\\n+ # Similarity mass normalization reduces activity-level effects.\\n+ g = normalize(g, norm=\\\"l1\\\", axis=1, copy=False)\\n+ y = response_matrix(mats, pop_power)\\n+\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").set_index(\\\"item_id\\\").loc[items]\\n+ pop = np.log1p(catalog.organic_positive_listens.to_numpy(np.float32))\\n+ pop = (pop - pop.mean()) / (pop.std() + 1e-6)\\n+\\n+ latent = None\\n+ if svd_blend:\\n+ started = time.time()\\n+ profile = make_profile(mats, 1.0)\\n+ svd = TruncatedSVD(n_components=128, n_iter=5, random_state=2026)\\n+ user_latent = svd.fit_transform(profile).astype(np.float32)\\n+ latent = (user_latent, svd.components_.astype(np.float32))\\n+ print(\\\"svd variance\\\", round(float(svd.explained_variance_ratio_.sum()), 4), \\\"seconds\\\", round(time.time()-started,1), flush=True)\\n+\\n+ out_u, out_i, out_r = [], [], []\\n+ block = 128\\n+ for lo in range(0, len(targets), block):\\n+ hi = min(len(targets), lo + block)\\n+ score = (g[lo:hi] @ y).toarray().astype(np.float32)\\n+ # Per-user standardization makes blend weights stable.\\n+ mu = score.mean(axis=1, keepdims=True); sd = score.std(axis=1, keepdims=True) + 1e-6\\n+ score = (score - mu) / sd\\n+ score += pop_blend * pop[None, :]\\n+ if latent is not None:\\n+ ls = latent[0][lo:hi] @ latent[1]\\n+ ls = (ls - ls.mean(axis=1, keepdims=True)) / (ls.std(axis=1, keepdims=True) + 1e-6)\\n+ score += svd_blend * ls\\n+ for j, row in enumerate(range(lo, hi)):\\n+ bits = np.unpackbits(packed[row], count=packed.shape[1] * 8, bitorder=\\\"little\\\")\\n+ eligible = bits[source_cols].astype(bool)\\n+ s = score[j]\\n+ s[~eligible] = -np.inf\\n+ best = np.argpartition(s, -100)[-100:]\\n+ best = best[np.argsort(s[best])[::-1]]\\n+ out_u.extend([int(targets[row])] * 100)\\n+ out_i.extend(items[best].tolist())\\n+ out_r.extend(range(1, 101))\\n+ frame = pd.DataFrame({\\\"uid\\\": out_u, \\\"item_id\\\": out_i, \\\"rank\\\": out_r}).astype(np.int64)\\n+ path = OUT / name\\n+ frame.to_parquet(path, index=False)\\n+ print(\\\"wrote\\\", path, len(frame), flush=True)\\n+ return path\\n+\\n+\\n+def main() -> None:\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--final\\\", action=\\\"store_true\\\")\\n+ ap.add_argument(\\\"--experiments\\\", action=\\\"store_true\\\")\\n+ ap.add_argument(\\\"--k\\\", type=int, default=150)\\n+ ap.add_argument(\\\"--pop-power\\\", type=float, default=0.25)\\n+ ap.add_argument(\\\"--pop-blend\\\", type=float, default=0.15)\\n+ ap.add_argument(\\\"--svd-blend\\\", type=float, default=0.15)\\n+ args = ap.parse_args()\\n+ OUT.mkdir(exist_ok=True)\\n+ mats = history_matrices(args.final)\\n+ profile = make_profile(mats, 1.0)\\n+ graph = neighbor_graph(profile, 300)\\n+ if args.experiments and not args.final:\\n+ configs = [\\n+ (50, 0.0, 0.0, 0.0),\\n+ (150, 0.0, 0.0, 0.0),\\n+ (300, 0.0, 0.0, 0.0),\\n+ (150, 0.25, 0.0, 0.0),\\n+ (150, 0.5, 0.0, 0.0),\\n+ (150, 0.25, 0.10, 0.0),\\n+ (150, 0.25, 0.20, 0.0),\\n+ (150, 0.25, 0.10, 0.15),\\n+ ]\\n+ for k, pp, pb, sb in configs:\\n+ name = f\\\"val_k{k}_pp{pp}_pb{pb}_sb{sb}.parquet\\\"\\n+ top_ranking(False, mats, graph, k, pp, pb, sb, name)\\n+ else:\\n+ name = \\\"submission.parquet\\\" if args.final else \\\"validation.parquet\\\"\\n+ top_ranking(args.final, mats, graph, args.k, args.pop_power, args.pop_blend, args.svd_blend, name)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:42:32.170Z Script completed Wall time 0.0 seconds Output: {} ## Step 32 · agent · 2026-09-24T17:42:34.741Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:36.025Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_cf.py --experiments\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:42:47.039Z Script running with cell ID 17 Wall time 11.0 seconds Output: ## Step 33 · agent · 2026-09-24T17:42:48.206Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:42:48.620Z { "cell_id": "17", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:43:06.199Z Script completed Wall time 17.6 seconds Output: history validation {'pos': (5514505, 652.1), 'organic': (2043504, 241.7), 'recent': (5514505, 652.1), 'like': (418435, 49.5)} seconds 8.4 neighbors (8456, 8456) 2536800 seconds 2.8 wrote /app/solution/val_k50_pp0.0_pb0.0_sb0.0.parquet 845600 wrote /app/solution/val_k150_pp0.0_pb0.0_sb0.0.parquet 845600 SESSION_ID=27177 ## Step 34 · agent · 2026-09-24T17:43:07.649Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:08.646Z { "code": "const r = await tools.write_stdin({session_id:27177,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:43:19.660Z Script running with cell ID 18 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:24.192Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:43:38.663Z Script completed Wall time 14.5 seconds Output: wrote /app/solution/val_k300_pp0.0_pb0.0_sb0.0.parquet 845600 wrote /app/solution/val_k150_pp0.25_pb0.0_sb0.0.parquet 845600 wrote /app/solution/val_k150_pp0.5_pb0.0_sb0.0.parquet 845600 SESSION_ID=27177 ## Step 35 · agent · 2026-09-24T17:43:40.022Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:40.971Z { "code": "const r = await tools.write_stdin({session_id:27177,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:43:51.986Z Script running with cell ID 19 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:53.410Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:44:10.245Z Script completed Wall time 16.8 seconds Output: wrote /app/solution/val_k150_pp0.25_pb0.1_sb0.0.parquet 845600 wrote /app/solution/val_k150_pp0.25_pb0.2_sb0.0.parquet 845600 svd variance 0.1997 seconds 3.5 wrote /app/solution/val_k150_pp0.25_pb0.1_sb0.15.parquet 845600 ## Step 36 · agent · 2026-09-24T17:44:11.727Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:13.250Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:44:24.263Z Script running with cell ID 20 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:44:25.795Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:44:33.626Z Script completed Wall time 7.8 seconds Output: { "model": "/app/solution/val_k150_pp0.0_pb0.0_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.020635, "quality_users": 4263 } } { "model": "/app/solution/val_k150_pp0.25_pb0.0_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.024249, "quality_users": 4263 } } { "model": "/app/solution/val_k150_pp0.25_pb0.1_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.024596, "quality_users": 4263 } } { "model": "/app/solution/val_k150_pp0.25_pb0.1_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.025172, "quality_users": 4263 } } { "model": "/app/solution/val_k150_pp0.25_pb0.2_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.024631, "quality_users": 4263 } } { "model": "/app/solution/val_k150_pp0.5_pb0.0_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.018533, "quality_users": 4263 } } { "model": "/app/solution/val_k300_pp0.0_pb0.0_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.019288, "quality_users": 4263 } } { "model": "/app/solution/val_k50_pp0.0_pb0.0_sb0.0.parquet", "scores": { "quality_ndcg@10": 0.021851, "quality_users": 4263 } } ## Step 37 · agent · 2026-09-24T17:44:47.369Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-24T17:44:58.941Z ### Thinking unavailable — no text recorded ## Step 39 · agent · 2026-09-24T17:45:00.323Z ### Thinking unavailable — no text recorded The first collaborative pass more than doubled the popularity baseline (0.02517 vs. 0.01071 NDCG@10). Its best result uses moderate popularity correction plus a small low-rank component. I’m tightening the useful region now—neighbor count, popularity debiasing, latent blend, and recent catalog trend—rather than spending time on clearly inferior families. ### exec · 2026-09-24T17:45:21.819Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_cf.py\\n@@\\n def top_ranking(\\n@@\\n pop_blend: float,\\n svd_blend: float,\\n+ trend_blend: float,\\n name: str,\\n ) -> Path:\\n@@\\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(np.float32))\\n pop = (pop - pop.mean()) / (pop.std() + 1e-6)\\n+ trend = np.log1p(np.asarray(mats[\\\"recent\\\"].sum(axis=0)).ravel().astype(np.float32))\\n+ trend = (trend - trend.mean()) / (trend.std() + 1e-6)\\n@@\\n- latent = None\\n+ latent = None\\n if svd_blend:\\n- started = time.time()\\n- profile = make_profile(mats, 1.0)\\n- svd = TruncatedSVD(n_components=128, n_iter=5, random_state=2026)\\n- user_latent = svd.fit_transform(profile).astype(np.float32)\\n- latent = (user_latent, svd.components_.astype(np.float32))\\n- print(\\\"svd variance\\\", round(float(svd.explained_variance_ratio_.sum()), 4), \\\"seconds\\\", round(time.time()-started,1), flush=True)\\n+ cache = getattr(top_ranking, \\\"latent_cache\\\", None)\\n+ if cache is None:\\n+ started = time.time()\\n+ profile = make_profile(mats, 1.0)\\n+ svd = TruncatedSVD(n_components=128, n_iter=5, random_state=2026)\\n+ user_latent = svd.fit_transform(profile).astype(np.float32)\\n+ cache = (user_latent, svd.components_.astype(np.float32))\\n+ top_ranking.latent_cache = cache\\n+ print(\\\"svd variance\\\", round(float(svd.explained_variance_ratio_.sum()), 4), \\\"seconds\\\", round(time.time()-started,1), flush=True)\\n+ latent = cache\\n@@\\n score = (score - mu) / sd\\n score += pop_blend * pop[None, :]\\n+ score += trend_blend * trend[None, :]\\n@@\\n ap.add_argument(\\\"--svd-blend\\\", type=float, default=0.15)\\n+ ap.add_argument(\\\"--trend-blend\\\", type=float, default=0.0)\\n@@\\n- configs = [\\n- (50, 0.0, 0.0, 0.0),\\n- (150, 0.0, 0.0, 0.0),\\n- (300, 0.0, 0.0, 0.0),\\n- (150, 0.25, 0.0, 0.0),\\n- (150, 0.5, 0.0, 0.0),\\n- (150, 0.25, 0.10, 0.0),\\n- (150, 0.25, 0.20, 0.0),\\n- (150, 0.25, 0.10, 0.15),\\n+ configs = [\\n+ (30, 0.25, 0.10, 0.15, 0.0),\\n+ (50, 0.25, 0.10, 0.15, 0.0),\\n+ (75, 0.25, 0.10, 0.15, 0.0),\\n+ (100, 0.25, 0.10, 0.15, 0.0),\\n+ (200, 0.25, 0.10, 0.15, 0.0),\\n+ (50, 0.10, 0.10, 0.20, 0.0),\\n+ (50, 0.15, 0.10, 0.20, 0.0),\\n+ (50, 0.20, 0.10, 0.20, 0.0),\\n+ (50, 0.30, 0.10, 0.20, 0.0),\\n+ (50, 0.35, 0.10, 0.20, 0.0),\\n+ (50, 0.25, 0.00, 0.30, 0.0),\\n+ (50, 0.25, 0.10, 0.30, 0.0),\\n+ (50, 0.25, 0.20, 0.30, 0.0),\\n+ (50, 0.25, 0.10, 0.50, 0.0),\\n+ (50, 0.25, 0.10, 0.20, 0.10),\\n+ (50, 0.25, 0.10, 0.20, 0.20),\\n ]\\n- for k, pp, pb, sb in configs:\\n- name = f\\\"val_k{k}_pp{pp}_pb{pb}_sb{sb}.parquet\\\"\\n- top_ranking(False, mats, graph, k, pp, pb, sb, name)\\n+ for k, pp, pb, sb, tb in configs:\\n+ name = f\\\"tune_k{k}_pp{pp}_pb{pb}_sb{sb}_tb{tb}.parquet\\\"\\n+ top_ranking(False, mats, graph, k, pp, pb, sb, tb, name)\\n@@\\n- top_ranking(args.final, mats, graph, args.k, args.pop_power, args.pop_blend, args.svd_blend, name)\\n+ top_ranking(args.final, mats, graph, args.k, args.pop_power, args.pop_blend, args.svd_blend, args.trend_blend, name)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:45:21.870Z Script completed Wall time 0.0 seconds Output: {} ## Step 40 · agent · 2026-09-24T17:45:23.582Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:24.864Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_cf.py --experiments\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:45:35.879Z Script running with cell ID 22 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:45:38.712Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:45:55.076Z Script completed Wall time 16.4 seconds Output: history validation {'pos': (5514505, 652.1), 'organic': (2043504, 241.7), 'recent': (5514505, 652.1), 'like': (418435, 49.5)} seconds 8.5 neighbors (8456, 8456) 2536800 seconds 2.7 svd variance 0.1997 seconds 3.6 wrote /app/solution/tune_k30_pp0.25_pb0.1_sb0.15_tb0.0.parquet 845600 SESSION_ID=20002 ## Step 41 · agent · 2026-09-24T17:45:56.381Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:57.335Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:46:08.398Z Script running with cell ID 23 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:10.347Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:46:27.395Z Script completed Wall time 17.0 seconds Output: wrote /app/solution/tune_k50_pp0.25_pb0.1_sb0.15_tb0.0.parquet 845600 wrote /app/solution/tune_k75_pp0.25_pb0.1_sb0.15_tb0.0.parquet 845600 wrote /app/solution/tune_k100_pp0.25_pb0.1_sb0.15_tb0.0.parquet 845600 SESSION_ID=20002 ## Step 42 · agent · 2026-09-24T17:46:31.421Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:32.367Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:46:43.401Z Script running with cell ID 24 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:45.475Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:47:02.405Z Script completed Wall time 16.9 seconds Output: wrote /app/solution/tune_k200_pp0.25_pb0.1_sb0.15_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.1_pb0.1_sb0.2_tb0.0.parquet 845600 SESSION_ID=20002 ## Step 43 · agent · 2026-09-24T17:47:06.863Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:06.868Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:47:17.904Z Script running with cell ID 25 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:19.563Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:47:36.908Z Script completed Wall time 17.3 seconds Output: wrote /app/solution/tune_k50_pp0.15_pb0.1_sb0.2_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.2_pb0.1_sb0.2_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.3_pb0.1_sb0.2_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.35_pb0.1_sb0.2_tb0.0.parquet 845600 SESSION_ID=20002 ## Step 44 · agent · 