yambda-discovery-ranking-v01 / sol-high-0.147.0-exact-r03-2026-09-24 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T17:37:28.744Z 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 · 2026-09-24T17:37:30.404Z ## 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 · 2026-09-24T17:37:30.404Z 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 · 2026-09-24T17:37:30.404Z 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 · 2026-09-24T17:37:30.404Z 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": "01a0d47e-669f-7b12-8df6-5b6d65fdd1a5", "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 · 2026-09-24T17:37:30.414Z # 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 · 2026-09-24T17:37:32.361Z ### Thinking unavailable — no text recorded I’ll first profile the data, eligibility density, and public-window signal, then build a validation-safe ensemble, tune it against the provided evaluator, and refit the winning recipe on train+validation. I’ll keep a structurally valid fallback submission saved early, then overwrite it only after checks improve. ### exec · 2026-09-24T17:37:36.877Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data schemas, scale, time ranges, and candidate/eligibility structure\",\"status\":\"in_progress\"},\n {step:\"Build a valid baseline and validation feature pipeline\",\"status\":\"pending\"},\n {step:\"Tune ranking ensemble on the public temporal window\",\"status\":\"pending\"},\n {step:\"Refit on train+validation and generate final test ranking\",\"status\":\"pending\"},\n {step:\"Validate deliverables and document reproduction\",\"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\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:36.945Z 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/validation_preference_state.parquet /task/data/val_unlikes.parquet /task/data/target_users.parquet /task/data/val_undislikes.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 ## Step 8 · agent · 2026-09-24T17:37:38.930Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:43.977Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n print(os.path.basename(f), os.path.getsize(f)/1e6, 'MB')\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(' ',pf.metadata.num_rows, pf.schema_arrow)\\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\\n z=np.load(f)\\n print(os.path.basename(f), {k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog same head',z['item_ids'][:10])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:44.090Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-09-24T17:37:45.799Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:47.539Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; python --version; python3 --version; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | sed -n '1,40p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:47.606Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 Python 3.12.13 Python 3.12.13 total 36 dr-xr-xr-x 1 root root 4096 Sep 24 17:34 . drwxr-xr-x 1 root root 4096 Sep 24 17:37 .. dr-xr-xr-x 1 root root 4096 Sep 24 17:30 data -r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 24 17:30 requirements.txt ## Step 10 · agent · 2026-09-24T17:37:49.305Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:50.456Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list | sed -n '1,160p'; env | sort | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:51.215Z Script completed Wall time 0.8 seconds Output: Package Version ------- ------- pip 25.0.1 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=01a0d47e-668b-7612-a3fc-eff74959dc0c COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=63ea770e5a15 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 11 · agent · 2026-09-24T17:37:53.102Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:56.272Z { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name 'site-packages' -o -name 'dist-packages' \\\\) 2>/dev/null | sed -n '1,100p'; find / -type f -name 'pyarrow*.so' 2>/dev/null | sed -n '1,40p'\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:57.049Z Script completed Wall time 0.8 seconds Output: /usr/lib/python3/dist-packages /usr/local/lib/python3.11/dist-packages /usr/local/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages ## Step 12 · agent · 2026-09-24T17:37:58.445Z ### Thinking unavailable — no text recorded The default shell Python is intentionally bare; the task’s pinned environment is available separately, so I’m switching to that interpreter. No package installation or network access is needed. ### exec · 2026-09-24T17:38:04.312Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os, glob, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n print(os.path.basename(f), round(os.path.getsize(f)/1e6,2), 'MB')\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(' ',pf.metadata.num_rows, pf.schema_arrow.names)\\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\\n z=np.load(f)\\n print(os.path.basename(f), {k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog head',z['item_ids'][:10])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:05.509Z Script completed Wall time 1.2 seconds Output: candidate_catalog.parquet 0.35 MB 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet 0.08 MB 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] test_eligible_candidates.npz 7.74 MB test_preference_state.parquet 1.09 MB 484776 ['uid', 'item_id', 'state'] train_dislikes.parquet 0.86 MB 101075 ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet 5.37 MB 814831 ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet 319.18 MB 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_undislikes.parquet 0.16 MB 19960 ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet 1.86 MB 292665 ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet 0.04 MB 3563 ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet 0.3 MB 37795 ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet 9.95 MB 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_undislikes.parquet 0.01 MB 606 ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet 0.07 MB 9346 ['uid', 'timestamp', 'item_id', 'is_organic'] validation_eligible_candidates.npz 7.57 MB validation_preference_state.parquet 1.06 MB 469124 ['uid', 'item_id', 'state'] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 24657 [28038.5 29242. 29852. ] 30000 29067.306291390727 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 24552 [28001. 29220. 29835.] 30000 29041.3178807947 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] ## Step 13 · agent · 2026-09-24T17:38:08.985Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:10.219Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,320p' /task/ranking_evaluation.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:10.283Z Script completed Wall time 0.0 seconds Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """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), } ## Step 14 · agent · 2026-09-24T17:38:21.354Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:27.779Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build cutoff-valid catalog-popularity rankings for both stages.\\\"\\\"\\\"\\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+\\n+\\n+def rank_from_npz(name: str, output: str) -> None:\\n+ with np.load(DATA / name, 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 r, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[r], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[eligible][:100]\\n+ rows.append(pd.DataFrame({\\n+ \\\"uid\\\": uid,\\n+ \\\"item_id\\\": chosen,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }))\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ rank_from_npz(\\\"validation_eligible_candidates.npz\\\", \\\"validation_baseline.parquet\\\")\\n+ rank_from_npz(\\\"test_eligible_candidates.npz\\\", \\\"submission.parquet\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:38:27.819Z Script completed Wall time 0.0 seconds Output: {} ## Step 15 · agent · 2026-09-24T17:38:29.396Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:31.580Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/build_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_baseline.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:37.748Z Script completed Wall time 6.2 seconds Output: total 16 drwxr-xr-x 1 agent agent 4096 Sep 24 17:38 . drwxr-xr-x 1 agent agent 4096 Sep 24 17:34 .. -rw-r--r-- 1 agent agent 1163 Sep 24 17:38 build_baseline.py { "model": "/app/solution/validation_baseline.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 16 · agent · 2026-09-24T17:38:48.679Z ### Thinking unavailable — no text recorded The first valid fallback is saved. Plain catalog popularity scores `0.010710` NDCG@10 over 4,263 scored users, which leaves substantial room. I’m now measuring three likely sources of lift separately: repeat affinity, recent item momentum, and collaborative similarity. ### exec · 2026-09-24T17:39:10.145Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/analyze.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D=Path('/task/data')\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+catalog=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+tmap=pd.Index(targets)\\n+imap=pd.Index(catalog)\\n+\\n+def ranges(path, col='timestamp'):\\n+ pf=pq.ParquetFile(path)\\n+ vals=[]\\n+ for i in range(pf.num_row_groups):\\n+ s=pf.metadata.row_group(i).column(pf.schema_arrow.get_field_index(col)).statistics\\n+ vals += [s.min,s.max]\\n+ return min(vals),max(vals)\\n+\\n+for stem in ['train_listens','train_likes','val_listens','val_likes']:\\n+ print(stem, ranges(D/(stem+'.parquet')))\\n+\\n+# validation truth in compact integer form\\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)]\\n+vk=vk[vk.is_organic==1]\\n+lab=pd.concat([vl[['uid','item_id']].assign(grade=1),vk[['uid','item_id']].assign(grade=3)])\\n+lab=lab[lab.uid.isin(targets)&lab.item_id.isin(catalog)].groupby(['uid','item_id'],as_index=False).grade.