yambda-discovery-ranking-v01 / sol-high-0.147.0-exact-r05-2026-09-24 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T17:37:31.442Z 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 · 2026-09-24T17:37:32.970Z ## 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 · 2026-09-24T17:37:32.970Z 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 · 2026-09-24T17:37:32.970Z 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 · 2026-09-24T17:37:32.970Z 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-712b-7473-9f2b-cd21e4f4a1b1", "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 · 2026-09-24T17:37:32.979Z # 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 · 2026-09-24T17:37:35.964Z ### Thinking unavailable — no text recorded I’ll inspect the data shapes and installed libraries, establish a valid baseline immediately, then iterate against the public temporal validation score. I’ll keep the best valid artifact saved under `/app/solution/` throughout and only use validation outcomes for tuning—not as validation features. ### exec · 2026-09-24T17:37:40.247Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect datasets, eligibility layout, and available libraries\",status:\"in_progress\"},\n {step:\"Build a cutoff-correct baseline and save valid submission\",status:\"pending\"},\n {step:\"Engineer and tune ranking models on public validation\",status:\"pending\"},\n {step:\"Refit selected model on train plus validation history\",status:\"pending\"},\n {step:\"Generate and validate final submission and documentation\",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 · 2026-09-24T17:37:40.314Z 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/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/target_users.parquet /task/data/val_unlikes.parquet /task/data/val_undislikes.parquet /task/data/val_dislikes.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 · 2026-09-24T17:37:42.845Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:47.820Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, numpy as np, os\\nD='/task/data'\\nfor f in sorted(os.listdir(D)):\\n p=f'{D}/{f}'\\n if f.endswith('.parquet'):\\n x=pd.read_parquet(p)\\n print(f, x.shape, x.dtypes.to_dict())\\n print(x.head(3).to_dict('records'))\\n elif f.endswith('.npz'):\\n z=np.load(p)\\n print(f,{k:z[k].shape for k in z.files},{k:str(z[k].dtype) for k in z.files})\\n print('elig counts', np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)[:10], 'range',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min(),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).max())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:47.877Z 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 · 2026-09-24T17:37:50.265Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:51.877Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; find /opt /usr/local -maxdepth 4 -type f -name python 2>/dev/null | sed -n '1,40p'; env | sort | rg 'VIRTUAL|PATH|PYTHON'\",workdir:\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:51.928Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 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 PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 ## Step 10 · agent · 2026-09-24T17:37:53.192Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:55.109Z { "code": "const r = await tools.exec_command({cmd:\"find / -path /proc -prune -o -path /sys -prune -o -type f \\\\( -name 'activate' -o -name 'pyarrow*.so' -o -name 'python3.12' \\\\) -print 2>/dev/null | sed -n '1,100p'\",workdir:\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:37:55.455Z Script completed Wall time 0.3 seconds Output: /usr/lib/python3.11/venv/scripts/common/activate /usr/local/lib/python3.12/venv/scripts/common/activate /usr/local/bin/python3.12 /opt/task-venv/bin/activate ## Step 11 · agent · 2026-09-24T17:37:56.700Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:01.086Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, os\\nD='/task/data'\\nfor f in sorted(os.listdir(D)):\\n p=f'{D}/{f}'\\n if f.endswith('.parquet'):\\n x=pd.read_parquet(p)\\n print('\\\\n',f, x.shape, x.dtypes.to_dict())\\n print(x.head(3).to_dict('records'))\\n elif f.endswith('.npz'):\\n z=np.load(p)\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('\\\\n',f,{k:z[k].shape for k in z.files},{k:str(z[k].dtype) for k in z.files})\\n print('elig range/mean',counts.min(),counts.max(),counts.mean(),'first', counts[:10])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:03.438Z Script completed Wall time 2.3 seconds Output: candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}] target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}] test_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} elig range/mean 24552 30000 29041.3178807947 first [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665] test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}] train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}] train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}] train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}] train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}] train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}] val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}] val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}] val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} [{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}] val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}] val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}] validation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} elig range/mean 24657 30000 29067.306291390727 first [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736] validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}] ## Step 12 · agent · 2026-09-24T17:38:11.038Z ### Thinking unavailable — no text recorded The data is substantial (43.5M training listens, 8,456 users, 30k candidates), and each user retains roughly 29k eligible tracks. I’m now measuring what drives the public positives—repeat affinity, recency, organic history, and collaborative signal—so the first model is grounded in the actual temporal behavior rather than generic popularity. ### exec · 2026-09-24T17:38:17.865Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,280p' /task/ranking_evaluation.py && /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data'\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet')\\ntu=pd.read_parquet(D+'/target_users.parquet')\\nu=set(tu.uid.astype(int)); items=set(cat.item_id.astype(int))\\nfor family in ['train_listens','train_likes','val_listens','val_likes']:\\n x=pd.read_parquet(D+'/'+family+'.parquet')\\n print(family,'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(u).sum(),'catalog rows',x.item_id.isin(items).sum())\\n if 'played_ratio_pct' in x:\\n y=x[(x.uid.isin(u))&(x.item_id.isin(items))&(x.is_organic.eq(1))&(x.played_ratio_pct.ge(80))]\\n else:\\n y=x[(x.uid.isin(u))&(x.item_id.isin(items))&(x.is_organic.eq(1))]\\n print(' objective-ish',len(y),'pairs',y[['uid','item_id']].drop_duplicates().shape[0],'users',y.uid.nunique())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:28.880Z Script running with cell ID 6 Wall time 11.0 seconds Output: ## Step 13 · agent · 2026-09-24T17:38:30.668Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:38:30.805Z { "cell_id": "6", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:38:33.177Z Script completed Wall time 2.4 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), } train_listens users 9238 items 845395 target rows 43222297 catalog rows 31249613 objective-ish 8661020 pairs 1935328 users 8014 train_likes users 8144 items 172303 target rows 690968 catalog rows 554824 objective-ish 223046 pairs 215264 users 6532 val_listens users 8196 items 191747 target rows 1482553 catalog rows 1011771 objective-ish 288828 pairs 170791 users 6027 val_likes users 4114 items 22902 target rows 33177 catalog rows 21055 objective-ish 10406 pairs 8528 users 2073 ## Step 14 · agent · 2026-09-24T17:38:41.231Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:42.517Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq\\nD='/task/data'\\nfor f in ['train_listens','train_likes','val_listens','val_likes']:\\n x=pd.read_parquet(D+'/'+f+'.parquet',columns=['timestamp'])\\n print(f,int(x.timestamp.min()),int(x.timestamp.max()),int(x.timestamp.max()-x.timestamp.min()))\\nPY\",workdir:\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:38:43.692Z Script completed Wall time 1.2 seconds Output: train_listens 0 24786795 24786795 train_likes 80 