2026-09-24T17:47:38.288Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:39.263Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:47:50.282Z Script running with cell ID 26 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:51.965Z { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:48:09.279Z Script completed Wall time 17.3 seconds Output: wrote /app/solution/tune_k50_pp0.25_pb0.0_sb0.3_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.25_pb0.1_sb0.3_tb0.0.parquet 845600 SESSION_ID=20002 ## Step 45 · agent · 2026-09-24T17:48:10.563Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:11.563Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:48:22.582Z Script running with cell ID 27 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:25.497Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:48:41.594Z Script completed Wall time 16.1 seconds Output: wrote /app/solution/tune_k50_pp0.25_pb0.2_sb0.3_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.25_pb0.1_sb0.5_tb0.0.parquet 845600 wrote /app/solution/tune_k50_pp0.25_pb0.1_sb0.2_tb0.1.parquet 845600 SESSION_ID=20002 ## Step 46 · agent · 2026-09-24T17:48:45.641Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:45.643Z { "code": "const r = await tools.write_stdin({session_id:20002,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:48:45.739Z Script completed Wall time 0.0 seconds Output: wrote /app/solution/tune_k50_pp0.25_pb0.1_sb0.2_tb0.2.parquet 845600 ## Step 47 · agent · 2026-09-24T17:48:47.388Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:49.333Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/tune_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; printf '\\\\n'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:49:00.347Z Script running with cell ID 29 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:01.900Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:49:19.508Z Script completed Wall time 17.6 seconds Output: { "model": "/app/solution/tune_k100_pp0.25_pb0.1_sb0.15_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024493, "quality_users": 4263 } } { "model": "/app/solution/tune_k200_pp0.25_pb0.1_sb0.15_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.025779, "quality_users": 4263 } } { "model": "/app/solution/tune_k30_pp0.25_pb0.1_sb0.15_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.022004, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.15_pb0.1_sb0.2_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024727, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.1_pb0.1_sb0.2_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024224, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.0_sb0.3_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.023844, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.1_sb0.15_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024157, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.1_sb0.2_tb0.1.parquet", "scores": { "quality_ndcg@10": 0.024338, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.1_sb0.2_tb0.2.parquet", "scores": { "quality_ndcg@10": 0.024413, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.1_sb0.3_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.02404, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.25_pb0.1_sb0.5_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.02482, "quality_users": 4263 } } SESSION_ID=79503 ## Step 48 · agent · 2026-09-24T17:49:20.809Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:21.747Z { "code": "const r = await tools.write_stdin({session_id:79503,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:49:30.395Z Script completed Wall time 8.6 seconds Output: { "model": "/app/solution/tune_k50_pp0.25_pb0.2_sb0.3_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024308, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.2_pb0.1_sb0.2_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.024617, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.35_pb0.1_sb0.2_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.022163, "quality_users": 4263 } } { "model": "/app/solution/tune_k50_pp0.3_pb0.1_sb0.2_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.023286, "quality_users": 4263 } } { "model": "/app/solution/tune_k75_pp0.25_pb0.1_sb0.15_tb0.0.parquet", "scores": { "quality_ndcg@10": 0.023673, "quality_users": 4263 } } ## Step 49 · agent · 2026-09-24T17:49:43.431Z ### Thinking unavailable — no text recorded ## Step 50 · agent · 2026-09-24T17:49:52.223Z ### Thinking unavailable — no text recorded The broader sweep moved the best score to 0.02578 with roughly 200 taste-neighbors; very small neighborhoods were noisier, while aggressive popularity correction removed too many valid mainstream tracks. One important axis remains underexplored: whether similarity and neighbor transfer should emphasize organic, liked, or recent behavior. I’m testing those signal mixtures now, plus the 200–300-neighbor boundary. ### exec · 2026-09-24T17:50:02.218Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_cf.py\\n@@\\n-def make_profile(mats: dict[str, sparse.csr_matrix], idf_power: float) -> sparse.csr_matrix:\\n- x = mats[\\\"pos\\\"] + 0.8 * mats[\\\"organic\\\"] + 1.8 * mats[\\\"like\\\"] + 0.25 * mats[\\\"recent\\\"]\\n+def make_profile(mats: dict[str, sparse.csr_matrix], idf_power: float, mode: int = 0) -> sparse.csr_matrix:\\n+ weights = {\\n+ 0: (1.0, 0.8, 1.8, 0.25),\\n+ 1: (0.35, 1.5, 2.5, 0.8),\\n+ 2: (0.25, 0.8, 1.5, 1.5),\\n+ 3: (1.0, 2.0, 3.0, 0.5),\\n+ }[mode]\\n+ x = sum(w * mats[k] for w, k in zip(weights, [\\\"pos\\\", \\\"organic\\\", \\\"like\\\", \\\"recent\\\"]))\\n@@\\n-def response_matrix(mats: dict[str, sparse.csr_matrix], pop_power: float) -> sparse.csr_matrix:\\n- y = mats[\\\"pos\\\"] + 1.0 * mats[\\\"organic\\\"] + 2.5 * mats[\\\"like\\\"] + 0.35 * mats[\\\"recent\\\"]\\n+def response_matrix(mats: dict[str, sparse.csr_matrix], pop_power: float, mode: int = 0) -> sparse.csr_matrix:\\n+ weights = {\\n+ 0: (1.0, 1.0, 2.5, 0.35),\\n+ 1: (0.25, 2.0, 4.0, 0.75),\\n+ 2: (0.5, 1.5, 3.0, 1.5),\\n+ 3: (1.0, 2.0, 4.0, 0.35),\\n+ }[mode]\\n+ y = sum(w * mats[k] for w, k in zip(weights, [\\\"pos\\\", \\\"organic\\\", \\\"like\\\", \\\"recent\\\"]))\\n@@\\n trend_blend: float,\\n name: str,\\n+ response_mode: int = 0,\\n ) -> Path:\\n@@\\n- y = response_matrix(mats, pop_power)\\n+ y = response_matrix(mats, pop_power, response_mode)\\n@@\\n if args.experiments and not args.final:\\n@@\\n top_ranking(False, mats, graph, k, pp, pb, sb, tb, name)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:50:02.271Z Script completed Wall time 0.0 seconds Output: {} ## Step 51 · agent · 2026-09-24T17:50:05.583Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:13.924Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/solution')\\nimport