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\\n+ok=[bool((int(p[ur[int(u)],ic[int(i)]//8])>>(ic[int(i)]%8))&1) for u,i in lab[['uid','item_id']].itertuples(index=False,name=None)]\\n+lab=lab[np.asarray(ok)]\\n+print('labels',len(lab),'users',lab.uid.nunique(),'grade',lab.grade.value_counts().to_dict(),'items',lab.item_id.nunique())\\n+print('label catalog ranks',np.quantile(imap.get_indexer(lab.item_id),[0,.1,.25,.5,.75,.9,.99,1]))\\n+\\n+# Target-user candidate positive history pairs, streamed.\\n+pair_parts=[]; event_parts=[]\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for bi,b in enumerate(pf.iter_batches(batch_size=1_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\\n+ x=b.to_pandas()\\n+ r=tmap.get_indexer(x.uid); c=imap.get_indexer(x.item_id)\\n+ m=(r>=0)&(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\\n+ if m.any():\\n+ xx=pd.DataFrame({'r':r[m].astype(np.int32),'c':c[m].astype(np.int32),'ts':x.timestamp.to_numpy()[m],\\n+ 'organic':x.is_organic.to_numpy()[m].astype(np.int8),'ratio':x.played_ratio_pct.to_numpy()[m].astype(np.float32)})\\n+ pair_parts.append(xx.groupby(['r','c'],as_index=False).agg(n=('ts','size'),last=('ts','max'),organic=('organic','sum'),ratio=('ratio','max')))\\n+ if bi%10==0: print('batch',bi,'parts rows',sum(map(len,pair_parts)))\\n+hist=pd.concat(pair_parts).groupby(['r','c'],as_index=False).agg(n=('n','sum'),last=('last','max'),organic=('organic','sum'),ratio=('ratio','max'))\\n+hist['uid']=targets[hist.r]; hist['item_id']=catalog[hist.c]\\n+hist.to_parquet('/app/solution/target_history.parquet',index=False)\\n+labkey=pd.MultiIndex.from_frame(lab[['uid','item_id']])\\n+hkey=pd.MultiIndex.from_frame(hist[['uid','item_id']])\\n+print('history pairs',len(hist),'events',hist.n.sum(),'label repeat',labkey.isin(hkey).mean())\\n+matched=lab[labkey.isin(hkey)].merge(hist,on=['uid','item_id'])\\n+print('repeat label stats',matched[['n','last','organic','ratio']].describe(percentiles=[.1,.5,.9,.99]))\\n+print('labels/user',lab.groupby('uid').size().describe())\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:39:10.193Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:39:12.732Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/analyze.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:39:20.610Z Script completed Wall time 7.9 seconds Output: train_listens (0, 24786795) train_likes (80, 24786790) val_listens (24788600, 25393395) val_likes (24788815, 25393390) labels 37769 users 4263 grade {1: 33352, 3: 4417} items 14124 label catalog ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ] batch 0 parts rows 131932 batch 10 parts rows 1390687 batch 20 parts rows 2665765 batch 30 parts rows 3928994 batch 40 parts rows 5209799 history pairs 5514505 events 19959853 label repeat 0.0 repeat label stats n last organic ratio count 0.0 0.0 0.0 0.0 mean NaN NaN NaN NaN std NaN NaN NaN NaN min NaN NaN NaN NaN 10% NaN NaN NaN NaN 50% NaN NaN NaN NaN 90% NaN NaN NaN NaN 99% NaN NaN NaN NaN max NaN NaN NaN NaN labels/user count 4263.000000 mean 8.859723 std 15.773444 min 1.000000 25% 2.000000 50% 4.000000 75% 10.000000 max 442.000000 dtype: float64 ## Step 17 · agent · 2026-09-24T17:39:31.435Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-09-24T17:39:40.501Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:48.778Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_global.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+h=pd.read_parquet(O/'target_history.parquet')\\n+g=h.groupby('c').agg(users=('r','size'),events=('n','sum'),organic=('organic','sum'),last=('last','max'))\\n+for days in [3,7,14,30,60,90]:\\n+ g[f'u{days}']=(h['last'] >= CUT-days*86400).groupby(h.c).sum()\\n+g=g.reindex(np.arange(len(cat)),fill_value=0)\\n+features={\\n+ 'target_users':g.users.to_numpy(),\\n+ 'target_events':g.events.to_numpy(),\\n+ 'target_organic':g.organic.to_numpy(),\\n+ 'recent3':g.u3.to_numpy(), 'recent7':g.u7.to_numpy(), 'recent14':g.u14.to_numpy(),\\n+ 'recent30':g.u30.to_numpy(), 'recent60':g.u60.to_numpy(), 'recent90':g.u90.to_numpy(),\\n+}\\n+# popularity rank prior as small tie-breaker\\n+for name,x in features.items():\\n+ order=np.lexsort((np.arange(len(x)), -x))\\n+ with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids']; items=z['item_ids']; p=z['packed_eligible']\\n+ out=[]\\n+ for r,u in enumerate(uids):\\n+ mask=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\\n+ pick=order[mask[order]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\\n+ pd.concat(out,ignore_index=True).to_parquet(O/f'validation_global_{name}.parquet',index=False)\\n+ print(name, x.max(), np.count_nonzero(x))\\n+g.to_parquet(O/'global_features.parquet')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:39:48.800Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:39:53.050Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/tune_global.py >/app/solution/global_build.log && for f in /app/solution/validation_global_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:40:04.065Z Script running with cell ID 13 Wall time 11.0 seconds Output: ## Step 19 · agent · 2026-09-24T17:40:05.533Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:40:05.961Z { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:40:23.226Z Script completed Wall time 17.3 seconds Output: { "model": "/app/solution/validation_global_recent14.parquet", "scores": { "quality_ndcg@10": 0.019299, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent3.parquet", "scores": { "quality_ndcg@10": 0.024131, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent30.parquet", "scores": { "quality_ndcg@10": 0.016953, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent60.parquet", "scores": { "quality_ndcg@10": 0.015001, "quality_users": 4263 } } SESSION_ID=54524 ## Step 20 · agent · 2026-09-24T17:40:26.163Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:26.874Z { "code": "const r = await tools.write_stdin({session_id:54524,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" } ### Tool output · 2026-09-24T17:40:34.204Z Script completed Wall time 7.3 seconds Output: { "model": "/app/solution/validation_global_recent7.parquet", "scores": { "quality_ndcg@10": 0.02466, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent90.parquet", "scores": { "quality_ndcg@10": 0.014074, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_events.parquet", "scores": { "quality_ndcg@10": 0.010769, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_organic.parquet", "scores": { "quality_ndcg@10": 0.010582, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_users.parquet", "scores": { "quality_ndcg@10": 0.011037, "quality_users": 4263 } } ## Step 21 · agent · 2026-09-24T17:40:45.851Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-09-24T17:40:53.189Z ### Thinking unavailable — no text recorded A clear temporal pattern emerged: popularity among target users in only the last seven training days reaches `0.024660`, more than doubling the catalog baseline; older/global popularity is much weaker. I’m using that seven-day momentum as the ensemble prior while fitting latent user–track affinity from 5.5 million cutoff-safe history pairs. ### exec · 2026-09-24T17:41:14.612Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_svd.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fit latent implicit-feedback models and emit validation blend variants.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse, time\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--mode',default='bm25'); ap.add_argument('--rank',type=int,default=96)\\n+ a=ap.parse_args(); t=time.time()\\n+ h=pd.read_parquet(O/'target_history.parquet')\\n+ n_users=int(h.r.max())+1; n_items=30000\\n+ rows=h.r.to_numpy(np.int32); cols=h.c.to_numpy(np.int32)\\n+ age=(CUT-h['last'].to_numpy())/86400\\n+ if a.mode=='binary':\\n+ vals=np.ones(len(h),np.float32)\\n+ elif a.mode=='count':\\n+ vals=(1+np.log1p(h.n.to_numpy())).astype(np.float32)\\n+ elif a.mode=='recent':\\n+ vals=((1+np.log1p(h.n.to_numpy()))*(0.35+0.65*np.exp(-age/120))).astype(np.float32)\\n+ elif a.mode=='organic':\\n+ vals=(1+np.log1p(h.n.to_numpy())+0.5*np.log1p(h.organic.to_numpy())).astype(np.float32)\\n+ else:\\n+ vals=np.ones(len(h),np.float32)\\n+ x=sparse.csr_matrix((vals,(rows,cols)),shape=(n_users,n_items),dtype=np.float32)\\n+ df=np.diff(x.tocsc().indptr).astype(np.float32)\\n+ if a.mode in ('bm25','tfidf'):\\n+ idf=np.log((n_users+1)/(df+1)).astype(np.float32)\\n+ x=x.multiply(idf).tocsr()\\n+ norm=np.sqrt(np.asarray(x.multiply(x).sum(1)).ravel()).astype(np.float32)\\n+ x=sparse.diags(1/np.maximum(norm,1e-6))@x\\n+ print('matrix',x.shape,x.nnz,'build sec',time.time()-t,flush=True)\\n+ svd=TruncatedSVD(n_components=a.rank,n_iter=5,random_state=2026)\\n+ user=svd.fit_transform(x).astype(np.float32); comp=svd.components_.astype(np.float32)\\n+ print('fit sec',time.time()-t,'explained',svd.explained_variance_ratio_.sum(),flush=True)\\n+ gf=pd.read_parquet(O/'global_features.parquet')\\n+ gp=np.log1p(gf.u7.to_numpy()).astype(np.float32)\\n+ gp=(gp-gp.mean())/gp.std()\\n+ alphas=[0,.03,.06,.1,.16,.25,.4,.65,1.0]\\n+ outs={z:[] for z in alphas}\\n+ with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\n+ bs=128\\n+ for st in range(0,n_users,bs):\\n+ en=min(st+bs,n_users)\\n+ s=user[st:en]@comp\\n+ # Scale latent scores per listener so alpha is comparable.