24786790 24786710 val_listens 24788600 25393395 604795 val_likes 24788815 25393390 604575 ## Step 15 · agent · 2026-09-24T17:38:51.369Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:06.215Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data'; cut=24786800\\nuids=pd.read_parquet(D+'/target_users.parquet',columns=['uid'])\\nitems=pd.read_parquet(D+'/candidate_catalog.parquet',columns=['item_id'])\\nuset=set(uids.uid); iset=set(items.item_id)\\nprint('loading listens'); t=time.time(); x=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('load',time.time()-t)\\nx=x[x.uid.isin(uset)&x.item_id.isin(iset)&(x.played_ratio_pct>=50)].copy(); print('filtered',len(x),time.time()-t)\\nx['org']=x.is_organic.astype('uint8'); x['strongorg']=((x.is_organic==1)&(x.played_ratio_pct>=80)).astype('uint8')\\ng=x.groupby(['uid','item_id'],sort=False).agg(cnt=('timestamp','size'),last=('timestamp','max'),org=('org','sum'),sorg=('strongorg','sum'))\\ngo=x[x.org==1].groupby(['uid','item_id'],sort=False).timestamp.max().rename('lastorg')\\ng=g.join(go).reset_index(); del x\\nprint('pairs',len(g),'group secs',time.time()-t)\\nlikes=pd.read_parquet(D+'/validation_preference_state.parquet'); liked=likes[likes.state=='liked'][['uid','item_id']].drop_duplicates(); disliked=likes[likes.state=='disliked'][['uid','item_id']].drop_duplicates()\\ng=g.merge(liked.assign(liked=1),how='left').merge(disliked.assign(disliked=1),how='left'); g[['liked','disliked']]=g[['liked','disliked']].fillna(0)\\nvl=pd.read_parquet(D+'/val_listens.parquet'); vl=vl[(vl.uid.isin(uset))&(vl.item_id.isin(iset))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(label=1)\\nvk=pd.read_parquet(D+'/val_likes.parquet'); vk=vk[(vk.uid.isin(uset))&(vk.item_id.isin(iset))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(like_label=1)\\ny=vl.merge(vk,how='outer').fillna(0); y['grade']=np.where(y.like_label==1,3,1)\\ny=y[['uid','item_id','grade']]\\nyh=y.merge(g,how='left',indicator=True)\\nprint('labels',len(yh),'users',yh.uid.nunique(),'history coverage', (yh._merge=='both').mean(),'like cov', (yh[yh.grade==3]._merge=='both').mean())\\nprint(yh.groupby('grade').agg(n=('uid','size'),hist=('cnt','count'),medcnt=('cnt','median'),medrec=('last','median'),liked=('liked','mean'),org=('org','mean'),sorg=('sorg','mean')))\\nprint('all hist baserate matched labels',len(g.merge(y,on=['uid','item_id']))/len(g))\\nfor days in [7,14,30,60,90,180,365]:\\n sub=g[g['last']>=cut-days*86400]\\n hit=len(sub.merge(y,on=['uid','item_id']))\\n print(days,'pairs',len(sub),'label_hits',hit,'precision',hit/max(len(sub),1),'coverage',hit/len(y))\\nPY\",workdir:\"/app\",yield_time_ms:1000,max_output_tokens:10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:39:07.437Z Script completed Wall time 1.2 seconds Output: SESSION_ID=20864 ## Step 16 · agent · 2026-09-24T17:39:08.775Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:09.480Z { "code": "const r = await tools.write_stdin({session_id:20864,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:39:20.494Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 17 · agent · 2026-09-24T17:39:22.643Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:39:23.005Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:39:26.432Z Script completed Wall time 3.4 seconds Output: loading listens load 0.49206972122192383 filtered 19959853 6.898972034454346 pairs 5514505 group secs 11.55227541923523 labels 175729 users 6186 history coverage 0.7450961423555589 like cov 0.42295966228893056 n hist medcnt medrec liked org sorg grade 1 167201 127328 7.0 23961570.0 0.421384 12.651553 11.892726 3 8528 3607 2.0 23091160.0 0.181591 1.901580 1.739118 all hist baserate matched labels 0.02374374490548109 7 pairs 485757 label_hits 55831 precision 0.11493606885747401 coverage 0.3177107933238111 14 pairs 848775 label_hits 76036 precision 0.089583222880033 coverage 0.432688969948045 30 pairs 1480074 label_hits 97685 precision 0.06600007837445966 coverage 0.5558843446443102 60 pairs 2356610 label_hits 113119 precision 0.048000729861962736 coverage 0.6437127622646234 90 pairs 3000810 label_hits 120309 precision 0.040092175112719564 coverage 0.6846280352133114 180 pairs 4354969 label_hits 127668 precision 0.029315478479869775 coverage 0.726505016246607 365 pairs 5514505 label_hits 130935 precision 0.02374374490548109 coverage 0.7450961423555589 ## Step 18 · agent · 2026-09-24T17:39:38.190Z ### Thinking unavailable — no text recorded ## Step 19 · agent · 2026-09-24T17:39:47.119Z ### Thinking unavailable — no text recorded The first diagnostic is encouraging: about 75% of validation-relevant pairs appeared in prior positive history, and recent repeats are highly predictive (roughly 11.5% relevance among pairs heard in the prior week). Explicit likes carry a different, more discovery-heavy pattern, so I’m building a hybrid: high-confidence repeat affinity plus personalized collaborative candidates and popularity fallback, with active dislikes suppressed. ### exec · 2026-09-24T17:40:23.215Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fast public-window experiments for the music discovery ranker.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import gc\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+DAY = 86_400\\n+\\n+\\n+def aggregate_history() -> pd.DataFrame:\\n+ cache = OUT / \\\"train_pair_features.parquet\\\"\\n+ if cache.exists():\\n+ return pd.read_parquet(cache)\\n+ targets = set(pd.read_parquet(DATA / \\\"target_users.parquet\\\", columns=[\\\"uid\\\"]).uid)\\n+ catalog = set(pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\", columns=[\\\"item_id\\\"]).item_id)\\n+ x = pd.read_parquet(\\n+ DATA / \\\"train_listens.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ x = x[x.uid.isin(targets) & x.item_id.isin(catalog) & (x.played_ratio_pct >= 50)].copy()\\n+ x[\\\"org\\\"] = x.is_organic.astype(\\\"uint8\\\")\\n+ x[\\\"sorg\\\"] = ((x.is_organic == 1) & (x.played_ratio_pct >= 80)).astype(\\\"uint8\\\")\\n+ x[\\\"recent7\\\"] = (x.timestamp >= TRAIN_CUTOFF - 7 * DAY).astype(\\\"uint8\\\")\\n+ x[\\\"recent30\\\"] = (x.timestamp >= TRAIN_CUTOFF - 30 * DAY).astype(\\\"uint8\\\")\\n+ x[\\\"orecent30\\\"] = (x[\\\"recent30\\\"] & x[\\\"org\\\"]).astype(\\\"uint8\\\")\\n+ g = x.groupby([\\\"uid\\\", \\\"item_id\\\"], sort=False).agg(\\n+ cnt=(\\\"timestamp\\\", \\\"size\\\"),\\n+ last=(\\\"timestamp\\\", \\\"max\\\"),\\n+ org=(\\\"org\\\", \\\"sum\\\"),\\n+ sorg=(\\\"sorg\\\", \\\"sum\\\"),\\n+ recent7=(\\\"recent7\\\", \\\"sum\\\"),\\n+ recent30=(\\\"recent30\\\", \\\"sum\\\"),\\n+ orecent30=(\\\"orecent30\\\", \\\"sum\\\"),\\n+ )\\n+ lastorg = x[x.org == 1].groupby([\\\"uid\\\", \\\"item_id\\\"], sort=False).timestamp.max().rename(\\\"lastorg\\\")\\n+ g = g.join(lastorg).reset_index()\\n+ del x, lastorg\\n+ gc.collect()\\n+ pref = pd.read_parquet(DATA / \\\"validation_preference_state.parquet\\\")\\n+ liked = pref[pref.state == \\\"liked\\\"][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().assign(liked=np.uint8(1))\\n+ disliked = pref[pref.state == \\\"disliked\\\"][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().assign(disliked=np.uint8(1))\\n+ g = g.merge(liked, how=\\\"left\\\").merge(disliked, how=\\\"left\\\")\\n+ g[[\\\"liked\\\", \\\"disliked\\\"]] = g[[\\\"liked\\\", \\\"disliked\\\"]].fillna(0).astype(\\\"uint8\\\")\\n+ g.to_parquet(cache, index=False)\\n+ return g\\n+\\n+\\n+def scores(g: pd.DataFrame, variant: str) -> np.ndarray:\\n+ age = np.maximum((TRAIN_CUTOFF - g[\\\"last\\\"].to_numpy()) / DAY, 0)\\n+ age_org = np.maximum((TRAIN_CUTOFF - g[\\\"lastorg\\\"].fillna(0).to_numpy()) / DAY, 0)\\n+ cnt = g.cnt.to_numpy()\\n+ org = g.org.to_numpy()\\n+ sorg = g.sorg.to_numpy()\\n+ r7 = g.recent7.to_numpy()\\n+ r30 = g.recent30.to_numpy()\\n+ or30 = g.orecent30.to_numpy()\\n+ liked = g.liked.to_numpy()\\n+ disliked = g.disliked.to_numpy()\\n+ if variant == \\\"recency\\\":\\n+ s = -age + 0.001 * np.log1p(cnt) + 8 * liked\\n+ elif variant == \\\"decay\\\":\\n+ s = (\\n+ 4.0 * np.exp(-age / 5)\\n+ + 2.5 * np.log1p(r7)\\n+ + 1.2 * np.log1p(r30)\\n+ + 0.5 * np.log1p(cnt)\\n+ + 1.5 * np.log1p(org)\\n+ + 2.0 * liked\\n+ )\\n+ elif variant == \\\"organic\\\":\\n+ s = (\\n+ 4.0 * np.exp(-age_org / 7)\\n+ + 3.0 * np.log1p(or30)\\n+ + 1.5 * np.log1p(sorg)\\n+ + 0.3 * np.log1p(cnt)\\n+ + 3.0 * liked\\n+ )\\n+ elif variant == \\\"balanced\\\":\\n+ s = (\\n+ 3.0 * np.exp(-age / 7)\\n+ + 2.0 * np.exp(-age_org / 14)\\n+ + 1.8 * np.log1p(r7)\\n+ + 1.2 * np.log1p(r30)\\n+ + 1.0 * np.log1p(or30)\\n+ + 0.65 * np.log1p(cnt)\\n+ + 0.7 * np.log1p(sorg)\\n+ + 2.5 * liked\\n+ )\\n+ elif variant == \\\"liked\\\":\\n+ s = (\\n+ 3.0 * np.exp(-age / 7)\\n+ + 1.5 * np.log1p(r7)\\n+ + 1.0 * np.log1p(r30)\\n+ + 0.5 * np.log1p(cnt)\\n+ + 8.0 * liked\\n+ )\\n+ else:\\n+ raise ValueError(variant)\\n+ return s - 100 * disliked\\n+\\n+\\n+def build(variant: str) -> Path:\\n+ g = aggregate_history()\\n+ g[\\\"score\\\"] = scores(g, variant)\\n+ z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ uids, item_ids = z[\\\"uids\\\"], z[\\\"item_ids\\\"]\\n+ eligible = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(item_ids), bitorder=\\\"little\\\").astype(bool)\\n+ item_col = {int(v): i for i, v in enumerate(item_ids)}\\n+ uid_row = {int(v): i for i, v in enumerate(uids)}\\n+ # Catalog order is the robust global-popularity fallback.