rank_cf as r\\nm=r.history_matrices(False)\\ngraphs={\\n 'idf05':r.neighbor_graph(r.make_profile(m,.5,0),300),\\n 'base':r.neighbor_graph(r.make_profile(m,1.,0),300),\\n 'idf15':r.neighbor_graph(r.make_profile(m,1.5,0),300),\\n 'organic':r.neighbor_graph(r.make_profile(m,1.,1),300),\\n 'recent':r.neighbor_graph(r.make_profile(m,1.,2),300),\\n 'orgstrong':r.neighbor_graph(r.make_profile(m,1.,3),300),\\n}\\n# graph tuning on the same transfer model\\nfor gn in ['idf05','idf15','organic','recent','orgstrong']:\\n r.top_ranking(False,m,graphs[gn],200,.25,.10,.15,0.,f'sig_{gn}_y0.parquet',0)\\n# transfer-signal mixtures with the base graph\\nfor ym in [1,2,3]:\\n r.top_ranking(False,m,graphs['base'],200,.25,.10,.15,0.,f'sig_base_y{ym}.parquet',ym)\\n# neighbourhood and blend boundary\\nfor k,pp,pb,sb in [(250,.25,.1,.15),(300,.25,.1,.15),(200,.20,.1,.15),(200,.30,.1,.15),(200,.25,.05,.25),(200,.25,.1,.30)]:\\n r.top_ranking(False,m,graphs['base'],k,pp,pb,sb,0.,f'sig_k{k}_pp{pp}_pb{pb}_sb{sb}.parquet',0)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:50:24.940Z Script running with cell ID 32 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:50:26.704Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:50:44.099Z Script completed Wall time 17.4 seconds Output: history validation {'pos': (5514505, 652.1), 'organic': (2043504, 241.7), 'recent': (5514505, 652.1), 'like': (418435, 49.5)} seconds 8.4 neighbors (8456, 8456) 2536800 seconds 2.9 neighbors (8456, 8456) 2536800 seconds 2.8 neighbors (8456, 8456) 2536800 seconds 2.8 neighbors (8456, 8456) 2536800 seconds 2.7 neighbors (8456, 8456) 2536800 seconds 2.7 neighbors (8456, 8456) 2536800 seconds 2.8 SESSION_ID=92038 ## Step 52 · agent · 2026-09-24T17:50:45.363Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:46.299Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:50:57.367Z Script running with cell ID 33 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:50:58.992Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:51:16.376Z Script completed Wall time 17.4 seconds Output: svd variance 0.1997 seconds 3.5 wrote /app/solution/sig_idf05_y0.parquet 845600 wrote /app/solution/sig_idf15_y0.parquet 845600 SESSION_ID=92038 ## Step 53 · agent · 2026-09-24T17:51:17.626Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:18.563Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:51:29.594Z Script running with cell ID 34 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:51:31.084Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:51:48.596Z Script completed Wall time 17.5 seconds Output: wrote /app/solution/sig_organic_y0.parquet 845600 wrote /app/solution/sig_recent_y0.parquet 845600 SESSION_ID=92038 ## Step 54 · agent · 2026-09-24T17:51:49.877Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:50.863Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms\":30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:51:50.931Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ## Step 55 · agent · 2026-09-24T17:51:53.201Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:54.048Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:52:05.066Z Script running with cell ID 36 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:06.734Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:52:24.081Z Script completed Wall time 17.3 seconds Output: wrote /app/solution/sig_orgstrong_y0.parquet 845600 wrote /app/solution/sig_base_y1.parquet 845600 SESSION_ID=92038 ## Step 56 · agent · 2026-09-24T17:52:25.493Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:26.474Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:52:37.498Z Script running with cell ID 37 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:39.463Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:52:56.503Z Script completed Wall time 17.0 seconds Output: wrote /app/solution/sig_base_y2.parquet 845600 wrote /app/solution/sig_base_y3.parquet 845600 wrote /app/solution/sig_k250_pp0.25_pb0.1_sb0.15.parquet 845600 SESSION_ID=92038 ## Step 57 · agent · 2026-09-24T17:52:57.836Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:58.790Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:53:09.804Z Script running with cell ID 38 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:12.600Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:53:28.816Z Script completed Wall time 16.2 seconds Output: wrote /app/solution/sig_k300_pp0.25_pb0.1_sb0.15.parquet 845600 wrote /app/solution/sig_k200_pp0.2_pb0.1_sb0.15.parquet 845600 SESSION_ID=92038 ## Step 58 · agent · 2026-09-24T17:53:30.155Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:31.112Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms\":30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:53:31.207Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ## Step 59 · agent · 2026-09-24T17:53:32.508Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:33.463Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:53:44.497Z Script running with cell ID 40 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:46.107Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:54:03.500Z Script completed Wall time 17.4 seconds Output: wrote /app/solution/sig_k200_pp0.3_pb0.1_sb0.15.parquet 845600 wrote /app/solution/sig_k200_pp0.25_pb0.05_sb0.25.parquet 845600 SESSION_ID=92038 ## Step 60 · agent · 2026-09-24T17:54:05.219Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:06.177Z { "code": "const r = await tools.write_stdin({session_id:92038,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:54:09.054Z Script completed Wall time 2.9 seconds Output: wrote /app/solution/sig_k200_pp0.25_pb0.1_sb0.3.parquet 845600 ## Step 61 · agent · 2026-09-24T17:54:10.829Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:12.747Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/sig_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; printf '\\\\n'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:54:23.760Z Script running with cell ID 42 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:54:25.708Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:54:42.967Z Script completed Wall time 17.3 seconds Output: { "model": "/app/solution/sig_base_y1.parquet", "scores": { "quality_ndcg@10": 0.026598, "quality_users": 4263 } } { "model": "/app/solution/sig_base_y2.parquet", "scores": { "quality_ndcg@10": 0.025537, "quality_users": 4263 } } { "model": "/app/solution/sig_base_y3.parquet", "scores": { "quality_ndcg@10": 0.026209, "quality_users": 4263 } } { "model": "/app/solution/sig_idf05_y0.parquet", "scores": { "quality_ndcg@10": 0.024716, "quality_users": 4263 } } { "model": "/app/solution/sig_idf15_y0.parquet", "scores": { "quality_ndcg@10": 0.025283, "quality_users": 4263 } } { "model": "/app/solution/sig_k200_pp0.25_pb0.05_sb0.25.parquet", "scores": { "quality_ndcg@10": 0.02573, "quality_users": 4263 } } { "model": "/app/solution/sig_k200_pp0.25_pb0.1_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.025992, "quality_users": 4263 } } { "model": "/app/solution/sig_k200_pp0.2_pb0.1_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.025172, "quality_users": 4263 } } { "model": "/app/solution/sig_k200_pp0.3_pb0.1_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.025163, "quality_users": 4263 } } { "model": "/app/solution/sig_k250_pp0.25_pb0.1_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.02535, "quality_users": 4263 } } { "model": "/app/solution/sig_k300_pp0.25_pb0.1_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.025174, "quality_users": 4263 } } SESSION_ID=75689 ## Step 62 · agent · 2026-09-24T17:54:44.444Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:45.227Z { "code": "const r = await tools.write_stdin({session_id:75689,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:54:48.477Z Script completed Wall time 3.2 seconds Output: { "model": "/app/solution/sig_organic_y0.parquet", "scores": { "quality_ndcg@10": 0.026613, "quality_users": 4263 } } { "model": "/app/solution/sig_orgstrong_y0.parquet", "scores": { "quality_ndcg@10": 0.026544, "quality_users": 4263 } } { "model": "/app/solution/sig_recent_y0.parquet", "scores": { "quality_ndcg@10": 0.026183, "quality_users": 4263 } } ## Step 63 · agent · 2026-09-24T17:54:58.619Z ### Thinking unavailable — no text recorded Organic behavior matters in both places: emphasizing it when finding similar listeners and when transferring tracks each adds about 0.0008 NDCG, reaching 0.02661. Those two gains come from different score constructions, so I’m testing a small rank ensemble for complementarity; if it holds, the final refit will produce and combine both constituent models. ### exec · 2026-09-24T17:55:10.989Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/ensemble.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fuse several valid top-100 rankings with weighted reciprocal rank.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+def fuse(paths: list[Path], weights: list[float], output: Path, offset: float = 40.0) -> None:\\n+ frames = [pd.read_parquet(p) for p in paths]\\n+ uids = np.sort(frames[0].uid.unique())\\n+ by_model = [\\n+ {int(u): list(map(int, g.sort_values(\\\"rank\\\").item_id)) for u, g in f.groupby(\\\"uid\\\")}\\n+ for f in frames\\n+ ]\\n+ rows = []\\n+ for uid in uids:\\n+ scores: dict[int, float] = {}\\n+ best_rank: dict[int, int] = {}\\n+ for model, weight in zip(by_model, weights):\\n+ for rank, item in enumerate(model[int(uid)], 1):\\n+ scores[item] = scores.get(item, 0.0) + weight / (offset + rank)\\n+ best_rank[item] = min(best_rank.get(item, 10_000), rank)\\n+ chosen = sorted(scores, key=lambda x: (-scores[x], best_rank[x], x))[:100]\\n+ rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).astype(np.int64).to_parquet(output, index=False)\\n+\\n+\\n+def main() -> None:\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"output\\\", type=Path)\\n+ ap.add_argument(\\\"inputs\\\", nargs=\\\"+\\\", type=Path)\\n+ ap.add_argument(\\\"--weights\\\", nargs=\\\"+\\\", type=float)\\n+ ap.add_argument(\\\"--offset\\\", type=float, default=40.0)\\n+ args = ap.parse_args()\\n+ weights = args.weights or [1.0] * len(args.inputs)\\n+ if len(weights) != len(args.inputs):\\n+ raise ValueError(\\\"one weight is required per input\\\")\\n+ fuse(args.inputs, weights, args.output, args.offset)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:55:11.089Z Script completed Wall time 0.0 seconds Output: {} ## Step 64 · agent · 2026-09-24T17:55:13.458Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:18.984Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/app/solution'); from ensemble import fuse\\nD=Path('/app/solution')\\nA=D/'sig_organic_y0.parquet'; B=D/'sig_base_y1.parquet'; C=D/'sig_orgstrong_y0.parquet'; E=D/'sig_recent_y0.parquet'\\nfor off in [0,10,30,60,100]:\\n for label,paths,w in [('ab',[A,B],[1,1]),('abc',[A,B,C],[1,1,1]),('abe',[A,B,E],[1,1,.5]),('ab2',[A,B],[2,1]),('a2b',[A,B],[1,2])]:\\n fuse(paths,w,D/f'ens_{label}_o{off}.parquet',off)\\nPY\\nfor f in /app/solution/ens_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; printf '\\\\n'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:55:30.000Z Script running with cell ID 45 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:55:33.264Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:55:49.170Z Script completed Wall time 15.9 seconds Output: SESSION_ID=7633 ## Step 65 · agent · 2026-09-24T17:55:52.242Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:53.155Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:56:04.171Z Script running with cell ID 46 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:05.972Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:56:23.174Z Script completed Wall time 17.2 seconds Output: SESSION_ID=7633 ## Step 66 · agent · 2026-09-24T17:56:24.793Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:25.752Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:56:36.768Z Script running with cell ID 47 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:38.455Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:56:55.771Z Script completed Wall time 17.3 seconds Output: SESSION_ID=7633 ## Step 67 · agent · 2026-09-24T17:56:58.269Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:58.895Z { "code": "const r = await tools.exec_command({cmd:\"ls /app/solution/ens_*.parquet 2>/dev/null | wc -l\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:56:58.967Z Script completed Wall time 0.0 seconds Output: 22 ## Step 68 · agent · 2026-09-24T17:57:00.968Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:01.935Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:57:12.950Z Script running with cell ID 49 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:57:14.670Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:57:31.965Z Script completed Wall time 17.3 seconds Output: { "model": "/app/solution/ens_a2b_o0.parquet", "scores": { "quality_ndcg@10": 0.026738, "quality_users": 4263 } } { "model": "/app/solution/ens_a2b_o10.parquet", "scores": { "quality_ndcg@10": 0.026787, "quality_users": 4263 } } { "model": "/app/solution/ens_a2b_o100.parquet", "scores": { "quality_ndcg@10": 0.026562, "quality_users": 4263 } } { "model": "/app/solution/ens_a2b_o30.parquet", "scores": { "quality_ndcg@10": 0.026612, "quality_users": 4263 } } { "model": "/app/solution/ens_a2b_o60.parquet", "scores": { "quality_ndcg@10": 0.026559, "quality_users": 4263 } } { "model": "/app/solution/ens_ab2_o0.parquet", "scores": { "quality_ndcg@10": 0.026977, "quality_users": 4263 } } { "model": "/app/solution/ens_ab2_o10.parquet", "scores": { "quality_ndcg@10": 0.026573, "quality_users": 4263 } } { "model": "/app/solution/ens_ab2_o100.parquet", "scores": { "quality_ndcg@10": 0.026499, "quality_users": 4263 } } SESSION_ID=7633 ## Step 69 · agent · 2026-09-24T17:57:33.873Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:34.793Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms\":30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:57:34.895Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Invalid or unexpected token ## Step 70 · agent · 2026-09-24T17:57:36.551Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:37.266Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:57:48.281Z Script running with cell ID 51 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:57:50.035Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:58:07.284Z Script completed Wall time 17.2 seconds Output: { "model": "/app/solution/ens_ab2_o30.parquet", "scores": { "quality_ndcg@10": 0.026549, "quality_users": 4263 } } { "model": "/app/solution/ens_ab2_o60.parquet", "scores": { "quality_ndcg@10": 0.026512, "quality_users": 4263 } } { "model": "/app/solution/ens_ab_o0.parquet", "scores": { "quality_ndcg@10": 0.026982, "quality_users": 4263 } } { "model": "/app/solution/ens_ab_o10.parquet", "scores": { "quality_ndcg@10": 0.026745, "quality_users": 4263 } } { "model": "/app/solution/ens_ab_o100.parquet", "scores": { "quality_ndcg@10": 0.026627, "quality_users": 4263 } } { "model": "/app/solution/ens_ab_o30.parquet", "scores": { "quality_ndcg@10": 0.02662, "quality_users": 4263 } } { "model": "/app/solution/ens_ab_o60.parquet", "scores": { "quality_ndcg@10": 0.026632, "quality_users": 4263 } } { "model": "/app/solution/ens_abc_o0.parquet", "scores": { "quality_ndcg@10": 0.026712, "quality_users": 4263 } } { "model": "/app/solution/ens_abc_o10.parquet", "scores": { "quality_ndcg@10": 0.026547, "quality_users": 4263 } } { "model": "/app/solution/ens_abc_o100.parquet", "scores": { "quality_ndcg@10": 0.026476, "quality_users": 4263 } } { "model": "/app/solution/ens_abc_o30.parquet", "scores": { "quality_ndcg@10": 0.026428, "quality_users": 4263 } } { "model": "/app/solution/ens_abc_o60.parquet", "scores": { "quality_ndcg@10": 0.026461, "quality_users": 4263 } } { "model": "/app/solution/ens_abe_o0.parquet", "scores": { "quality_ndcg@10": 0.026954, "quality_users": 4263 } } SESSION_ID=7633 ## Step 71 · agent · 2026-09-24T17:58:09.090Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:10.053Z { "code": "const r = await tools.write_stdin({session_id:7633,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:58:15.116Z Script completed Wall time 5.1 seconds Output: { "model": "/app/solution/ens_abe_o10.parquet", "scores": { "quality_ndcg@10": 0.026765, "quality_users": 4263 } } { "model": "/app/solution/ens_abe_o100.parquet", "scores": { "quality_ndcg@10": 0.026618, "quality_users": 4263 } } { "model": "/app/solution/ens_abe_o30.parquet", "scores": { "quality_ndcg@10": 0.026665, "quality_users": 4263 } } { "model": "/app/solution/ens_abe_o60.parquet", "scores": { "quality_ndcg@10": 0.026624, "quality_users": 4263 } } ## Step 72 · agent · 2026-09-24T17:58:19.282Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:23.374Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/solution'); import rank_cf as r\\nm=r.history_matrices(False); g=r.neighbor_graph(r.make_profile(m,1.,1),300)\\nfor k,pp,sb in [(150,.25,.15),(200,.25,.15),(250,.25,.15),(200,.20,.15),(200,.30,.15),(200,.25,.3)]:\\n r.top_ranking(False,m,g,k,pp,.1,sb,0.,f'hyb_k{k}_pp{pp}_sb{sb}.parquet',1)\\nPY\\nfor f in /app/solution/hyb_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; printf '\\\\n'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:58:34.391Z Script running with cell ID 53 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:37.068Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:58:53.566Z Script completed Wall time 16.5 seconds Output: history validation {'pos': (5514505, 652.1), 'organic': (2043504, 241.7), 'recent': (5514505, 652.1), 'like': (418435, 49.5)} seconds 8.4 neighbors (8456, 8456) 2536800 seconds 2.8 svd variance 0.1997 seconds 3.4 SESSION_ID=89979 ## Step 73 · agent · 2026-09-24T17:58:55.135Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:56.111Z { "code": "const r = await tools.write_stdin({session_id:89979,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:59:07.125Z Script running with cell ID 54 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:59:09.163Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:59:26.127Z Script completed Wall time 17.0 seconds Output: wrote /app/solution/hyb_k150_pp0.25_sb0.15.parquet 845600 wrote /app/solution/hyb_k200_pp0.25_sb0.15.parquet 845600 wrote /app/solution/hyb_k250_pp0.25_sb0.15.parquet 845600 SESSION_ID=89979 ## Step 74 · agent · 2026-09-24T17:59:28.412Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:59:29.408Z { "code": "const r = await tools.write_stdin({session_id:89979,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T17:59:40.488Z Script running with cell ID 55 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:59:42.063Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:59:59.492Z Script completed Wall time 17.4 seconds Output: wrote /app/solution/hyb_k200_pp0.2_sb0.15.parquet 845600 wrote /app/solution/hyb_k200_pp0.3_sb0.15.parquet 845600 SESSION_ID=89979 ## Step 75 · agent · 2026-09-24T18:00:01.863Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:00:04.063Z { "code": "const r = await tools.write_stdin({session_id:89979,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:00:15.096Z Script running with cell ID 56 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:20.047Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T18:00:21.938Z Script completed Wall time 1.9 seconds Output: wrote /app/solution/hyb_k200_pp0.25_sb0.3.parquet 845600 { "model": "/app/solution/hyb_k150_pp0.25_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.027696, "quality_users": 4263 } } { "model": "/app/solution/hyb_k200_pp0.25_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.028059, "quality_users": 4263 } } { "model": "/app/solution/hyb_k200_pp0.25_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.028207, "quality_users": 4263 } } { "model": "/app/solution/hyb_k200_pp0.2_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.027656, "quality_users": 4263 } } { "model": "/app/solution/hyb_k200_pp0.3_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.028303, "quality_users": 