\\n+ s=(s-s.mean(1,keepdims=True))/np.maximum(s.std(1,keepdims=True),1e-6)\\n+ eligible=np.unpackbits(packed[st:en],axis=1,count=n_items,bitorder='little').astype(bool)\\n+ for alpha in alphas:\\n+ q=s+alpha*gp[None,:]\\n+ q[~eligible]=-np.inf\\n+ ix=np.argpartition(q,-100,axis=1)[:,-100:]\\n+ vv=np.take_along_axis(q,ix,axis=1)\\n+ ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):\\n+ outs[alpha].append(pd.DataFrame({'uid':uids[st+j],'item_id':items[ix[j]],'rank':np.arange(1,101)}))\\n+ if st%1024==0: print('rank',st,flush=True)\\n+ for alpha,parts in outs.items():\\n+ pd.concat(parts,ignore_index=True).to_parquet(O/f'validation_svd_{a.mode}_r{a.rank}_a{alpha}.parquet',index=False)\\n+ np.savez_compressed(O/f'svd_{a.mode}_r{a.rank}.npz',user=user,components=comp)\\n+ print('total sec',time.time()-t,flush=True)\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:41:14.664Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:41:20.615Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode tfidf --rank 96\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:41:31.632Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 23 · agent · 2026-09-24T17:41:33.453Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:41:33.859Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:41:45.593Z Script completed Wall time 11.7 seconds Output: matrix (8456, 30000) 5514505 build sec 0.3065223693847656 fit sec 2.341540575027466 explained 0.18627316 rank 0 rank 1024 rank 2048 rank 3072 rank 4096 rank 5120 rank 6144 rank 7168 rank 8192 total sec 23.16431212425232 ## Step 24 · agent · 2026-09-24T17:41:48.826Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:50.752Z { "code": "const r = await tools.exec_command({cmd:\"find /app/solution -maxdepth 1 -name 'validation_svd_tfidf_r96_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:41:59.502Z Script completed Wall time 8.7 seconds Output: { "model": "/app/solution/validation_svd_tfidf_r96_a0.16.parquet", "scores": { "quality_ndcg@10": 0.020148, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.06.parquet", "scores": { "quality_ndcg@10": 0.019912, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.25.parquet", "scores": { "quality_ndcg@10": 0.020425, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.65.parquet", "scores": { "quality_ndcg@10": 0.022179, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.1.parquet", "scores": { "quality_ndcg@10": 0.019989, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.03.parquet", "scores": { "quality_ndcg@10": 0.019833, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.parquet", "scores": { "quality_ndcg@10": 0.01962, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a0.4.parquet", "scores": { "quality_ndcg@10": 0.021004, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_tfidf_r96_a1.0.parquet", "scores": { "quality_ndcg@10": 0.023927, "quality_users": 4263 } } ## Step 25 · agent · 2026-09-24T17:42:01.619Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:03.395Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 128\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:42:14.410Z Script running with cell ID 18 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:42:16.370Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:42:28.856Z Script completed Wall time 12.5 seconds Output: matrix (8456, 30000) 5514505 build sec 0.22640752792358398 fit sec 2.741776943206787 explained 0.34042275 rank 0 rank 1024 rank 2048 rank 3072 rank 4096 rank 5120 rank 6144 rank 7168 rank 8192 total sec 23.73560380935669 ### exec · 2026-09-24T17:42:33.923Z { "code": "const r = await tools.exec_command({cmd:\"find /app/solution -maxdepth 1 -name 'validation_svd_binary_r128_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:42:42.456Z Script completed Wall time 8.5 seconds Output: { "model": "/app/solution/validation_svd_binary_r128_a0.16.parquet", "scores": { "quality_ndcg@10": 0.020152, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a1.0.parquet", "scores": { "quality_ndcg@10": 0.022085, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.25.parquet", "scores": { "quality_ndcg@10": 0.02018, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.06.parquet", "scores": { "quality_ndcg@10": 0.019771, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.1.parquet", "scores": { "quality_ndcg@10": 0.019923, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.65.parquet", "scores": { "quality_ndcg@10": 0.021227, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.parquet", "scores": { "quality_ndcg@10": 0.019398, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.4.parquet", "scores": { "quality_ndcg@10": 0.020411, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_binary_r128_a0.03.parquet", "scores": { "quality_ndcg@10": 0.019549, "quality_users": 4263 } } ## Step 26 · agent · 2026-09-24T17:42:52.377Z ### Thinking unavailable — no text recorded The latent model is useful but the unpersonalized seven-day trend still wins; TF-IDF SVD alone scores `0.019620`, and its best tested blend reaches `0.023927`. That suggests near-term discovery demand is dominated by item momentum. I’m tightening the prior to the exact objective—organic ≥80% listens plus organic likes—across shorter lookback windows before deciding how much personalization to retain. ### exec · 2026-09-24T17:43:10.466Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_recent.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune cutoff-safe popularity features that mirror the discovery label.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); imap=pd.Index(items)\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); tset=set(targets.tolist())\\n+start=CUT-35*86400\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic'])\\n+li=li[li.item_id.isin(items)]; la=la[la.item_id.isin(items)]\\n+li['day']=(CUT-li.timestamp)//86400; la['day']=(CUT-la.timestamp)//86400\\n+print('recent rows',len(li),len(la),li.day.min(),li.day.max())\\n+features={}\\n+for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\\n+ for scope,tm in [('t',li.uid.isin(tset)),('a',np.ones(len(li),bool))]:\\n+ for typ,cm in [('o80',(li.is_organic==1)&(li.played_ratio_pct>=80)),\\n+ ('o50',(li.is_organic==1)&(li.played_ratio_pct>=50)),\\n+ ('x80',(li.played_ratio_pct>=80)),('x50',(li.played_ratio_pct>=50))]:\\n+ q=li[(li.day=80)][['uid','item_id']].assign(grade=1),\\n+ vk[vk.is_organic==1][['uid','item_id']].assign(grade=3)])\\n+truth=truth[truth.uid.isin(tset)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).grade.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as zz:\\n+ euids=zz['uids'].copy(); ep=zz['packed_eligible'].copy(); eitems=zz['item_ids'].copy()\\n+ur={int(u):r for r,u in enumerate(euids)}; ic={int(i):c for c,i in enumerate(eitems)}\\n+gold={}\\n+for (u,i),grade in truth.items():\\n+ r=ur[int(u)]; c=ic[int(i)]\\n+ if (int(ep[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(grade)\\n+\\n+best=(-1,None,None)\\n+def write_rank(name,score):\\n+ global best\\n order=np.lexsort((np.arange(len(score)), -score))\\n- with np.load(D/'validation_eligible_candidates.npz') as z:\\n- out=[]\\n- for r,u in enumerate(z['uids']):\\n- m=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\\n- ix=order[m[order]][:100]\\n- out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][ix],'rank':np.arange(1,101)}))\\n- pd.concat(out,ignore_index=True).to_parquet(O/f'validation_recent_{name}.parquet',index=False)\\n+ nd=[]\\n+ disc=1/np.log2(np.arange(2,12))\\n+ for u,g in gold.items():\\n+ r=ur[u]\\n+ chosen=[]\\n+ for c in order:\\n+ if (int(ep[r,c//8])>>(c%8))&1:\\n+ chosen.append(int(eitems[c]))\\n+ if len(chosen)==10: break\\n+ gains=np.array([2**g.get(i,0)-1 for i in chosen])\\n+ ideal=np.array(sorted((2**v-1 for v in g.values()),reverse=True)[:10])\\n+ nd.append((gains*disc).sum()/(ideal*disc[:len(ideal)]).sum())\\n+ val=float(np.mean(nd)); print(name,round(val,6),flush=True)\\n+ if val>best[0]: best=(val,name,order.copy())\\n@@\\n np.savez_compressed(O/'recent_features.npz',**features)\\n+print('BEST',best[0],best[1])\\n+order=best[2]; out=[]\\n+for r,u in enumerate(euids):\\n+ m=np.unpackbits(ep[r],count=len(items),bitorder='little').astype(bool)\\n+ ix=order[m[order]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_recent_best.