\\n+ rows = []\\n+ groups = {int(uid): frame for uid, frame in g.groupby(\\\"uid\\\", sort=False)}\\n+ for uid in uids:\\n+ r = uid_row[int(uid)]\\n+ mask = eligible[r]\\n+ chosen = []\\n+ seen = set()\\n+ h = groups.get(int(uid))\\n+ if h is not None:\\n+ h = h.sort_values([\\\"score\\\", \\\"last\\\", \\\"cnt\\\"], ascending=False)\\n+ for item in h.item_id.to_numpy():\\n+ col = item_col.get(int(item))\\n+ if col is not None and mask[col]:\\n+ chosen.append(int(item)); seen.add(int(item))\\n+ if len(chosen) == 100:\\n+ break\\n+ if len(chosen) < 100:\\n+ for col in np.flatnonzero(mask):\\n+ item = int(item_ids[col])\\n+ if item not in seen:\\n+ chosen.append(item)\\n+ if len(chosen) == 100:\\n+ break\\n+ rows.extend((int(uid), item, rank) for rank, item in enumerate(chosen, 1))\\n+ out = OUT / f\\\"validation_{variant}.parquet\\\"\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(out, index=False)\\n+ return out\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"variant\\\", choices=[\\\"recency\\\", \\\"decay\\\", \\\"organic\\\", \\\"balanced\\\", \\\"liked\\\"])\\n+ args = parser.parse_args()\\n+ print(build(args.variant))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:40:23.285Z Script completed Wall time 0.0 seconds Output: {} ## Step 20 · agent · 2026-09-24T17:40:25.080Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:27.581Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /app/solution/experiment.py && for v in recency decay organic balanced liked; do /opt/task-venv/bin/python /app/solution/experiment.py \\\"$v\\\" && /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_${v}.parquet\\\"; done\",workdir:\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:40:28.756Z Script completed Wall time 1.2 seconds Output: SESSION_ID=11729 -rw-r--r-- 1 agent agent 5672 Sep 24 17:40 /app/solution/experiment.py ## Step 21 · agent · 2026-09-24T17:40:30.039Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:30.734Z { "code": "const r = await tools.write_stdin({session_id:11729,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:40:41.750Z Script running with cell ID 12 Wall time 11.0 seconds Output: ## Step 22 · agent · 2026-09-24T17:40:43.402Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:40:43.795Z { "cell_id": "12", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:41:00.753Z Script completed Wall time 17.0 seconds Output: /app/solution/validation_recency.parquet { "model": "/app/solution/validation_recency.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 23 · agent · 2026-09-24T17:41:02.722Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:03.767Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution; ps aux | rg 'experiment|evaluate' || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:41:03.850Z Script completed Wall time 0.0 seconds Output: total 59M -rw-r--r-- 1 agent agent 5.6K Sep 24 17:40 experiment.py -rw-r--r-- 1 agent agent 58M Sep 24 17:40 train_pair_features.parquet -rw-r--r-- 1 agent agent 712K Sep 24 17:40 validation_recency.parquet /bin/sh: 1: ps: not found ## Step 24 · agent · 2026-09-24T17:41:05.583Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:07.324Z { "code": "const r = await tools.exec_command({cmd:\"for v in decay organic balanced liked; do /opt/task-venv/bin/python /app/solution/experiment.py \\\"$v\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_${v}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:41:18.338Z Script running with cell ID 14 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:41:20.153Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:41:37.500Z Script completed Wall time 17.3 seconds Output: /app/solution/validation_decay.parquet { "model": "/app/solution/validation_decay.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } /app/solution/validation_organic.parquet ## Step 25 · agent · 2026-09-24T17:41:39.480Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:41.199Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_organic.parquet; /opt/task-venv/bin/python /app/solution/experiment.py balanced\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:41:52.219Z Script running with cell ID 15 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:41:56.134Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:41:57.883Z Script completed Wall time 1.7 seconds Output: { "model": "/app/solution/validation_organic.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } /app/solution/validation_balanced.parquet ## Step 26 · agent · 2026-09-24T17:42:03.549Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:08.216Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data';g=pd.read_parquet('/app/solution/train_pair_features.parquet');z=np.load(D+'/validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\\ndef ok(u,i):\\n c=icol.get(int(i));r=urow.get(int(u));return c is not None and ((int(p[r,c//8])>>(c%8))&1)\\nq=np.fromiter((ok(u,i) for u,i in g[['uid','item_id']].itertuples(index=False,name=None)),dtype=bool,count=len(g));print('eligible hist',q.sum(),len(q),q.mean());print(g[q].head(),g[q].groupby('uid').size().describe())\\nPY\\ncmp -s /app/solution/validation_recency.parquet /app/solution/validation_decay.parquet; echo cmp:$?\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:42:13.842Z Script completed Wall time 5.6 seconds Output: eligible hist 0 5514505 0.0 Empty DataFrame Columns: [uid, item_id, cnt, last, org, sorg, recent7, recent30, orecent30, lastorg, liked, disliked] Index: [] count 0.0 mean NaN std NaN min NaN 25% NaN 50% NaN 75% NaN max NaN dtype: float64 cmp:0 ## Step 27 · agent · 2026-09-24T17:42:25.396Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-09-24T17:42:32.763Z ### Thinking unavailable — no text recorded A key boundary condition is now clear: validation eligibility excludes every previously positive user–track pair. That makes repeat-history rankers equivalent to popularity and explains the identical baseline score (0.010710). I’m shifting the model to true discovery: learn each user’s taste from consumed tracks, then score only unseen eligible tracks through collaborative latent factors, with popularity blended after per-user normalization. ### exec · 2026-09-24T17:42:56.167Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/svd_experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fit latent collaborative models and emit several public rankings.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import gc\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+from sklearn.preprocessing import normalize\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def main() -> None:\\n+ users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy()\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ items = catalog.item_id.to_numpy()\\n+ uidx = {int(v): i for i, v in enumerate(users)}\\n+ iidx = {int(v): i for i, v in enumerate(items)}\\n+ g = pd.read_parquet(OUT / \\\"train_pair_features.parquet\\\")\\n+ rows = g.uid.map(uidx).to_numpy()\\n+ cols = g.item_id.map(iidx).to_numpy()\\n+ # Confidence-weighted implicit taste matrix. Long-term affinity is useful\\n+ # because eligible candidates are all new to the user.\\n+ vals = (\\n+ 1.0\\n+ + 0.45 * np.log1p(g.cnt.to_numpy())\\n+ + 0.45 * np.log1p(g.org.to_numpy())\\n+ + 0.35 * np.log1p(g.sorg.to_numpy())\\n+ + 1.25 * g.liked.to_numpy()\\n+ ).astype(\\\"float32\\\")\\n+ X = sparse.csr_matrix((vals, (rows, cols)), shape=(len(users), len(items)), dtype=np.float32)\\n+ del g, rows, cols, vals\\n+ gc.collect()\\n+\\n+ # Damp ubiquitous tracks, then L2-normalize users so activity level cannot\\n+ # dominate the factorization.