4263 } } { "model": "/app/solution/hyb_k250_pp0.25_sb0.15.parquet", "scores": { "quality_ndcg@10": 0.027809, "quality_users": 4263 } } ## Step 76 · agent · 2026-09-24T18:00:27.781Z ### Thinking unavailable — no text recorded Combining the two organic-weighted choices directly is substantially better than rank fusion: validation reaches 0.02830, about 164% above popularity. I’m doing one final local refinement of that hybrid’s blend weights, then I’ll freeze the configuration, refit on train plus validation, validate every contract condition, and document the build. ### exec · 2026-09-24T18:00:32.977Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/solution'); import rank_cf as r\\nm=r.history_matrices(False); g=r.neighbor_graph(r.make_profile(m,1.,1),300)\\nfor pp,pb,sb in [(.30,.1,.30),(.30,.1,.50),(.35,.1,.30),(.30,0.,.30),(.30,.2,.30)]:\\n r.top_ranking(False,m,g,200,pp,pb,sb,0.,f'last_pp{pp}_pb{pb}_sb{sb}.parquet',1)\\nPY\\nfor f in /app/solution/last_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; printf '\\\\n'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:00:43.994Z Script running with cell ID 57 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:46.863Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:01:03.168Z Script completed Wall time 16.3 seconds Output: history validation {'pos': (5514505, 652.1), 'organic': (2043504, 241.7), 'recent': (5514505, 652.1), 'like': (418435, 49.5)} seconds 8.4 neighbors (8456, 8456) 2536800 seconds 2.8 svd variance 0.1997 seconds 3.4 SESSION_ID=18560 ## Step 77 · agent · 2026-09-24T18:01:05.163Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:06.393Z { "code": "const r = await tools.write_stdin({session_id:18560,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:01:17.407Z Script running with cell ID 58 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:20.464Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:01:36.410Z Script completed Wall time 15.9 seconds Output: wrote /app/solution/last_pp0.3_pb0.1_sb0.3.parquet 845600 wrote /app/solution/last_pp0.3_pb0.1_sb0.5.parquet 845600 wrote /app/solution/last_pp0.35_pb0.1_sb0.3.parquet 845600 SESSION_ID=18560 ## Step 78 · agent · 2026-09-24T18:01:38.792Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:39.763Z { "code": "const r = await tools.write_stdin({session_id:18560,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:01:50.798Z Script running with cell ID 59 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:54.763Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:02:09.800Z Script completed Wall time 15.0 seconds Output: wrote /app/solution/last_pp0.3_pb0.0_sb0.3.parquet 845600 wrote /app/solution/last_pp0.3_pb0.2_sb0.3.parquet 845600 { "model": "/app/solution/last_pp0.35_pb0.1_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.028487, "quality_users": 4263 } } { "model": "/app/solution/last_pp0.3_pb0.0_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.028501, "quality_users": 4263 } } { "model": "/app/solution/last_pp0.3_pb0.1_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.028549, "quality_users": 4263 } } SESSION_ID=18560 ## Step 79 · agent · 2026-09-24T18:02:11.190Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:12.156Z { "code": "const r = await tools.write_stdin({session_id:18560,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:02:13.188Z Script completed Wall time 1.0 seconds Output: { "model": "/app/solution/last_pp0.3_pb0.1_sb0.5.parquet", "scores": { "quality_ndcg@10": 0.028173, "quality_users": 4263 } } { "model": "/app/solution/last_pp0.3_pb0.2_sb0.3.parquet", "scores": { "quality_ndcg@10": 0.028518, "quality_users": 4263 } } ## Step 80 · agent · 2026-09-24T18:02:16.076Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:23.082Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_cf.py\\n@@\\n- ap.add_argument(\\\"--k\\\", type=int, default=150)\\n- ap.add_argument(\\\"--pop-power\\\", type=float, default=0.25)\\n- ap.add_argument(\\\"--pop-blend\\\", type=float, default=0.15)\\n- ap.add_argument(\\\"--svd-blend\\\", type=float, default=0.15)\\n+ ap.add_argument(\\\"--k\\\", type=int, default=200)\\n+ ap.add_argument(\\\"--pop-power\\\", type=float, default=0.30)\\n+ ap.add_argument(\\\"--pop-blend\\\", type=float, default=0.10)\\n+ ap.add_argument(\\\"--svd-blend\\\", type=float, default=0.30)\\n ap.add_argument(\\\"--trend-blend\\\", type=float, default=0.0)\\n+ ap.add_argument(\\\"--profile-mode\\\", type=int, default=1)\\n+ ap.add_argument(\\\"--response-mode\\\", type=int, default=1)\\n@@\\n- profile = make_profile(mats, 1.0)\\n+ profile = make_profile(mats, 1.0, args.profile_mode)\\n@@\\n- top_ranking(args.final, mats, graph, args.k, args.pop_power, args.pop_blend, args.svd_blend, args.trend_blend, name)\\n+ top_ranking(\\n+ args.final, mats, graph, args.k, args.pop_power, args.pop_blend,\\n+ args.svd_blend, args.trend_blend, name, args.response_mode\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T18:02:23.160Z Script completed Wall time 0.0 seconds Output: {} ## Step 81 · agent · 2026-09-24T18:02:25.075Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:28.928Z { "code": "const r = await tools.update_plan({explanation:\"Data analysis established that eligibility excludes prior user-item interactions. The selected organic-weighted neighbor + SVD hybrid scores 0.028549 public NDCG@10; proceeding to final refit and checks.\",plan:[\n {step:\"Inspect data, schemas, distributions, and evaluator mechanics\",\"status\":\"completed\"},\n {step:\"Create a valid baseline submission and validation ranking\",\"status\":\"completed\"},\n {step:\"Develop and evaluate personalized temporal ranking models\",\"status\":\"completed\"},\n {step:\"Refit selected model on train + validation and build final submission\",\"status\":\"in_progress\"},\n {step:\"Validate artifact and document reproducible build\",\"status\":\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_cf.py --final\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:02:29.024Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Identifier 'r' has already been declared ## Step 82 · agent · 2026-09-24T18:02:30.756Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:34.696Z { "code": "const x = await tools.update_plan({explanation:\"Data analysis established that eligibility excludes prior user-item interactions. The selected organic-weighted neighbor + SVD hybrid scores 0.028549 public NDCG@10; proceeding to final refit and checks.