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:43:32.724Z Script completed Wall time 0.0 seconds Output: {} ## Step 28 · agent · 2026-09-24T17:43:34.576Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:36.168Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:43:47.182Z Script running with cell ID 22 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:49.063Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:44:01.417Z Script completed Wall time 12.4 seconds Output: recent rows 5037944 94894 0 35 to80_1 0.032906 to50_1 0.033452 tx80_1 0.027216 ao80_1 0.032991 grade_1_w1 0.033949 grade_1_w3 0.034619 grade_1_w7 0.034226 to80_2 0.032848 to50_2 0.03283 tx80_2 0.025839 ao80_2 0.032849 grade_2_w1 0.032905 grade_2_w3 0.032931 grade_2_w7 0.033914 to80_3 0.031497 to50_3 0.031293 tx80_3 0.023418 ao80_3 0.031654 grade_3_w1 0.031443 grade_3_w3 0.033033 grade_3_w7 0.033901 to80_4 0.031912 to50_4 0.031605 tx80_4 0.0223 ao80_4 0.031387 grade_4_w1 0.032051 grade_4_w3 0.033273 grade_4_w7 0.033985 to80_5 0.030616 to50_5 0.030861 tx80_5 0.023153 ao80_5 0.030652 grade_5_w1 0.031414 grade_5_w3 0.032279 grade_5_w7 0.033217 to80_6 0.030494 to50_6 0.030812 tx80_6 0.022576 ao80_6 0.030407 grade_6_w1 0.031421 grade_6_w3 0.032039 grade_6_w7 0.033831 to80_7 0.030288 to50_7 0.031196 tx80_7 0.022852 ao80_7 0.030286 grade_7_w1 0.031402 grade_7_w3 0.031467 grade_7_w7 0.031983 to80_8 0.029116 to50_8 0.029966 tx80_8 0.02185 ao80_8 0.02904 grade_8_w1 0.030797 grade_8_w3 0.031843 grade_8_w7 0.03242 to80_10 0.028041 to50_10 0.028148 tx80_10 0.020168 ao80_10 0.028248 grade_10_w1 0.029015 grade_10_w3 0.031529 grade_10_w7 0.032188 to80_12 0.027358 to50_12 0.027829 tx80_12 0.019644 ao80_12 0.027402 grade_12_w1 0.028556 grade_12_w3 0.029499 grade_12_w7 0.031744 to80_14 0.025493 to50_14 0.025983 tx80_14 0.019168 ao80_14 0.025481 grade_14_w1 0.026943 grade_14_w3 0.027997 grade_14_w7 0.029309 to80_18 0.022502 to50_18 0.02305 tx80_18 0.019326 ao80_18 0.022345 grade_18_w1 0.023931 grade_18_w3 0.026165 grade_18_w7 0.028175 to80_21 0.020376 to50_21 0.02067 tx80_21 0.017744 ao80_21 0.020384 grade_21_w1 0.021564 grade_21_w3 0.023368 grade_21_w7 0.024848 to80_28 0.019033 to50_28 0.018943 tx80_28 0.017123 ao80_28 0.019032 grade_28_w1 0.019478 grade_28_w3 0.020093 grade_28_w7 0.020607 to80_35 0.01867 to50_35 0.018652 tx80_35 0.016402 ao80_35 0.018672 grade_35_w1 0.019267 grade_35_w3 0.019516 grade_35_w7 0.020098 BEST 0.0346188748576378 grade_1_w3 ## Step 29 · agent · 2026-09-24T17:44:04.623Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:05.651Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:44:08.239Z Script completed Wall time 2.6 seconds Output: { "model": "/app/solution/validation_recent_best.parquet", "scores": { "quality_ndcg@10": 0.034619, "quality_users": 4263 } } ## Step 30 · agent · 2026-09-24T17:44:20.089Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-24T17:44:23.619Z ### Thinking unavailable — no text recorded Matching the target definition changed the picture sharply: one-day target-cohort organic activity, with organic likes weighted 3×, scores `0.034619`—a 223% lift over the fallback and 40% over the earlier seven-day proxy. I’m now optimizing tie-breaking and decay around that signal; integer count ties are common, so secondary momentum can matter materially without disturbing the strongest primary ordering. ### exec · 2026-09-24T17:44:46.808Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_blends.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np, pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+with np.load(O/'recent_features.npz') as z: F={k:z[k] for k in z.files}\\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids'].copy(); packed=z['packed_eligible'].copy(); eitems=z['item_ids'].copy()\\n+ur={int(u):r for r,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(eitems)}\\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\n+truth=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\\n+truth=truth[truth.uid.isin(targets)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\\n+gold={}\\n+for (u,i),g in truth.items():\\n+ r=ur[int(u)]; c=ic[int(i)]\\n+ if (int(packed[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(g)\\n+disc=1/np.log2(np.arange(2,12))\\n+def evaluate(score):\\n+ order=np.lexsort((np.arange(len(score)),-score)); vals=[]\\n+ for u,g in gold.items():\\n+ r=ur[u]; chosen=[]\\n+ for c in order:\\n+ if (int(packed[r,c//8])>>(c%8))&1:\\n+ chosen.append(int(eitems[c]))\\n+ if len(chosen)==10: break\\n+ gain=np.array([2**g.get(i,0)-1 for i in chosen]); ideal=np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])\\n+ vals.append((gain*disc).sum()/(ideal*disc[:len(ideal)]).sum())\\n+ return np.mean(vals),order\\n+def scaled(x):\\n+ x=np.log1p(x.astype(float)); return (x-x.min())/(x.max()-x.min()+1e-9)\\n+best=(-1,'',None)\\n+secondaries={\\n+ 'cat':np.linspace(1,0,len(items)),\\n+ 'to50_1':scaled(F['to50_1']), 'ao80_1':scaled(F['ao80_1']),\\n+ 'g2':scaled(F['to80_2']+3*F['tlike_2']), 'g3':scaled(F['to80_3']+3*F['tlike_3']),\\n+ 'g7':scaled(F['to80_7']+3*F['tlike_7']), 'o7':scaled(F['to80_7']),\\n+}\\n+for w in [1.5,2,2.5,3,3.5,4,5,6,7]:\\n+ p=F['to80_1']+w*F['tlike_1']\\n+ for sn,s in secondaries.items():\\n+ for eps in [.01,.05,.15,.3,.49]:\\n+ val,order=evaluate(p+eps*s)\\n+ name=f'w{w}_{sn}_e{eps}'\\n+ print(name,round(val,6),flush=True)\\n+ if val>best[0]:best=(val,name,order.copy())\\n+# multi-level lexical: day-1 primary, day-2 then catalog.\\n+for w in [2,2.5,3,3.5,4,5]:\\n+ p=F['to80_1']+w*F['tlike_1']\\n+ for a in [.05,.15,.3,.49]:\\n+ score=p+a*scaled(F['to80_2']+w*F['tlike_2'])+.01*secondaries['cat']\\n+ val,order=evaluate(score); name=f'multi_w{w}_a{a}'; print(name,round(val,6),flush=True)\\n+ if val>best[0]:best=(val,name,order.copy())\\n+print('BEST',best[0],best[1])\\n+out=[]\\n+for r,u in enumerate(uids):\\n+ m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); ix=best[2][m[best[2]]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_blend_best.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:44:46.878Z Script completed Wall time 0.0 seconds Output: {} ## Step 32 · agent · 2026-09-24T17:44:49.076Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:51.213Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/tune_blends.py > /app/solution/blend_scores.txt && tail -n 25 /app/solution/blend_scores.txt && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:45:02.228Z Script running with cell ID 25 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:45:06.577Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:45:21.390Z Script completed Wall time 14.8 seconds Output: SESSION_ID=7346 ## Step 33 · agent · 2026-09-24T17:45:22.902Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:23.775Z { "code": "const r = await tools.write_stdin({session_id:7346,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);" } ### Tool output · 2026-09-24T17:45:26.222Z Script completed Wall time 2.4 seconds Output: multi_w2_a0.05 0.034175 multi_w2_a0.15 0.034114 multi_w2_a0.3 0.034222 multi_w2_a0.49 0.034222 multi_w2.5_a0.05 0.034609 multi_w2.5_a0.15 0.034588 multi_w2.5_a0.3 0.034588 multi_w2.5_a0.49 0.034588 multi_w3_a0.05 0.03456 multi_w3_a0.15 0.03457 multi_w3_a0.3 0.034605 multi_w3_a0.49 0.034589 multi_w3.5_a0.05 0.034731 multi_w3.5_a0.15 0.034735 multi_w3.5_a0.3 0.034735 multi_w3.5_a0.49 0.034735 multi_w4_a0.05 0.034497 multi_w4_a0.15 0.03449 multi_w4_a0.3 0.034472 multi_w4_a0.49 0.034476 multi_w5_a0.05 0.034474 multi_w5_a0.15 0.034479 multi_w5_a0.3 0.034457 multi_w5_a0.49 0.034457 BEST 0.03478709902101945 w3_g7_e0.01 { "model": "/app/solution/validation_blend_best.parquet", "scores": { "quality_ndcg@10": 0.034787, "quality_users": 4263 } } ## Step 34 · agent · 2026-09-24T17:45:37.681Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-09-24T17:45:47.196Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-09-24T17:45:53.120Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:53.661Z { "code": "const r = await tools.exec_command({cmd:\"sort -k2,2nr /app/solution/blend_scores.txt | sed -n '1,25p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:45:53.737Z Script completed Wall time 0.0 seconds Output: BEST 0.03478709902101945 w3_g7_e0.01 w3_g7_e0.01 0.034787 w3_g7_e0.05 0.034787 w3_g7_e0.15 0.034787 w3_g7_e0.3 0.034787 w3_g7_e0.49 0.034787 w3_to50_1_e0.01 0.034744 w3_to50_1_e0.05 0.034744 w3_to50_1_e0.15 0.034744 w3_to50_1_e0.3 0.034744 w3_to50_1_e0.49 0.034744 w3.5_g7_e0.01 0.034739 w3.5_g7_e0.05 0.034739 w3.5_g7_e0.15 0.034739 w3.5_g7_e0.3 0.034739 w3.5_g7_e0.49 0.034739 w3.5_g2_e0.01 0.034736 w3.5_g2_e0.05 0.034736 w3.5_g2_e0.15 0.034736 w3.5_g2_e0.3 0.034736 w3.5_g2_e0.49 0.034736 multi_w3.5_a0.15 0.034735 multi_w3.5_a0.3 0.034735 multi_w3.5_a0.49 0.034735 w3.5_g3_e0.01 0.034735 ## Step 37 · agent · 2026-09-24T17:45:59.267Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:29.615Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_collab.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Personalize trending candidates by similarity to their recent adopters.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np, pandas as pd\\n+from scipy import sparse\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64); imap=pd.Index(items)\\n+uids=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); umap=pd.Index(uids); uset=set(uids.tolist())\\n+# Objective-matched recent adopter matrix.\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\\n+li=li[(li.is_organic==1)&(li.played_ratio_pct>=80)&li.uid.isin(uset)&li.item_id.isin(items)][['uid','item_id']].assign(w=1.)\\n+la=la[(la.is_organic==1)&la.uid.isin(uset)&la.item_id.isin(items)][['uid','item_id']].assign(w=3.)\\n+a=pd.concat([li,la]).groupby(['uid','item_id'],as_index=False).w.max()\\n+rr=umap.get_indexer(a.uid); cc=imap.get_indexer(a.item_id)\\n+A=sparse.csr_matrix((a.w.to_numpy(np.float32),(rr,cc)),shape=(len(uids),len(items)))\\n+with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\\n+U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+P=(A.T@U).astype(np.float32); P/=np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\\n+with np.load(O/'recent_features.npz') as z:\\n+ base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+tie=np.log1p(tie); tie=(tie-tie.min())/(tie.max()-tie.min())\\n+# Truth and denominator.\\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\n+tr=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\\n+tr=tr[tr.uid.isin(uset)&tr.