\\n+ df = np.diff(X.tocsc().indptr).astype(np.float32)\\n+ idf = np.log1p(len(users) / (1.0 + df)).astype(np.float32)\\n+ Xw = X.multiply(np.power(idf, 0.55)).tocsr()\\n+ Xw = normalize(Xw, norm=\\\"l2\\\", axis=1, copy=False)\\n+ print(\\\"matrix\\\", Xw.shape, Xw.nnz, flush=True)\\n+ svd = TruncatedSVD(n_components=96, n_iter=5, random_state=20260924)\\n+ U = svd.fit_transform(Xw).astype(\\\"float32\\\")\\n+ V = svd.components_.astype(\\\"float32\\\")\\n+ print(\\\"explained\\\", float(svd.explained_variance_ratio_.sum()), flush=True)\\n+ del X, Xw, svd\\n+ gc.collect()\\n+\\n+ ez = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ assert np.array_equal(ez[\\\"uids\\\"], users)\\n+ assert np.array_equal(ez[\\\"item_ids\\\"], items)\\n+ packed = ez[\\\"packed_eligible\\\"]\\n+ pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ # Blend coefficient is in per-user latent standard deviations.\\n+ betas = [0.0, 0.08, 0.16, 0.28, 0.45, 0.70]\\n+ outputs = {b: [] for b in betas}\\n+ block = 128\\n+ for lo in range(0, len(users), block):\\n+ hi = min(lo + block, len(users))\\n+ base = U[lo:hi] @ V\\n+ # Normalize each user's factor score before blending item bias.\\n+ base -= base.mean(axis=1, keepdims=True)\\n+ base /= base.std(axis=1, keepdims=True) + 1e-7\\n+ elig = np.unpackbits(packed[lo:hi], axis=1, count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ base[~elig] = -np.inf\\n+ for beta in betas:\\n+ score = base + beta * pop[None, :]\\n+ # argpartition then exact order within the selected 100.\\n+ top = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ top_score = np.take_along_axis(score, top, axis=1)\\n+ order = np.argsort(-top_score, axis=1)\\n+ top = np.take_along_axis(top, order, axis=1)\\n+ for rr, row in enumerate(top):\\n+ uid = int(users[lo + rr])\\n+ outputs[beta].extend((uid, int(items[c]), rank) for rank, c in enumerate(row, 1))\\n+ if lo % 1024 == 0:\\n+ print(\\\"scored\\\", hi, flush=True)\\n+ for beta, rows_out in outputs.items():\\n+ tag = str(beta).replace(\\\".\\\", \\\"p\\\")\\n+ path = OUT / f\\\"validation_svd_b{tag}.parquet\\\"\\n+ pd.DataFrame(rows_out, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(path, index=False)\\n+ print(path, flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:42:56.222Z Script completed Wall time 0.0 seconds Output: {} ## Step 29 · agent · 2026-09-24T17:42:58.211Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:59.655Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:43:00.836Z Script completed Wall time 1.2 seconds Output: SESSION_ID=96471 ## Step 30 · agent · 2026-09-24T17:43:03.172Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:03.850Z { "code": "const r = await tools.write_stdin({session_id:96471,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:43:14.863Z Script running with cell ID 19 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:16.473Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:43:19.756Z Script completed Wall time 3.3 seconds Output: matrix (8456, 30000) 5514505 explained 0.20058992505073547 scored 128 scored 1152 scored 2176 scored 3200 scored 4224 scored 5248 scored 6272 scored 7296 scored 8320 /app/solution/validation_svd_b0p0.parquet /app/solution/validation_svd_b0p08.parquet /app/solution/validation_svd_b0p16.parquet /app/solution/validation_svd_b0p28.parquet /app/solution/validation_svd_b0p45.parquet /app/solution/validation_svd_b0p7.parquet ## Step 31 · agent · 2026-09-24T17:43:22.371Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:23.803Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_svd_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:43:34.820Z Script running with cell ID 20 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:37.546Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:43:39.272Z Script completed Wall time 1.7 seconds Output: { "model": "/app/solution/validation_svd_b0p0.parquet", "scores": { "quality_ndcg@10": 0.024054, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_b0p08.parquet", "scores": { "quality_ndcg@10": 0.024123, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_b0p16.parquet", "scores": { "quality_ndcg@10": 0.024285, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_b0p28.parquet", "scores": { "quality_ndcg@10": 0.02439, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_b0p45.parquet", "scores": { "quality_ndcg@10": 0.024801, "quality_users": 4263 } } { "model": "/app/solution/validation_svd_b0p7.parquet", "scores": { "quality_ndcg@10": 0.024978, "quality_users": 4263 } } ## Step 32 · agent · 2026-09-24T17:43:52.679Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-09-24T17:43:59.451Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:11.859Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/pop_experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Temporal popularity experiments for unseen eligible tracks.\\\"\\\"\\\"\\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=24_786_800; DAY=86_400\\n+\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+items=cat.item_id.to_numpy(); iset=set(items); idx={int(x):i for i,x in enumerate(items)}\\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+x=x[x.item_id.isin(iset)&x.is_organic.eq(1)&x.played_ratio_pct.ge(80)]\\nx['d7']=(x.timestamp>=CUT-7*DAY).astype('uint8');x['d30']=(x.timestamp>=CUT-30*DAY).astype('uint8');x['d90']=(x.timestamp>=CUT-90*DAY).astype('uint8')\\ng=x.groupby('item_id').agg(ev7=('d7','sum'),ev30=('d30','sum'),ev90=('d90','sum'),evall=('uid','size'))\\nfor n,d in [(7,7),(30,30)]:\\n q=x[x.timestamp>=CUT-d*DAY].groupby('item_id').uid.nunique().rename(f'u{n}')\\n g=g.join(q)\\ndel x\\nlk=pd.read_parquet(D/'train_likes.parquet')\\nlk=lk[lk.item_id.isin(iset)&lk.is_organic.eq(1)]\\nfor n,d in [(7,7),(30,30),(90,90)]:\\n q=lk[lk.timestamp>=CUT-d*DAY].groupby('item_id').size().rename(f'l{n}')\\n g=g.join(q)\\ng=cat[['item_id','organic_positive_listens']].merge(g.reset_index(),how='left').fillna(0)\\nfor c in g.columns[1:]: g[c]=np.log1p(g[c].to_numpy(dtype='float32'))\\n# All scores are global, but eligibility makes the actual list user-specific.\\nscores={\\n 'total':g.organic_positive_listens,\\n 'ev7':g.ev7,\\n 'ev30':g.ev30,\\n 'u7':g.u7,\\n 'recentblend':1.4*g.u7+0.8*g.u30+0.5*g.ev30+0.25*g.organic_positive_listens,\\n 'likeblend':1.0*g.l7+0.5*g.l30+0.35*g.u7+0.2*g.organic_positive_listens,\\n 'allblend':1.2*g.u7+0.6*g.u30+0.6*g.l7+0.3*g.l30+0.3*g.organic_positive_listens,\\n}\\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(z['item_ids'],items)\\nfor name,score in scores.items():\\n order=np.argsort(-np.asarray(score),kind='stable');rows=[]\\n for r,uid in enumerate(z['uids']):\\n mask=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\\n take=order[mask[order]][:100]\\n rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(take,1))\\n p=O/f'validation_pop_{name}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(p,index=False);print(p)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:44:11.958Z Script failed Wall time 0.0 seconds Output: Script error: apply_patch verification failed: invalid hunk at line 15, 'x['d7']=(x.timestamp>=CUT-7*DAY).astype('uint8');x['d30']=(x.timestamp>=CUT-30*DAY).astype('uint8');x['d90']=(x.timestamp>=CUT-90*DAY).astype('uint8')' is not a valid hunk header. Valid hunk headers: '*** Add File: {path}', '*** Delete File: {path}', '*** Update File: {path}' ## Step 34 · agent · 2026-09-24T17:44:15.175Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:31.775Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/pop_experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Temporal popularity experiments for unseen eligible tracks.