\",plan:[\n {step:\"Inspect data, schemas, distributions, and evaluator mechanics\",\"status\":\"completed\"},\n {step:\"Create a valid baseline submission and validation ranking\",\"status\":\"completed\"},\n {step:\"Develop and evaluate personalized temporal ranking models\",\"status\":\"completed\"},\n {step:\"Refit selected model on train + validation and build final submission\",\"status\":\"in_progress\"},\n {step:\"Validate artifact and document reproducible build\",\"status\":\"pending\"}\n]});\nconst y = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_cf.py --final\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(y.output); if(y.session_id) text(`SESSION_ID=${y.session_id}`);\n" } ### Tool output · 2026-09-24T18:02:45.711Z Script running with cell ID 63 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:47.607Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:03:04.881Z Script completed Wall time 17.3 seconds Output: history final {'pos': (5669201, 670.4), 'organic': (2102641, 248.7), 'recent': (5669201, 670.4), 'like': (432659, 51.2)} seconds 8.7 neighbors (8456, 8456) 2536800 seconds 2.9 svd variance 0.2021 seconds 3.5 SESSION_ID=98999 ## Step 83 · agent · 2026-09-24T18:03:07.489Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:08.481Z { "code": "const r = await tools.write_stdin({session_id:98999,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:03:08.585Z Script completed Wall time 0.0 seconds Output: wrote /app/solution/submission.parquet 845600 ## Step 84 · agent · 2026-09-24T18:03:18.938Z ### Thinking unavailable — no text recorded The final refit is complete and `submission.parquet` now uses all train and validation interactions as history, with cutoff-correct test preference state and test eligibility. I’m performing an independent schema/eligibility audit and saving the selected public ranking alongside it, then I’ll leave a concise reproducibility note. ### exec · 2026-09-24T18:03:26.967Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+`submission.parquet` is the final test-cutoff ranking. It contains 100 ranked,\\n+distinct, test-eligible tracks for each of the 8,456 target users.\\n+\\n+## Model\\n+\\n+The eligibility service removes a user's prior interactions, so the ranker is\\n+designed for unseen-track discovery rather than repeat prediction. It builds\\n+sparse user/catalog histories from positive listens, organic listens, recency,\\n+and the cutoff-specific active-like state. An IDF-weighted taste profile finds\\n+200 nearest listeners. Their tracks are transferred with extra weight on\\n+organic behavior and active likes, moderate item-frequency normalization, and\\n+a 128-dimensional truncated-SVD taste score. A small global organic-popularity\\n+term stabilizes sparse users. Every score is filtered by the relevant packed\\n+eligibility mask before ranking.\\n+\\n+The selected train-only configuration scored **0.028549 NDCG@10** on the public\\n+validation window (popularity baseline: 0.010710). The delivered model was then\\n+refit on train plus validation history, using `test_preference_state.parquet`.\\n+\\n+## Rebuild\\n+\\n+From `/app` with the provided environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python solution/rank_cf.py --final\\n+```\\n+\\n+Run without `--final` to produce a train-only `validation.parquet`. The random\\n+seed is fixed. `build.py` retains the simple popularity fallback, while\\n+`ensemble.py` contains the rank-fusion utility used during model comparison.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T18:03:27.081Z Script completed Wall time 0.0 seconds Output: {} ## Step 85 · agent · 2026-09-24T18:03:28.982Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:36.176Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/last_pp0.3_pb0.1_sb0.3.parquet /app/solution/validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking, CandidateSet\\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\\nf=pd.read_parquet(S)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nn=validate_ranking(f,targets)\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=sum(not c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\\nprint('shape',n.shape,'dtypes',n.dtypes.astype(str).to_dict())\\nprint('users',n.uid.nunique(),'rows/user',n.groupby('uid').size().agg(['min','max']).to_dict())\\nprint('rank bounds',(n['rank'].min(),n['rank'].max()),'duplicate pairs',int(n.duplicated(['uid','item_id']).sum()),'ineligible',bad)\\nprint('file bytes',S.stat().st_size)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/README.md /app/solution/rank_cf.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-24T18:03:40.650Z Script completed Wall time 4.5 seconds Output: shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'} users 8456 rows/user {'min': 100, 'max': 100} rank bounds (np.int64(1), np.int64(100)) duplicate pairs 0 ineligible 0 file bytes 1801787 { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.028549, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 1.4K Sep 24 18:03 /app/solution/README.md -rw-r--r-- 1 agent agent 13K Sep 24 18:02 /app/solution/rank_cf.py -rw-r--r-- 1 agent agent 1.8M Sep 24 18:03 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 1.8M Sep 24 18:03 /app/solution/validation.parquet ## Step 86 · agent · 2026-09-24T18:03:45.684Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:48.194Z { "code": "const r = await tools.update_plan({explanation:\"Final model was refit on train+validation, submission was generated, independently audited against schema and test eligibility, and the selected validation score was reproduced.\",plan:[\n {step:\"Inspect data, schemas, distributions, and evaluator mechanics\",\"status\":\"completed\"},\n {step:\"Create a valid baseline submission and validation ranking\",\"status\":\"completed\"},\n {step:\"Develop and evaluate personalized temporal ranking models\",\"status\":\"completed\"},\n {step:\"Refit selected model on train + validation and build final submission\",\"status\":\"completed\"},\n {step:\"Validate artifact and document reproducible build\",\"status\":\"completed\"}\n]});\ntext(r);\n" } ### Tool output · 2026-09-24T18:03:48.316Z Script completed Wall time 0.0 seconds Output: {} ## Step 87 · agent · 2026-09-24T18:03:51.724Z ### Thinking unavailable — no text recorded Completed the music-discovery ranker. - Final artifact: [submission.parquet](/app/solution/submission.parquet) - Reproducible builder: [rank_cf.py](/app/solution/rank_cf.py) - Documentation: [README.md](/app/solution/README.md) - Selected validation ranking: [validation.parquet](/app/solution/validation.parquet) Public validation NDCG@10: **0.028549**, versus **0.010710** for the popularity baseline. Final audit passed: - 8,456 users - Exactly 100 unique tracks per user - Ranks 1–100 - No duplicate user-item pairs - Zero test-ineligible tracks - Integer schema as required