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ep=z['packed_eligible'].copy(); ei=z['item_ids'].copy()\\n+eur={int(u):r for r,u in enumerate(eu)}; eic={int(i):c for c,i in enumerate(ei)}; gold={}\\n+for (u,i),g in tr.items():\\n+ r=eur[int(u)]; c=eic[int(i)]\\n+ if (int(ep[r,c//8])>>(c%8))&1:gold.setdefault(int(u),{})[int(i)]=int(g)\\n+disc=1/np.log2(np.arange(2,12)); denom={u:(np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])*disc[:min(10,len(g))]).sum() for u,g in gold.items()}\\n+variants=[]\\n+for eps in [-.49,-.2,-.05,0,.02,.05,.1,.2,.35,.49,.75,1.0]: variants.append(('raw',eps))\\n+for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\\n+tot={v:[] for v in variants}; bs=128\\n+for st in range(0,len(uids),bs):\\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T\\n+ elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6)\\n+ bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\\n+ for v in variants:\\n+ typ,x=v\\n+ q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az)\\n+ q=np.where(elig,q,-np.inf)\\n+ ix=np.argpartition(q,-10,axis=1)[:,-10:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):\\n+ u=int(eu[st+j]); g=gold.get(u)\\n+ if g:tot[v].append(sum((2**g.get(int(ei[c]),0)-1)*disc[k] for k,c in enumerate(ix[j]))/denom[u])\\n+for v in variants:print(v,round(float(np.mean(tot[v])),6))\\n+best=max(variants,key=lambda v:np.mean(tot[v])); print('BEST',best,np.mean(tot[best]))\\n+# Generate the selected full ranking.\\n+out=[]\\n+for st in range(0,len(uids),bs):\\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T; elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6); bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\\n+ typ,x=best; q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az); q=np.where(elig,q,-np.inf)\\n+ ix=np.argpartition(q,-100,axis=1)[:,-100:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\\n+np.savez_compressed(O/'collab_model.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:46:29.694Z Script completed Wall time 0.0 seconds Output: {} ## Step 38 · agent · 2026-09-24T17:46:32.812Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:34.692Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:46:45.706Z Script running with cell ID 29 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:47.384Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:47:00.430Z Script completed Wall time 13.0 seconds Output: ('raw', -0.49) 0.034522 ('raw', -0.2) 0.034522 ('raw', -0.05) 0.034494 ('raw', 0) 0.034787 ('raw', 0.02) 0.03485 ('raw', 0.05) 0.034848 ('raw', 0.1) 0.034831 ('raw', 0.2) 0.034835 ('raw', 0.35) 0.034835 ('raw', 0.49) 0.034835 ('raw', 0.75) 0.034835 ('raw', 1.0) 0.034835 ('log', 0.02) 0.034966 ('log', 0.05) 0.035576 ('log', 0.1) 0.036818 ('log', 0.2) 0.039314 ('log', 0.3) 0.04129 ('log', 0.5) 0.043651 ('log', 1.0) 0.043156 BEST ('log', 0.5) 0.04365137646605114 { "model": "/app/solution/validation_collab_best.parquet", "scores": { "quality_ndcg@10": 0.04365, "quality_users": 4263 } } ## Step 39 · agent · 2026-09-24T17:47:09.259Z ### Thinking unavailable — no text recorded Collaborative momentum produced the largest gain: tracks are represented by the latent profiles of their most recent organic adopters, then matched to each listener. Blending that affinity with objective-matched momentum scores `0.043650` publicly, about 4× the catalog baseline. I’m narrowing the blend coefficient and then will refit the same cutoff-safe recipe using validation as observed history for the hidden window. ### exec · 2026-09-24T17:47:11.975Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:47:12.059Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:47:15.346Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py | tee /app/solution/collab_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:47:26.360Z Script running with cell ID 31 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:28.223Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:47:45.420Z Script completed Wall time 17.2 seconds Output: ('raw', -0.49) 0.034522 ('raw', -0.2) 0.034522 ('raw', -0.05) 0.034494 ('raw', 0) 0.034787 ('raw', 0.02) 0.03485 ('raw', 0.05) 0.034848 ('raw', 0.1) 0.034831 ('raw', 0.2) 0.034835 ('raw', 0.35) 0.034835 ('raw', 0.49) 0.034835 ('raw', 0.75) 0.034835 ('raw', 1.0) 0.034835 ('log', 0.02) 0.034966 ('log', 0.05) 0.035576 ('log', 0.1) 0.036818 ('log', 0.2) 0.039314 ('log', 0.3) 0.04129 ('log', 0.4) 0.042445 ('log', 0.45) 0.043012 ('log', 0.5) 0.043651 ('log', 0.55) 0.043845 ('log', 0.6) 0.044065 ('log', 0.65) 0.044083 ('log', 0.7) 0.044247 ('log', 0.8) 0.044413 ('log', 0.9) 0.044056 ('log', 1.0) 0.043156 BEST ('log', 0.8) 0.0444126936936653 ## Step 40 · agent · 2026-09-24T17:47:49.943Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:55.222Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-from pathlib import Path\\n-import numpy as np, pandas as pd\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np, pandas as pd\\n@@\\n D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\\n@@\\n-with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\\n-np.savez_compressed(O/'collab_model.npz',U=U,P=P)\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:47:55.316Z Script completed Wall time 0.0 seconds Output: {} ## Step 41 · agent · 2026-09-24T17:48:00.717Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:02.447Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:48:13.460Z Script running with cell ID 33 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:15.281Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:48:32.622Z Script completed Wall time 17.3 seconds Output: SESSION_ID=7200 ### exec · 2026-09-24T17:48:35.033Z { "code": "const r = await tools.write_stdin({session_id:7200,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" } ### Tool output · 2026-09-24T17:48:35.144Z Script completed Wall time 0.0 seconds Output: ('raw', -0.49) 0.034478 ('raw', -0.2) 0.034478 ('raw', -0.05) 0.034476 ('raw', 0) 0.034787 ('raw', 0.02) 0.034851 ('raw', 0.05) 0.034856 ('raw', 0.1) 0.034853 ('raw', 0.2) 0.034853 ('raw', 0.35) 0.034853 ('raw', 0.49) 0.034853 ('raw', 0.75) 0.034853 ('raw', 1.0) 0.034853 ('log', 0.02) 0.034932 ('log', 0.05) 0.035388 ('log', 0.1) 0.036414 ('log', 0.2) 0.038163 ('log', 0.3) 0.039865 ('log', 0.4) 0.041577 ('log', 0.45) 0.041996 ('log', 0.5) 0.042468 ('log', 0.55) 0.042735 ('log', 0.6) 0.043161 ('log', 0.65) 0.043585 ('log', 0.7) 0.044399 ('log', 0.8) 0.044531 ('log', 0.9) 0.044584 ('log', 1.0) 0.044654 BEST ('log', 1.0) 0.04465410532862228 ## Step 42 · agent · 2026-09-24T17:48:37.263Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:40.241Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0,1.1,1.2,1.35,1.5,1.75,2.0]:variants.append(('log',beta))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:48:40.331Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:48:44.078Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores2.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:48:55.092Z Script running with cell ID 36 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:57.748Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:49:14.253Z Script completed Wall time 16.5 seconds Output: SESSION_ID=68238 ### exec · 2026-09-24T17:49:16.304Z { "code": "const r = await tools.write_stdin({session_id:68238,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" } ### Tool output · 2026-09-24T17:49:20.903Z Script completed Wall time 4.6 seconds Output: ('raw', -0.49) 0.034478 ('raw', -0.2) 0.034478 ('raw', -0.05) 0.034476 ('raw', 0) 0.034787 ('raw', 0.02) 0.034851 ('raw', 0.05) 0.034856 ('raw', 0.1) 0.034853 ('raw', 0.2) 0.034853 ('raw', 0.35) 0.034853 ('raw', 0.49) 0.034853 ('raw', 0.75) 0.034853 ('raw', 1.0) 0.034853 ('log', 0.02) 0.034932 ('log', 0.05) 0.035388 ('log', 0.1) 0.036414 ('log', 0.2) 0.038163 ('log', 0.3) 0.039865 ('log', 0.4) 0.041577 ('log', 0.45) 0.041996 ('log', 0.5) 0.042468 ('log', 0.55) 0.042735 ('log', 0.6) 0.043161 ('log', 0.65) 0.043585 ('log', 0.7) 0.044399 ('log', 0.8) 0.044531 ('log', 0.9) 0.044584 ('log', 1.0) 0.044654 ('log', 1.1) 0.044023 ('log', 1.2) 0.043852 ('log', 1.35) 0.043083 ('log', 1.5) 0.041362 ('log', 1.75) 0.039758 ('log', 2.0) 0.037727 BEST ('log', 1.0) 0.04465410532862228 ## Step 43 · agent · 2026-09-24T17:49:22.494Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:24.015Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:49:26.603Z Script completed Wall time 2.6 seconds Output: { "model": "/app/solution/validation_collab_binary_r128.parquet", "scores": { "quality_ndcg@10": 0.044668, "quality_users": 4263 } } ## Step 44 · agent · 2026-09-24T17:49:37.126Z ### Thinking unavailable — no text recorded ## Step 45 · agent · 2026-09-24T17:49:43.416Z ### Thinking unavailable — no text recorded The best validated configuration is now binary latent profiles (128 dimensions) plus recent-adopter affinity at a 1:1 standardized blend, scoring `0.044668` publicly. I’m doing one final capacity check, then the production build will reconstruct profiles from train+validation, use only the last observed day for momentum/adopters, and apply test-cutoff eligibility. ### exec · 2026-09-24T17:49:46.628Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 256\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:49:57.642Z Script running with cell ID 39 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:59.560Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:50:14.928Z Script completed Wall time 15.4 seconds Output: matrix (8456, 30000) 5514505 build sec 0.22344207763671875 fit sec 4.9727489948272705 explained 0.40143767 rank 0 rank 1024 rank 2048 rank 3072 rank 4096 rank 5120 rank 6144 rank 7168 rank 8192 total sec 26.54409885406494 ## Step 46 · agent · 2026-09-24T17:50:19.469Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:22.787Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r256 | tee /app/solution/collab_binary256_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:50:33.804Z Script running with cell ID 40 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:50:35.683Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-09-24T17:50:52.962Z Script completed Wall time 17.3 seconds Output: SESSION_ID=36561 ### exec · 2026-09-24T17:50:54.929Z { "code": "const r = await tools.write_stdin({session_id:36561,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-09-24T17:50:59.801Z Script completed Wall time 4.9 seconds Output: ('raw', -0.49) 0.034477 ('raw', -0.2) 0.034477 ('raw', -0.05) 0.034476 ('raw', 0) 0.034787 ('raw', 0.02) 0.034907 ('raw', 0.05) 0.034855 ('raw', 0.1) 0.034853 ('raw', 0.2) 0.034852 ('raw', 0.35) 0.034852 ('raw', 0.49) 0.034852 ('raw', 0.75) 0.034852 ('raw', 1.0) 0.034852 ('log', 0.02) 0.034944 ('log', 0.05) 0.035385 ('log', 0.1) 0.036576 ('log', 0.2) 0.03836 ('log', 0.3) 0.04024 ('log', 0.4) 0.041658 ('log', 0.45) 0.042193 ('log', 0.5) 0.042493 ('log', 0.55) 0.042893 ('log', 0.6) 0.043472 ('log', 0.65) 0.043955 ('log', 0.7) 0.044345 ('log', 0.8) 0.04456 ('log', 0.9) 0.04432 ('log', 1.0) 0.044548 ('log', 1.1) 0.043749 ('log', 1.2) 0.043567 ('log', 1.35) 0.042365 ('log', 1.5) 0.041035 ('log', 1.75) 0.039094 ('log', 2.0) 0.037621 BEST ('log', 0.8) 0.044559889656155954 ## Step 47 · agent · 2026-09-24T17:51:07.377Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:39.976Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_final.