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D = Path('/task/data')\\n+O = Path('/app/solution')\\n+CUT = 24_786_800\\n+DAY = 86_400\\n+\\n+cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n+items = cat.item_id.to_numpy()\\n+iset = set(items)\\n+x = pd.read_parquet(D/'train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+x = x[x.item_id.isin(iset) & x.is_organic.eq(1) & x.played_ratio_pct.ge(80)]\\n+x['d7'] = (x.timestamp >= CUT-7*DAY).astype('uint8')\\n+x['d30'] = (x.timestamp >= CUT-30*DAY).astype('uint8')\\n+x['d90'] = (x.timestamp >= CUT-90*DAY).astype('uint8')\\n+g = x.groupby('item_id').agg(ev7=('d7','sum'), ev30=('d30','sum'), ev90=('d90','sum'), evall=('uid','size'))\\n+for n, d in [(7,7),(30,30)]:\\n+ q = x[x.timestamp >= CUT-d*DAY].groupby('item_id').uid.nunique().rename(f'u{n}')\\n+ g = g.join(q)\\n+del x\\n+lk = pd.read_parquet(D/'train_likes.parquet')\\n+lk = lk[lk.item_id.isin(iset) & lk.is_organic.eq(1)]\\n+for n, d in [(7,7),(30,30),(90,90)]:\\n+ q = lk[lk.timestamp >= CUT-d*DAY].groupby('item_id').size().rename(f'l{n}')\\n+ g = g.join(q)\\n+g = cat[['item_id','organic_positive_listens']].merge(g.reset_index(), how='left').fillna(0)\\n+for c in g.columns[1:]:\\n+ g[c] = np.log1p(g[c].to_numpy(dtype='float32'))\\n+scores = {\\n+ 'total': g.organic_positive_listens,\\n+ 'ev7': g.ev7,\\n+ 'ev30': g.ev30,\\n+ 'u7': g.u7,\\n+ 'recentblend': 1.4*g.u7 + 0.8*g.u30 + 0.5*g.ev30 + 0.25*g.organic_positive_listens,\\n+ 'likeblend': 1.0*g.l7 + 0.5*g.l30 + 0.35*g.u7 + 0.2*g.organic_positive_listens,\\n+ 'allblend': 1.2*g.u7 + 0.6*g.u30 + 0.6*g.l7 + 0.3*g.l30 + 0.3*g.organic_positive_listens,\\n+}\\n+z = np.load(D/'validation_eligible_candidates.npz')\\n+assert np.array_equal(z['item_ids'], items)\\n+for name, score in scores.items():\\n+ order = np.argsort(-np.asarray(score), kind='stable')\\n+ rows = []\\n+ for r, uid in enumerate(z['uids']):\\n+ mask = np.unpackbits(z['packed_eligible'][r], count=len(items), bitorder='little').astype(bool)\\n+ take = order[mask[order]][:100]\\n+ rows.extend((int(uid), int(items[c]), rank) for rank, c in enumerate(take, 1))\\n+ p = O/f'validation_pop_{name}.parquet'\\n+ pd.DataFrame(rows, columns=['uid','item_id','rank']).to_parquet(p, index=False)\\n+ print(p)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:44:31.832Z Script completed Wall time 0.0 seconds Output: {} ## Step 35 · agent · 2026-09-24T17:44:33.307Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:34.754Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:44:35.936Z Script completed Wall time 1.2 seconds Output: SESSION_ID=5471 ### exec · 2026-09-24T17:44:38.474Z { "code": "const r = await tools.write_stdin({session_id:5471,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:44:49.490Z Script running with cell ID 24 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:44:51.810Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:45:08.493Z Script completed Wall time 16.7 seconds Output: ## Step 36 · agent · 2026-09-24T17:45:10.271Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:11.253Z { "code": "const r = await tools.exec_command({cmd:\"ls /app/solution/validation_pop_* 2>/dev/null || true; df -h /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:45:11.350Z Script completed Wall time 0.0 seconds Output: Filesystem Size Used Avail Use% Mounted on overlay 96G 12G 85G 12% / ## Step 37 · agent · 2026-09-24T17:45:17.559Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:23.615Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n-x = pd.read_parquet(D/'train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n-x = x[x.item_id.isin(iset) & x.is_organic.eq(1) & x.played_ratio_pct.ge(80)]\\n+x = pd.read_parquet(\\n+ D/'train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],\\n+ filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1),('played_ratio_pct','>=',80)],\\n+)\\n+x = x[x.item_id.isin(iset)]\\n@@\\n-g = x.groupby('item_id').agg(ev7=('d7','sum'), ev30=('d30','sum'), ev90=('d90','sum'), evall=('uid','size'))\\n+g = x.groupby('item_id').agg(ev7=('d7','sum'), ev30=('d30','sum'), ev90=('d90','sum'))\\n@@\\n-lk = pd.read_parquet(D/'train_likes.parquet')\\n-lk = lk[lk.item_id.isin(iset) & lk.is_organic.eq(1)]\\n+lk = pd.read_parquet(D/'train_likes.parquet', filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1)])\\n+lk = lk[lk.item_id.isin(iset)]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:45:23.715Z Script completed Wall time 0.0 seconds Output: {} ## Step 38 · agent · 2026-09-24T17:45:27.350Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:28.352Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:45:39.368Z Script running with cell ID 27 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:45:41.013Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:45:58.538Z Script completed Wall time 17.5 seconds Output: ## Step 39 · agent · 2026-09-24T17:46:04.910Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:05.717Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n scores = {\\n@@\\n }\\n+del x, lk, g\\n+import gc\\n+gc.collect()\\n z = np.load(D/'validation_eligible_candidates.npz')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:46:05.792Z Script completed Wall time 0.0 seconds Output: {} ## Step 40 · agent · 2026-09-24T17:46:10.143Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:13.062Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n from pathlib import Path\\n+import sys\\n@@\\n-for name, score in scores.items():\\n+selected = sys.argv[1:] or list(scores)\\n+for name in selected:\\n+ score = scores[name]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:46:13.177Z Script completed Wall time 0.0 seconds Output: {} ## Step 41 · agent · 2026-09-24T17:46:16.512Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:17.716Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py ev7 ev30 u7 recentblend likeblend allblend\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:46:20.116Z Script completed Wall time 2.4 seconds Output: Traceback (most recent call last): File "/app/solution/pop_experiment.py", line 46, in del x, lk, g ^ NameError: name 'x' is not defined ## Step 42 · agent · 2026-09-24T17:46:22.955Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:24.045Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"nl -ba /app/solution/pop_experiment.py | sed -n '1,100p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});text(rest);text(output);" } ### Tool output · 2026-09-24T17:46:24.136Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b39650","wall_time_seconds":0.00000416,"exit_code":0,"original_token_count":734} 1 #!/usr/bin/env python3 2 """Temporal popularity experiments for unseen eligible tracks.""" 3 from pathlib import Path 4 import sys 5 import numpy as np 6 import pandas as pd 7 8 D = Path('/task/data') 9 O = Path('/app/solution') 10 CUT = 24_786_800 11 DAY = 86_400 12 13 cat = pd.read_parquet(D/'candidate_catalog.parquet') 14 items = cat.item_id.to_numpy() 15 iset = set(items) 16 x = pd.read_parquet( 17 D/'train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'], 18 filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1),('played_ratio_pct','>=',80)], 19 ) 20 x = x[x.item_id.isin(iset)] 21 x['d7'] = (x.timestamp >= CUT-7*DAY).astype('uint8') 22 x['d30'] = (x.timestamp >= CUT-30*DAY).astype('uint8') 23 x['d90'] = (x.timestamp >= CUT-90*DAY).astype('uint8') 24 g = x.groupby('item_id').agg(ev7=('d7','sum'), ev30=('d30','sum'), ev90=('d90','sum')) 25 for n, d in [(7,7),(30,30)]: 26 q = x[x.timestamp >= CUT-d*DAY].groupby('item_id').uid.nunique().rename(f'u{n}') 27 g = g.join(q) 28 del x 29 lk = pd.read_parquet(D/'train_likes.parquet', filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1)]) 30 lk = lk[lk.item_id.isin(iset)] 31 for n, d in [(7,7),(30,30),(90,90)]: 32 q = lk[lk.timestamp >= CUT-d*DAY].groupby('item_id').size().rename(f'l{n}') 33 g = g.join(q) 34 g = cat[['item_id','organic_positive_listens']].merge(g.reset_index(), how='left').fillna(0) 35 for c in g.columns[1:]: 36 g[c] = np.log1p(g[c].to_numpy(dtype='float32')) 37 scores = { 38 'total': g.organic_positive_listens, 39 'ev7': g.ev7, 40 'ev30': g.ev30, 41 'u7': g.u7, 42 'recentblend': 1.4*g.u7 + 0.8*g.u30 + 0.5*g.ev30 + 0.25*g.organic_positive_listens, 43 'likeblend': 1.0*g.l7 + 0.5*g.l30 + 0.35*g.u7 + 0.2*g.organic_positive_listens, 44 'allblend': 1.2*g.u7 + 0.6*g.u30 + 0.6*g.l7 + 0.3*g.l30 + 0.3*g.organic_positive_listens, 45 } 46 del x, lk, g 47 import gc 48 gc.collect() 49 z = np.load(D/'validation_eligible_candidates.npz') 50 assert np.array_equal(z['item_ids'], items) 51 selected = sys.argv[1:] or list(scores) 52 for name in selected: 53 score = scores[name] 54 order = np.argsort(-np.asarray(score), kind='stable') 55 rows = [] 56 for r, uid in enumerate(z['uids']): 57 mask = np.unpackbits(z['packed_eligible'][r], count=len(items), bitorder='little').astype(bool) 58 take = order[mask[order]][:100] 59 rows.extend((int(uid), int(items[c]), rank) for rank, c in enumerate(take, 1)) 60 p = O/f'validation_pop_{name}.parquet' 61 pd.DataFrame(rows, columns=['uid','item_id','rank']).to_parquet(p, index=False) 62 print(p) ## Step 43 · agent · 2026-09-24T17:46:26.605Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:27.409Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n-del x, lk, g\\n+del lk, g\\n*** End Patch\"));" } ### Tool output · 2026-09-24T17:46:27.498Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:46:30.343Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py ev7 ev30 u7 recentblend likeblend allblend\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:46:41.357Z Script running with cell ID 33 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:44.950Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:47:00.543Z Script completed Wall time 15.6 seconds Output: ## Step 44 · agent · 