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected discovery ranker on train+validation and rank test candidates.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+DATA = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+FINAL_CUTOFF = 25_393_400 # five seconds after the last validation event\\n+LATENT_DIM = 128\\n+BLEND = 1.0\\n+\\n+\\n+def main() -> None:\\n+ started = time.time()\\n+ items = pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+ users = pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64)\\n+ imap, umap = pd.Index(items), pd.Index(users)\\n+ user_set, item_set = set(users.tolist()), set(items.tolist())\\n+\\n+ # The streamed train aggregation is produced by analyze.py and retained as\\n+ # a reproducible intermediate. Add validation positive listens, then refit.\\n+ train = pd.read_parquet(OUT/'target_history.parquet', columns=['r','c'])\\n+ val = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','item_id','played_ratio_pct'])\\n+ val = val[(val.played_ratio_pct >= 50) & val.uid.isin(user_set) & val.item_id.isin(item_set)]\\n+ vr, vc = umap.get_indexer(val.uid), imap.get_indexer(val.item_id)\\n+ pairs = pd.concat([\\n+ train.rename(columns={'r':'r','c':'c'})[['r','c']],\\n+ pd.DataFrame({'r':vr.astype(np.int32), 'c':vc.astype(np.int32)}),\\n+ ]).drop_duplicates()\\n+ X = sparse.csr_matrix(\\n+ (np.ones(len(pairs),np.float32),(pairs.r.to_numpy(),pairs.c.to_numpy())),\\n+ shape=(len(users),len(items)), dtype=np.float32,\\n+ )\\n+ svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\\n+ U = svd.fit_transform(X).astype(np.float32)\\n+ U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+ print('latent fit',X.nnz,round(time.time()-started,2),'sec',flush=True)\\n+\\n+ # The selected momentum window is the final observed 24 hours. Organic\\n+ # strong listens and likes mirror the evaluation labels exactly.\\n+ li = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ la = pd.read_parquet(DATA/'val_likes.parquet', columns=['uid','timestamp','item_id','is_organic'])\\n+ common_li = li.uid.isin(user_set) & li.item_id.isin(item_set) & (li.is_organic == 1)\\n+ common_la = la.uid.isin(user_set) & la.item_id.isin(item_set) & (la.is_organic == 1)\\n+\\n+ def recent_pairs(hours: int) -> tuple[pd.DataFrame,pd.DataFrame]:\\n+ floor = FINAL_CUTOFF-hours*3600\\n+ l = li[common_li & (li.timestamp >= floor) & (li.played_ratio_pct >= 80)][['uid','item_id']].drop_duplicates()\\n+ k = la[common_la & (la.timestamp >= floor)][['uid','item_id']].drop_duplicates()\\n+ return l,k\\n+\\n+ l1,k1 = recent_pairs(24)\\n+ l7,k7 = recent_pairs(24*7)\\n+ base = (\\n+ l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ )\\n+ # Written separately to keep the like channel's predictive weight explicit.\\n+ base = l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ base += 3*k1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie = l7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie += 3*k7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie = np.log1p(tie); tie = (tie-tie.min())/max(float(tie.max()-tie.min()),1e-7)\\n+\\n+ adopters = pd.concat([l1.assign(w=1.),k1.assign(w=3.)]).groupby(['uid','item_id'],as_index=False).w.max()\\n+ ar, ac = umap.get_indexer(adopters.uid), imap.get_indexer(adopters.item_id)\\n+ A = sparse.csr_matrix((adopters.w.to_numpy(np.float32),(ar,ac)),shape=X.shape)\\n+ P = (A.T@U).astype(np.float32)\\n+ P /= np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\\n+ bz = np.log1p(base); bz = (bz-bz.mean())/max(float(bz.std()),1e-7)\\n+\\n+ with np.load(DATA/'test_eligible_candidates.npz',allow_pickle=False) as z:\\n+ eu, ei, packed = z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\\n+ assert np.array_equal(eu.astype(np.int64),users)\\n+ assert np.array_equal(ei.astype(np.int64),items)\\n+ out=[]; batch=128\\n+ for st in range(0,len(users),batch):\\n+ en=min(st+batch,len(users))\\n+ affinity=U[st:en]@P.T\\n+ az=(affinity-affinity.mean(1,keepdims=True))/np.maximum(affinity.std(1,keepdims=True),1e-7)\\n+ score=bz[None,:]+BLEND*az+1e-6*tie[None,:]\\n+ eligible=np.unpackbits(packed[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ score=np.where(eligible,score,-np.inf)\\n+ ix=np.argpartition(score,-100,axis=1)[:,-100:]\\n+ vv=np.take_along_axis(score,ix,axis=1)\\n+ ix=np.take_along_axis(ix,np.argsort(-vv,axis=1,kind='stable'),axis=1)\\n+ for j in range(en-st):\\n+ out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\\n+ submission=pd.concat(out,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\\n+ submission.to_parquet(OUT/'submission.parquet',index=False)\\n+ print('wrote',len(submission),'rows in',round(time.time()-started,2),'sec',flush=True)\\n+\\n+\\n+if __name__ == '__main__':\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:51:40.118Z Script completed Wall time 0.0 seconds Output: {} ## Step 48 · agent · 2026-09-24T17:51:42.248Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:44.900Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n- base = (\\n- l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n- + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n- )\\n- # Written separately to keep the like channel's predictive weight explicit.\\n+ # Written separately to keep the like channel's predictive weight explicit.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:51:45.016Z Script completed Wall time 0.0 seconds Output: {} ## Step 49 · agent · 2026-09-24T17:51:46.813Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:48.408Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:51:57.077Z Script completed Wall time 8.7 seconds Output: latent fit 5669201 3.75 sec wrote 845600 rows in 7.17 sec ## Step 50 · agent · 2026-09-24T17:52:08.206Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:13.986Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_recent.py\\n@@\\n for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\\n@@\\n base=features[f'to80_{days}']; lk=features[f'tlike_{days}']\\n for w in [1,3,7]: write_rank(f'grade_{days}_w{w}',base+w*lk)\\n+# Finer search around the winning one-day boundary.\\n+for hours in [6,9,12,15,18,21,24,27,30,36,42,48,60]:\\n+ ml=(li.timestamp >= CUT-hours*3600)&li.uid.isin(tset)&(li.is_organic==1)&(li.played_ratio_pct>=80)\\n+ mk=(la.timestamp >= CUT-hours*3600)&la.uid.isin(tset)&(la.is_organic==1)\\n+ bl=li[ml].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ lk=la[mk].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ for w in [2,2.5,3,3.5,4,5]: write_rank(f'hours_{hours}_w{w}',bl+w*lk)\\n np.savez_compressed(O/'recent_features.npz',**features)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:52:14.080Z Script completed Wall time 0.0 seconds Output: {} ## Step 51 · agent · 2026-09-24T17:52:15.885Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:18.269Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py > /app/solution/recent_scores_hours.txt && grep '^hours_' /app/solution/recent_scores_hours.txt | sort -k2,2nr | sed -n '1,20p' && tail -n 1 /app/solution/recent_scores_hours.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:52:29.284Z Script running with cell ID 46 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:31.934Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:52:48.449Z Script completed Wall time 16.5 seconds Output: SESSION_ID=15742 ### exec · 2026-09-24T17:52:50.431Z { "code": "const r = await tools.write_stdin({session_id:15742,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);" } ### Tool output · 2026-09-24T17:52:55.509Z Script completed Wall time 5.1 seconds Output: hours_21_w3 0.035042 hours_18_w5 0.035011 hours_18_w4 0.035007 hours_21_w3.5 0.034919 hours_15_w4 0.034879 hours_18_w3.5 0.03487 hours_21_w4 0.03485 hours_15_w5 0.034839 hours_15_w3 0.034747 hours_15_w3.5 0.034709 hours_21_w2.5 0.034636 hours_24_w3 0.034619 hours_24_w3.5 0.034602 hours_18_w3 0.034556 hours_21_w5 0.034552 hours_24_w4 0.034538 hours_18_w2.5 0.034511 hours_27_w5 0.034502 hours_24_w2.5 0.034482 hours_27_w4 0.034451 BEST 0.035042412582765914 hours_21_w3 ## Step 52 · agent · 2026-09-24T17:52:59.355Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:09.354Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\\n@@\\n-li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n-la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic'])\\n@@\\n with np.load(O/'recent_features.npz') as z:\\n- base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+ tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+base=li.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+base+=3*la.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\\n-np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:53:09.444Z Script completed Wall time 0.0 seconds Output: {} ## Step 53 · agent · 2026-09-24T17:53:11.551Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:13.440Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 | tee /app/solution/collab_binary128_h21_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:53:24.455Z Script running with cell ID 49 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:26.218Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-09-24T17:53:43.614Z Script completed Wall time 17.4 seconds Output: SESSION_ID=13544 ### exec · 2026-09-24T17:53:47.243Z { "code": "const r = await tools.write_stdin({session_id:13544,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-09-24T17:53:49.735Z Script completed Wall time 2.5 seconds Output: ('raw', -0.49) 0.034871 ('raw', -0.2) 0.034871 ('raw', -0.05) 0.034884 ('raw', 0) 0.035079 ('raw', 0.02) 0.03514 ('raw', 0.05) 0.035111 ('raw', 0.1) 0.03511 ('raw', 0.2) 0.03511 ('raw', 0.35) 0.03511 ('raw', 0.49) 0.03511 ('raw', 0.75) 0.03511 ('raw', 1.0) 0.03511 ('log', 0.02) 0.03514 ('log', 0.05) 0.035496 ('log', 0.1) 0.036223 ('log', 0.2) 0.038207 ('log', 0.3) 0.039512 ('log', 0.4) 0.040944 ('log', 0.45) 0.041581 ('log', 0.5) 0.04231 ('log', 0.55) 0.042683 ('log', 0.6) 0.04302 ('log', 0.65) 0.043466 ('log', 0.7) 0.044103 ('log', 0.8) 0.044612 ('log', 0.9) 0.044429 ('log', 1.0) 0.044704 ('log', 1.1) 0.044046 ('log', 1.2) 0.043482 ('log', 1.35) 0.042559 ('log', 1.5) 0.041448 ('log', 1.75) 0.039684 ('log', 2.0) 0.037831 BEST ('log', 1.0) 0.044704257604108524 ## Step 54 · agent · 2026-09-24T17:53:51.350Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:52.649Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:53:55.283Z Script completed Wall time 2.6 seconds Output: { "model": "/app/solution/validation_collab_binary_r128_h21.parquet", "scores": { "quality_ndcg@10": 0.044685, "quality_users": 4263 } } ## Step 55 · agent · 2026-09-24T17:54:00.086Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:01.892Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n- # The selected momentum window is the final observed 24 hours. Organic\\n+ # The selected momentum window is the final observed 21 hours. Organic\\n@@\\n- l1,k1 = recent_pairs(24)\\n+ l1,k1 = recent_pairs(21)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:54:02.007Z Script completed Wall time 0.0 seconds Output: {} ## Step 56 · agent · 2026-09-24T17:54:03.694Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:04.912Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:54:13.609Z Script completed Wall time 8.7 seconds Output: latent fit 5669201 3.77 sec wrote 845600 rows in 7.26 sec ## Step 57 · agent · 2026-09-24T17:54:20.287Z ### Thinking unavailable — no text recorded The finer search settled on a 21-hour momentum/adopter window, with a small public improvement to `0.044685`. The final submission has now been refit on 5,669,201 distinct train+validation user–track positives and rebuilt against test eligibility. I’m running contract-level validation and documenting the exact rebuild path. ### exec · 2026-09-24T17:54:29.533Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Music discovery ranker\\n+\\n+The final model is an implicit collaborative momentum ranker. It uses binary\\n+target-user/catalog interactions with at least 50% completion, refits a\\n+128-dimensional randomized SVD on `train + validation`, and represents each\\n+currently trending track by the normalized latent centroid of target users who\\n+organically discovered it in the last 21 observed hours. The final score is an\\n+equal standardized blend of:\\n+\\n+- 21-hour organic discovery momentum (organic >=80% listens plus 3x organic\\n+ like counts), and\\n+- listener similarity to the track's recent organic adopters.\\n+\\n+A seven-day momentum value is retained only as a deterministic tie-breaker.\\n+All ranking happens after applying the supplied per-user cutoff eligibility.\\n+\\n+Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\\n+plain catalog-popularity fallback scored 0.010710.\\n+\\n+## Rebuild\\n+\\n+Use the task's pinned Python environment:\\n+\\n+```sh\\n+cd /app\\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py\\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py\\n+```\\n+\\n+The first command streams the large training listen log and writes the compact\\n+`target_history.parquet` intermediate. The second adds validation history,\\n+refits the selected model, applies `test_eligible_candidates.npz`, and writes\\n+`submission.parquet`. Random seeds and cutoff constants are fixed in code.\\n+\\n+`submission.parquet` contains exactly the required columns `uid`, `item_id`,\\n+and `rank`, with 100 cutoff-eligible distinct tracks per target user.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:54:29.621Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:54:37.161Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\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')\\neligible=all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(n),'users':n.uid.nunique(),'columns':list(f.columns),'dtypes':{k:str(v) for k,v in f.dtypes.items()},'all_test_eligible':eligible,'bytes':S.stat().st_size})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build_final.py /app/solution/target_history.parquet\\ndu -sh /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:54:39.049Z Script completed Wall time 1.9 seconds Output: {'rows': 845600, 'users': 8456, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'all_test_eligible': True, 'bytes': 1640967} -rw-r--r-- 1 agent agent 1.6K Sep 24 17:54 /app/solution/README.md -rw-r--r-- 1 agent agent 5.1K Sep 24 17:54 /app/solution/build_final.py -rw-r--r-- 1 agent agent 1.6M Sep 24 17:54 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 54M Sep 24 17:39 /app/solution/target_history.parquet 232M /app/solution ## Step 58 · agent · 2026-09-24T17:54:51.929Z ### Thinking unavailable — no text recorded ## Step 59 · agent · 2026-09-24T17:54:58.527Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:01.718Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\nt=pd.read_parquet(D+'target_users.parquet').uid\\nc=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\\nfor stage in ['validation','test']:\\n p=pd.read_parquet(D+stage+'_preference_state.parquet')\\n q=p[p.uid.isin(t)&p.item_id.isin(c)]\\n print(stage,len(p),len(q),q.state.value_counts().to_dict(),q.groupby('uid').size().describe().to_dict())\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:55:02.671Z Script completed Wall time 0.9 seconds Output: validation 469124 469124 {'liked': 418435, 'disliked': 50689} {'count': 7038.0, 'mean': 66.65586814435919, 'std': 108.6863506794777, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 81.0, 'max': 1847.0} test 484776 484776 {'liked': 432659, 'disliked': 52117} {'count': 7092.0, 'mean': 68.35532994923858, 'std': 112.3020426159094, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 83.0, 'max': 1861.0} ## Step 60 · agent · 2026-09-24T17:55:06.064Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:11.248Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nfrom scipy import sparse\\nD='/task/data/'; O='/app/solution/'\\nuids=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); items=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy(); um=pd.Index(uids); im=pd.Index(items)\\nwith np.load(O+'svd_binary_r128.npz') as z: U=z['user']; C=z['components']\\np=pd.read_parquet(D+'validation_preference_state.parquet'); r=um.get_indexer(p.uid); c=im.get_indexer(p.item_id)\\nfor st in ['liked','disliked']:\\n m=p.state.eq(st).to_numpy(); A=sparse.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(uids),len(items)))\\n V=A@C.T\\n print(st,'base norm median',np.median(np.linalg.norm(U,axis=1)),'adj norm',np.quantile(np.linalg.norm(V,axis=1),[.25,.5,.75,.9]))\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:55:12.363Z Script completed Wall time 1.1 seconds Output: liked base norm median 11.444354 adj norm [0.11191944 0.61916333 1.82857478 3.62718737] disliked base norm median 11.444354 adj norm [0. 0.03020553 0.2039081 0.43307543] ## Step 61 · agent · 2026-09-24T17:55:14.775Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:24.165Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); ap.add_argument('--like-profile',type=float,default=0); ap.add_argument('--dislike-profile',type=float,default=0); args=ap.parse_args()\\n@@\\n-with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32); C=z['components'].astype(np.float32)\\n+if args.like_profile or args.dislike_profile:\\n+ pref=pd.read_parquet(D/'validation_preference_state.parquet')\\n+ pr=umap.get_indexer(pref.uid); pc=imap.get_indexer(pref.item_id)\\n+ for state,weight in [('liked',args.like_profile),('disliked',-args.dislike_profile)]:\\n+ m=pref.state.eq(state).to_numpy()\\n+ M=sparse.csr_matrix((np.ones(m.sum(),np.float32),(pr[m],pc[m])),shape=(len(uids),len(items)))\\n+ U += weight*(M@C.T)\\n U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\\n-np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\\n+tag=f'{args.latent}_h{args.hours}_lp{args.like_profile}_dp{args.dislike_profile}'\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{tag}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{tag}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:55:24.279Z Script completed Wall time 0.0 seconds Output: {} ## Step 62 · agent · 2026-09-24T17:55:25.993Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:27.991Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 3 --dislike-profile 10 | tee /app/solution/collab_pref_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:55:39.006Z Script running with cell ID 59 