2026-09-24T17:47:03.446Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:04.003Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_pop_* 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:47:04.090Z Script completed Wall time 0.0 seconds Output: ## Step 45 · agent · 2026-09-24T17:47:11.890Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:36.853Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n import sys\\n import numpy as np\\n import pandas as pd\\n+import pyarrow.parquet as pq\\n@@\\n cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n items = cat.item_id.to_numpy()\\n-iset = set(items)\\n-x = pd.read_parquet(\\n- D/'train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],\\n- filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1),('played_ratio_pct','>=',80)],\\n-)\\n-x = x[x.item_id.isin(iset)]\\n-x['d7'] = (x.timestamp >= CUT-7*DAY).astype('uint8')\\n-x['d30'] = (x.timestamp >= CUT-30*DAY).astype('uint8')\\n-x['d90'] = (x.timestamp >= CUT-90*DAY).astype('uint8')\\n-g = x.groupby('item_id').agg(ev7=('d7','sum'), ev30=('d30','sum'), ev90=('d90','sum'))\\n-for n, d in [(7,7),(30,30)]:\\n- q = x[x.timestamp >= CUT-d*DAY].groupby('item_id').uid.nunique().rename(f'u{n}')\\n- g = g.join(q)\\n-del x\\n-lk = pd.read_parquet(D/'train_likes.parquet', filters=[('timestamp','>=',CUT-90*DAY),('is_organic','=',1)])\\n-lk = lk[lk.item_id.isin(iset)]\\n-for n, d in [(7,7),(30,30),(90,90)]:\\n- q = lk[lk.timestamp >= CUT-d*DAY].groupby('item_id').size().rename(f'l{n}')\\n- g = g.join(q)\\n-g = cat[['item_id','organic_positive_listens']].merge(g.reset_index(), how='left').fillna(0)\\n-for c in g.columns[1:]:\\n- g[c] = np.log1p(g[c].to_numpy(dtype='float32'))\\n+sorter = np.argsort(items)\\n+sorted_items = items[sorter]\\n+def columns_for(raw):\\n+ pos = np.searchsorted(sorted_items, raw)\\n+ good = pos < len(sorted_items)\\n+ good[good] &= sorted_items[pos[good]] == raw[good]\\n+ return sorter[pos[good]], good\\n+\\n+ev7=np.zeros(len(items),dtype='int64'); ev30=ev7.copy(); ev90=ev7.copy()\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for b in pf.iter_batches(batch_size=1_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n+ t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy(); ratio=b.column(3).to_numpy()\\n+ base=(t>=CUT-90*DAY)&(org==1)&(ratio>=80)\\n+ if not base.any(): continue\\n+ raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n+ ev90 += np.bincount(col,minlength=len(items))\\n+ ev30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items))\\n+ ev7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n+print('listen stats',flush=True)\\n+l7=np.zeros(len(items),dtype='int64'); l30=l7.copy(); l90=l7.copy()\\n+pf=pq.ParquetFile(D/'train_likes.parquet')\\n+for b in pf.iter_batches(batch_size=500_000,columns=['timestamp','item_id','is_organic']):\\n+ t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy()\\n+ base=(t>=CUT-90*DAY)&(org==1); raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n+ l90 += np.bincount(col,minlength=len(items)); l30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items)); l7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n+print('like stats',flush=True)\\n+total=np.log1p(cat.organic_positive_listens.to_numpy(dtype='float32'))\\n+ev7=np.log1p(ev7); ev30=np.log1p(ev30); ev90=np.log1p(ev90); l7=np.log1p(l7); l30=np.log1p(l30); l90=np.log1p(l90)\\n scores = {\\n- 'total': g.organic_positive_listens,\\n- 'ev7': g.ev7,\\n- 'ev30': g.ev30,\\n- 'u7': g.u7,\\n- 'recentblend': 1.4*g.u7 + 0.8*g.u30 + 0.5*g.ev30 + 0.25*g.organic_positive_listens,\\n- 'likeblend': 1.0*g.l7 + 0.5*g.l30 + 0.35*g.u7 + 0.2*g.organic_positive_listens,\\n- 'allblend': 1.2*g.u7 + 0.6*g.u30 + 0.6*g.l7 + 0.3*g.l30 + 0.3*g.organic_positive_listens,\\n+ 'total': total,\\n+ 'ev7': ev7,\\n+ 'ev30': ev30,\\n+ 'u7': ev7,\\n+ 'recentblend': 1.8*ev7 + 0.8*ev30 + 0.25*total,\\n+ 'likeblend': 1.0*l7 + 0.5*l30 + 0.35*ev7 + 0.2*total,\\n+ 'allblend': 1.2*ev7 + 0.6*ev30 + 0.6*l7 + 0.3*l30 + 0.3*total,\\n }\\n-del lk, g\\n import gc\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:47:37.002Z Script completed Wall time 0.0 seconds Output: {} ## Step 46 · agent · 2026-09-24T17:47:39.267Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:47:40.293Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py ev7\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:47:51.310Z Script running with cell ID 36 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:53.721Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:48:10.472Z Script completed Wall time 16.8 seconds Output: listen stats like stats ## Step 47 · agent · 2026-09-24T17:48:17.605Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:36.890Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n-sorter = np.argsort(items)\\n+sorter = np.argsort(items)\\n sorted_items = items[sorter]\\n@@\\n-ev7=np.zeros(len(items),dtype='int64'); ev30=ev7.copy(); ev90=ev7.copy()\\n-pf=pq.ParquetFile(D/'train_listens.parquet')\\n-for b in pf.iter_batches(batch_size=1_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n- t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy(); ratio=b.column(3).to_numpy()\\n- base=(t>=CUT-90*DAY)&(org==1)&(ratio>=80)\\n- if not base.any(): continue\\n- raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n- ev90 += np.bincount(col,minlength=len(items))\\n- ev30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items))\\n- ev7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n-print('listen stats',flush=True)\\n-l7=np.zeros(len(items),dtype='int64'); l30=l7.copy(); l90=l7.copy()\\n-pf=pq.ParquetFile(D/'train_likes.parquet')\\n-for b in pf.iter_batches(batch_size=500_000,columns=['timestamp','item_id','is_organic']):\\n- t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy()\\n- base=(t>=CUT-90*DAY)&(org==1); raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n- l90 += np.bincount(col,minlength=len(items)); l30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items)); l7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n-print('like stats',flush=True)\\n+cache=O/'train_item_temporal.npz'\\n+if cache.exists():\\n+ zz=np.load(cache); ev7=zz['ev7']; ev30=zz['ev30']; ev90=zz['ev90']; l7=zz['l7']; l30=zz['l30']; l90=zz['l90']\\n+else:\\n+ ev7=np.zeros(len(items),dtype='int64'); ev30=ev7.copy(); ev90=ev7.copy()\\n+ pf=pq.ParquetFile(D/'train_listens.parquet')\\n+ for b in pf.iter_batches(batch_size=1_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n+ t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy(); ratio=b.column(3).to_numpy()\\n+ base=(t>=CUT-90*DAY)&(org==1)&(ratio>=80)\\n+ if not base.any(): continue\\n+ raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n+ ev90 += np.bincount(col,minlength=len(items))\\n+ ev30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items))\\n+ ev7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n+ print('listen stats',flush=True)\\n+ l7=np.zeros(len(items),dtype='int64'); l30=l7.copy(); l90=l7.copy()\\n+ pf=pq.ParquetFile(D/'train_likes.parquet')\\n+ for b in pf.iter_batches(batch_size=500_000,columns=['timestamp','item_id','is_organic']):\\n+ t=b.column(0).to_numpy(); raw=b.column(1).to_numpy(); org=b.column(2).to_numpy()\\n+ base=(t>=CUT-90*DAY)&(org==1); raw=raw[base]; tt=t[base]; col,good=columns_for(raw); tt=tt[good]\\n+ l90 += np.bincount(col,minlength=len(items)); l30 += np.bincount(col[tt>=CUT-30*DAY],minlength=len(items)); l7 += np.bincount(col[tt>=CUT-7*DAY],minlength=len(items))\\n+ print('like stats',flush=True)\\n+ np.savez_compressed(cache,ev7=ev7,ev30=ev30,ev90=ev90,l7=l7,l30=l30,l90=l90)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:48:36.972Z Script completed Wall time 0.0 seconds Output: {} ## Step 48 · agent · 2026-09-24T17:48:38.931Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:48:39.954Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py ev7\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:48:50.974Z Script running with cell ID 38 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:55.389Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:49:10.137Z Script completed Wall time 14.7 seconds Output: listen stats like stats ## Step 49 · agent · 2026-09-24T17:49:11.466Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:14.334Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/train_item_temporal.npz && /opt/task-venv/bin/python /app/solution/pop_experiment.py ev7 ev30 recentblend likeblend allblend\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:49:25.353Z Script running with cell ID 39 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:26.773Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:49:44.537Z