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:55:40.846Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-09-24T17:55:58.165Z Script completed Wall time 17.3 seconds Output: SESSION_ID=89873 ### exec · 2026-09-24T17:56:00.201Z { "code": "const r = await tools.write_stdin({session_id:89873,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-09-24T17:56:04.804Z Script completed Wall time 4.6 seconds Output: ('raw', -0.49) 0.034843 ('raw', -0.2) 0.034843 ('raw', -0.05) 0.034845 ('raw', 0) 0.035079 ('raw', 0.02) 0.035133 ('raw', 0.05) 0.035129 ('raw', 0.1) 0.035129 ('raw', 0.2) 0.035136 ('raw', 0.35) 0.035137 ('raw', 0.49) 0.035137 ('raw', 0.75) 0.035137 ('raw', 1.0) 0.035137 ('log', 0.02) 0.035184 ('log', 0.05) 0.035463 ('log', 0.1) 0.036307 ('log', 0.2) 0.038141 ('log', 0.3) 0.039579 ('log', 0.4) 0.041144 ('log', 0.45) 0.041627 ('log', 0.5) 0.042281 ('log', 0.55) 0.042781 ('log', 0.6) 0.043116 ('log', 0.65) 0.043422 ('log', 0.7) 0.043989 ('log', 0.8) 0.044089 ('log', 0.9) 0.044274 ('log', 1.0) 0.044146 ('log', 1.1) 0.043748 ('log', 1.2) 0.043323 ('log', 1.35) 0.042081 ('log', 1.5) 0.041016 ('log', 1.75) 0.038737 ('log', 2.0) 0.036627 BEST ('log', 0.9) 0.04427390575522779 ## Step 63 · agent · 2026-09-24T17:56:06.616Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:08.641Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 0 | tee /app/solution/collab_like1_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:56:19.655Z Script running with cell ID 61 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:21.502Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:56:38.816Z Script completed Wall time 17.3 seconds Output: SESSION_ID=55452 ### exec · 2026-09-24T17:56:41.038Z { "code": "const r = await tools.write_stdin({session_id:55452,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" } ### Tool output · 2026-09-24T17:56:44.544Z Script completed Wall time 3.5 seconds Output: ('raw', -0.49) 0.034867 ('raw', -0.2) 0.034867 ('raw', -0.05) 0.034881 ('raw', 0) 0.035079 ('raw', 0.02) 0.035164 ('raw', 0.05) 0.035135 ('raw', 0.1) 0.035115 ('raw', 0.2) 0.035115 ('raw', 0.35) 0.035115 ('raw', 0.49) 0.035115 ('raw', 0.75) 0.035115 ('raw', 1.0) 0.035099 ('log', 0.02) 0.035129 ('log', 0.05) 0.035489 ('log', 0.1) 0.036285 ('log', 0.2) 0.038261 ('log', 0.3) 0.039663 ('log', 0.4) 0.040966 ('log', 0.45) 0.041715 ('log', 0.5) 0.042273 ('log', 0.55) 0.042729 ('log', 0.6) 0.043287 ('log', 0.65) 0.043553 ('log', 0.7) 0.044097 ('log', 0.8) 0.044629 ('log', 0.9) 0.044919 ('log', 1.0) 0.044986 ('log', 1.1) 0.044737 ('log', 1.2) 0.044121 ('log', 1.35) 0.042842 ('log', 1.5) 0.042229 ('log', 1.75) 0.039953 ('log', 2.0) 0.037889 BEST ('log', 1.0) 0.04498647700478426 ## Step 64 · agent · 2026-09-24T17:56:46.511Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:47.867Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:56:50.481Z Script completed Wall time 2.6 seconds Output: { "model": "/app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet", "scores": { "quality_ndcg@10": 0.04496, "quality_users": 4263 } } ## Step 65 · agent · 2026-09-24T17:56:53.270Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:59.532Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\\n U = svd.fit_transform(X).astype(np.float32)\\n+ # Active likes sharpen the listener profile in the fitted item basis. A\\n+ # unit positive adjustment was selected on the temporal validation cut.\\n+ pref = pd.read_parquet(DATA/'test_preference_state.parquet')\\n+ pref = pref[pref.state.eq('liked')]\\n+ pr, pc = umap.get_indexer(pref.uid), imap.get_indexer(pref.item_id)\\n+ L = sparse.csr_matrix((np.ones(len(pref),np.float32),(pr,pc)),shape=X.shape)\\n+ U += L @ svd.components_.T\\n U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n*** Update File: /app/solution/README.md\\n@@\\n-128-dimensional randomized SVD on `train + validation`, and represents each\\n+128-dimensional randomized SVD on `train + validation`, sharpens each listener\\n+profile with the cutoff-specific active-like state, and represents each\\n@@\\n-Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\\n+Public temporal validation NDCG@10: **0.044960** over 4,263 scored users. The\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:56:59.640Z Script completed Wall time 0.0 seconds Output: {} ## Step 66 · agent · 2026-09-24T17:57:02.460Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:06.754Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py && PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import validate_ranking, CandidateSet\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint('VALID',len(f),f.uid.nunique(),p.stat().st_size)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:57:17.438Z Script completed Wall time 10.7 seconds Output: latent fit 5669201 3.82 sec wrote 845600 rows in 7.34 sec VALID 845600 8456 1640803 ## Step 67 · agent · 2026-09-24T17:57:20.388Z ### Thinking unavailable — no text recorded Active likes provided a modest but repeatable gain; the selected validation score is now `0.044960`. I’ve incorporated the cutoff-correct test preference state into the refit and revalidated all 845,600 rows. I’m checking whether a conservative negative-state adjustment helps before freezing the artifact; the current valid submission remains saved throughout. ### exec · 2026-09-24T17:57:23.796Z { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 3 | tail -n 23\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" } ### Tool output · 2026-09-24T17:57:34.824Z Script running with cell ID 66 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:57:36.649Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:57:53.983Z Script completed Wall time 17.3 seconds Output: SESSION_ID=50595 ### exec · 2026-09-24T17:57:58.069Z { "code": "const r = await tools.write_stdin({session_id:50595,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" } ### Tool output · 2026-09-24T17:58:00.282Z Script completed Wall time 2.2 seconds Output: ('raw', 1.0) 0.035115 ('log', 0.02) 0.035146 ('log', 0.05) 0.035544 ('log', 0.1) 0.036319 ('log', 0.2) 0.038336 ('log', 0.3) 0.039712 ('log', 0.4) 0.04107 ('log', 0.45) 0.042032 ('log', 0.5) 0.042556 ('log', 0.55) 0.043119 ('log', 0.6) 0.043483 ('log', 0.65) 0.043615 ('log', 0.7) 0.044135 ('log', 0.8) 0.044454 ('log', 0.9) 0.044667 ('log', 1.0) 0.044876 ('log', 1.1) 0.044271 ('log', 1.2) 0.043594 ('log', 1.35) 0.042784 ('log', 1.5) 0.041997 ('log', 1.75) 0.03955 ('log', 2.0) 0.037688 BEST ('log', 1.0) 0.044875728761976125 ## Step 68 · agent · 2026-09-24T17:58:06.038Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:10.000Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/analyze.py /app/solution/build_final.py /app/solution/build_baseline.py && sha256sum /app/solution/submission.parquet && sed -n '1,220p' /app/solution/README.md\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);\ntext(await tools.update_plan({explanation:\"Selected and refit the strongest public-validated collaborative momentum model; final artifact passes schema and cutoff eligibility checks.\",\"plan\":[{\"step\":\"Inspect data schemas, scale, time ranges, and candidate/eligibility structure\",\"status\":\"completed\"},{\"step\":\"Build a valid baseline and validation feature pipeline\",\"status\":\"completed\"},{\"step\":\"Tune ranking ensemble on the public temporal window\",\"status\":\"completed\"},{\"step\":\"Refit on train+validation and generate final test ranking\",\"status\":\"completed\"},{\"step\":\"Validate deliverables and document reproduction\",\"status\":\"completed\"}]}));" } ### Tool output · 2026-09-24T17:58:10.178Z Script completed Wall time 0.2 seconds Output: 1e7d088b56b8c273776eb9177a65bb9aaab1a96c639d2340a510c94d5174fc57 /app/solution/submission.parquet # Music discovery ranker The final model is an implicit collaborative momentum ranker. It uses binary target-user/catalog interactions with at least 50% completion, refits a 128-dimensional randomized SVD on `train + validation`, sharpens each listener profile with the cutoff-specific active-like state, and represents each currently trending track by the normalized latent centroid of target users who organically discovered it in the last 21 observed hours. The final score is an equal standardized blend of: - 21-hour organic discovery momentum (organic >=80% listens plus 3x organic like counts), and - listener similarity to the track's recent organic adopters. A seven-day momentum value is retained only as a deterministic tie-breaker. All ranking happens after applying the supplied per-user cutoff eligibility. Public temporal validation NDCG@10: **0.044960** over 4,263 scored users. The plain catalog-popularity fallback scored 0.010710. ## Rebuild Use the task's pinned Python environment: ```sh cd /app OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py ``` The first command streams the large training listen log and writes the compact `target_history.parquet` intermediate. The second adds validation history, refits the selected model, applies `test_eligible_candidates.npz`, and writes `submission.parquet`. Random seeds and cutoff constants are fixed in code. `submission.parquet` contains exactly the required columns `uid`, `item_id`, and `rank`, with 100 cutoff-eligible distinct tracks per target user. {} ## Step 69 · agent · 2026-09-24T17:58:12.383Z ### Thinking unavailable — no text recorded Completed the music-discovery ranker. - Final artifact: [submission.parquet](/app/solution/submission.parquet) - Rebuild code: [build_final.py](/app/solution/build_final.py) - Documentation: [README.md](/app/solution/README.md) - Public validation NDCG@10: **0.044960** - Verified: 845,600 rows, 8,456 users, ranks 1–100, unique items, and all candidates test-eligible.