Script completed Wall time 17.8 seconds Output: -rw-r--r-- 1 agent agent 161K Sep 24 17:48 /app/solution/train_item_temporal.npz ## Step 50 · agent · 2026-09-24T17:49:50.234Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:53.034Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n order = np.argsort(-np.asarray(score), kind='stable')\\n rows = []\\n for r, uid in enumerate(z['uids']):\\n- mask = np.unpackbits(z['packed_eligible'][r], count=len(items), bitorder='little').astype(bool)\\n- take = order[mask[order]][:100]\\n+ probe = order[:500]\\n+ keep = ((z['packed_eligible'][r, probe//8] >> (probe%8)) & 1).astype(bool)\\n+ take = probe[keep][:100]\\n+ if len(take) < 100:\\n+ mask = np.unpackbits(z['packed_eligible'][r], count=len(items), bitorder='little').astype(bool)\\n+ take = order[mask[order]][:100]\\n@@\\n- print(p)\\n+ print(p, flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:49:53.137Z Script completed Wall time 0.0 seconds Output: {} ## Step 51 · agent · 2026-09-24T17:49:54.644Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:55.796Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/pop_experiment.py ev7 ev30 recentblend likeblend allblend\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:50:06.854Z Script running with cell ID 41 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:50:09.334Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:50:26.039Z Script completed Wall time 16.7 seconds Output: ## Step 52 · agent · 2026-09-24T17:50:32.731Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:33.534Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/pop_experiment.py ev7\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:50:34.738Z Script completed Wall time 1.2 seconds Output: SESSION_ID=13875 ### exec · 2026-09-24T17:50:36.637Z { "code": "const r = await tools.write_stdin({session_id:13875,chars:\"\",yield_time_ms:5000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-24T17:50:41.670Z Script completed Wall time 5.0 seconds Output: ## Step 53 · agent · 2026-09-24T17:50:45.660Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:46.362Z { "code": "const r = await tools.write_stdin({session_id:13875,chars:\"\",yield_time_ms:5000,max_output_tokens:5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:50:51.459Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"ebc2a0","wall_time_seconds":5.000877237,"session_id":13875,"original_token_count":0,"output":""} ## Step 54 · agent · 2026-09-24T17:50:54.734Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:55.134Z { "code": "const r = await tools.write_stdin({session_id:13875,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:51:05.175Z Script completed Wall time 10.0 seconds Output: {"chunk_id":"d799a7","wall_time_seconds":10.001197419,"session_id":13875,"original_token_count":0,"output":""} ## Step 55 · agent · 2026-09-24T17:51:07.034Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:07.834Z { "code": "const r = await tools.write_stdin({session_id:13875,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:51:08.139Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"77b1c0","wall_time_seconds":0.269343233,"exit_code":130,"original_token_count":270,"output":"Traceback (most recent call last):\n File \"/app/solution/pop_experiment.py\", line 69, in \n keep = ((z['packed_eligible'][r, probe//8] >> (probe%8)) & 1).astype(bool)\n ~^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"} ## Step 56 · agent · 2026-09-24T17:51:10.011Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:14.487Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/pop_experiment.py\\n@@\\n z = np.load(D/'validation_eligible_candidates.npz')\\n assert np.array_equal(z['item_ids'], items)\\n+eligible_uids = z['uids'].copy()\\n+packed = z['packed_eligible'].copy()\\n selected = sys.argv[1:] or list(scores)\\n@@\\n- for r, uid in enumerate(z['uids']):\\n+ for r, uid in enumerate(eligible_uids):\\n@@\\n- keep = ((z['packed_eligible'][r, probe//8] >> (probe%8)) & 1).astype(bool)\\n+ keep = ((packed[r, probe//8] >> (probe%8)) & 1).astype(bool)\\n@@\\n- mask = np.unpackbits(z['packed_eligible'][r], count=len(items), bitorder='little').astype(bool)\\n+ mask = np.unpackbits(packed[r], count=len(items), bitorder='little').astype(bool)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:51:14.636Z Script completed Wall time 0.0 seconds Output: {} ## Step 57 · agent · 2026-09-24T17:51:16.334Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:17.534Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/pop_experiment.py ev7 ev30 recentblend likeblend allblend\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:51:28.562Z Script running with cell ID 48 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:51:30.234Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:51:37.172Z Script completed Wall time 6.9 seconds Output: /app/solution/validation_pop_ev7.parquet /app/solution/validation_pop_ev30.parquet /app/solution/validation_pop_recentblend.parquet /app/solution/validation_pop_likeblend.parquet /app/solution/validation_pop_allblend.parquet ## Step 58 · agent · 2026-09-24T17:51:38.556Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:40.053Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:51:51.149Z Script running with cell ID 49 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:51:53.334Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:52:10.349Z Script completed Wall time 17.0 seconds Output: { "model": "/app/solution/validation_pop_allblend.parquet", "scores": { "quality_ndcg@10": 0.030867, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_ev30.parquet", "scores": { "quality_ndcg@10": 0.013314, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_ev7.parquet", "scores": { "quality_ndcg@10": 0.028131, "quality_users": 4263 } } ## Step 59 · agent · 2026-09-24T17:52:12.734Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:14.382Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_pop_likeblend.parquet /app/solution/validation_pop_recentblend.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:52:25.475Z Script running with cell ID 50 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:27.637Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-09-24T17:52:37.283Z Script completed Wall time 9.6 seconds Output: { "model": "/app/solution/validation_pop_likeblend.parquet", "scores": { "quality_ndcg@10": 0.031534, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_recentblend.parquet", "scores": { "quality_ndcg@10": 0.02329, "quality_users": 4263 } } ## Step 60 · agent · 2026-09-24T17:52:42.834Z ### Thinking unavailable — no text recorded Temporal momentum is stronger than lifetime popularity: recent organic-like and strong-listen trends score 0.031534 alone, versus 0.010710 for catalog popularity and 0.024978 for the first collaborative model. I’m combining the two complementary signals now; after that I’ll test a like-specific latent model because grade-3 actions carry seven times the gain of a strong listen. ### exec · 2026-09-24T17:52:49.737Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/svd_experiment.py\\n@@\\n- pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\\n+ temporal = np.load(OUT / \\\"train_item_temporal.npz\\\")\\n+ ev7 = np.log1p(temporal[\\\"ev7\\\"].astype(np.float32))\\n+ l7 = np.log1p(temporal[\\\"l7\\\"].astype(np.float32))\\n+ l30 = np.log1p(temporal[\\\"l30\\\"].astype(np.float32))\\n+ total = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\\n+ pop = 1.0*l7 + 0.5*l30 + 0.35*ev7 + 0.2*total\\n pop = (pop - pop.mean()) / pop.std()\\n@@\\n- betas = [0.0, 0.08, 0.16, 0.28, 0.45, 0.70]\\n+ betas = [0.25, 0.45, 0.70, 1.0, 1.4, 2.0]\\n@@\\n- path = OUT / f\\\"validation_svd_b{tag}.parquet\\\"\\n+ path = OUT / f\\\"validation_svdtrend_b{tag}.parquet\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:52:49.883Z Script completed Wall time 0.0 seconds Output: {} ## Step 61 · agent · 2026-09-24T17:52:51.934Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:52:52.864Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:53:03.938Z Script running with cell ID 52 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:53:07.834Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:53:23.145Z Script completed Wall time 15.3 seconds Output: matrix (8456, 30000) 5514505 ## Step 62 · agent · 2026-09-24T17:53:27.646Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:28.634Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/svd_experiment.py\\n@@\\n- svd = TruncatedSVD(n_components=96, n_iter=5, random_state=20260924)\\n+ svd = TruncatedSVD(n_components=64, n_iter=3, random_state=20260924)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:53:28.778Z Script completed Wall time 0.1 seconds Output: {} ## Step 63 · agent · 2026-09-24T17:53:31.462Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:53:32.461Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);" } ### Tool output · 2026-09-24T17:53:43.549Z Script running with cell ID 54 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:53:45.859Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:54:02.747Z Script completed Wall time 16.9 seconds Output: matrix (8456, 30000) 5514505 ## Step 64 · agent · 2026-09-24T17:54:08.862Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:09.834Z { "code": "text(await tools.apply_patch(\"*** Begin Patch\\n*** Update File: /app/solution/svd_experiment.py\\n@@\\n- svd = TruncatedSVD(n_components=64, n_iter=3, random_state=20260924)\\n+ svd = TruncatedSVD(n_components=32, n_iter=2, random_state=20260924)\\n*** End Patch\"));" } ### Tool output · 2026-09-24T17:54:09.974Z Script completed Wall time 0.1 seconds Output: {} ### exec · 2026-09-24T17:54:12.934Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:54:14.249Z Script completed Wall time 1.3 seconds Output: SESSION_ID=83309 ### exec · 2026-09-24T17:54:18.968Z { "code": "const r = await tools.write_stdin({session_id:83309,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:54:29.065Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"0f30f6","wall_time_seconds":10.003116822,"session_id":83309,"original_token_count":8,"output":"matrix (8456, 30000) 5514505\n"} ### exec · 2026-09-24T17:54:31.476Z { "code": "const r = await tools.write_stdin({session_id:83309,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:54:41.655Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"0284fe","wall_time_seconds":10.000853741,"session_id":83309,"original_token_count":17,"output":"explained 0.13379985094070435\nscored 128\nscored 1152\nscored 2176\n"} ## Step 65 · agent · 2026-09-24T17:54:43.680Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:44.234Z { "code": "const r = await tools.write_stdin({session_id:83309,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:54:54.345Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"4d0134","wall_time_seconds":10.001696778,"session_id":83309,"original_token_count":12,"output":"scored 3200\nscored 4224\nscored 5248\nscored 6272\n"} ### exec · 2026-09-24T17:54:56.334Z { "code": "const r = await tools.write_stdin({session_id:83309,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:55:06.385Z Script completed Wall time 10.0 seconds Output: {"chunk_id":"0d7b0a","wall_time_seconds":10.000759533,"session_id":83309,"original_token_count":66,"output":"scored 7296\nscored 8320\n/app/solution/validation_svdtrend_b0p25.parquet\n/app/solution/validation_svdtrend_b0p45.parquet\n/app/solution/validation_svdtrend_b0p7.parquet\n/app/solution/validation_svdtrend_b1p0.parquet\n/app/solution/validation_svdtrend_b1p4.parquet\n"} ### exec · 2026-09-24T17:55:08.434Z { "code": "const r = await tools.write_stdin({session_id:83309,chars:\"\",yield_time_ms:5000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:55:09.039Z Script completed Wall time 0.6 seconds Output: {"chunk_id":"519bac","wall_time_seconds":0.55812964,"exit_code":0,"original_token_count":12,"output":"/app/solution/validation_svdtrend_b2p0.parquet\n"} ## Step 66 · agent · 2026-09-24T17:55:11.151Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:12.678Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_svdtrend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:55:13.937Z Script completed Wall time 1.3 seconds Output: SESSION_ID=64187 ### exec · 2026-09-24T17:55:15.834Z { "code": "const r = await tools.write_stdin({session_id:64187,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:55:25.873Z Script completed Wall time 10.0 seconds Output: {"chunk_id":"59e1d2","wall_time_seconds":10.000973746,"session_id":64187,"original_token_count":36,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b0p25.parquet\", \"scores\": { \"quality_ndcg@10\": 0.024559, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T17:55:28.534Z { "code": "const r = await tools.write_stdin({session_id:64187,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:55:38.585Z Script completed Wall time 10.0 seconds Output: {"chunk_id":"43f9c9","wall_time_seconds":10.000965158,"session_id":64187,"original_token_count":72,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b0p45.parquet\", \"scores\": { \"quality_ndcg@10\": 0.026104, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtrend_b0p7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028583, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T17:55:41.271Z { "code": "const r = await tools.write_stdin({session_id:64187,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:55:51.369Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"917c6e","wall_time_seconds":10.002651758,"session_id":64187,"original_token_count":36,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b1p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031615, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T17:55:54.134Z { "code": "const r = await tools.write_stdin({session_id:64187,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:56:03.577Z Script completed Wall time 9.4 seconds Output: {"chunk_id":"deab3e","wall_time_seconds":9.401830025,"exit_code":0,"original_token_count":72,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b1p4.parquet\", \"scores\": { \"quality_ndcg@10\": 0.034438, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtrend_b2p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.037945, \"quality_users\": 4263 } } \n"} ## Step 67 · agent · 2026-09-24T17:56:08.476Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:14.867Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/svd_experiment.py\\n@@\\n- svd = TruncatedSVD(n_components=32, n_iter=2, random_state=20260924)\\n- U = svd.fit_transform(Xw).astype(\\\"float32\\\")\\n- V = svd.components_.astype(\\\"float32\\\")\\n- print(\\\"explained\\\", float(svd.explained_variance_ratio_.sum()), flush=True)\\n+ factor_cache = OUT / \\\"train_svd32_factors.npz\\\"\\n+ if factor_cache.exists():\\n+ factors = np.load(factor_cache)\\n+ U, V = factors[\\\"U\\\"], factors[\\\"V\\\"]\\n+ print(\\\"loaded factors\\\", flush=True)\\n+ else:\\n+ svd = TruncatedSVD(n_components=32, n_iter=2, random_state=20260924)\\n+ U = svd.fit_transform(Xw).astype(\\\"float32\\\")\\n+ V = svd.components_.astype(\\\"float32\\\")\\n+ print(\\\"explained\\\", float(svd.explained_variance_ratio_.sum()), flush=True)\\n+ np.savez_compressed(factor_cache, U=U, V=V)\\n@@\\n- del X, Xw, svd\\n+ del X, Xw\\n@@\\n- betas = [0.25, 0.45, 0.70, 1.0, 1.4, 2.0]\\n+ betas = [2.5, 3.0, 4.0, 5.0, 7.0]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:56:14.957Z Script completed Wall time 0.1 seconds Output: {} ## Step 68 · agent · 2026-09-24T17:56:17.445Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:19.034Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:56:20.243Z Script completed Wall time 1.2 seconds Output: SESSION_ID=87536 ### exec · 2026-09-24T17:56:23.434Z { "code": "const r = await tools.write_stdin({session_id:87536,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:56:34.471Z Script running with cell ID 69 Wall time 11.0 seconds Output: ## Step 69 · agent · 2026-09-24T17:56:36.335Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:56:36.734Z { "cell_id": "69", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:56:38.473Z Script completed Wall time 1.7 seconds Output: {"chunk_id":"6536e5","wall_time_seconds":15.004430137,"session_id":87536,"original_token_count":24,"output":"matrix (8456, 30000) 5514505\nexplained 0.13379985094070435\nscored 128\nscored 1152\nscored 2176\n"} ## Step 70 · agent · 2026-09-24T17:56:43.574Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:56:43.641Z { "code": "const r = await tools.write_stdin({session_id:87536,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:56:54.679Z Script running with cell ID 70 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:56.350Z { "cell_id": "70", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:56:58.739Z Script completed Wall time 2.4 seconds Output: {"chunk_id":"bdcfd5","wall_time_seconds":15.000990053,"session_id":87536,"original_token_count":42,"output":"scored 3200\nscored 4224\nscored 5248\nscored 6272\nscored 7296\nscored 8320\n/app/solution/validation_svdtrend_b2p5.parquet\n/app/solution/validation_svdtrend_b3p0.parquet\n"} ### exec · 2026-09-24T17:57:01.559Z { "code": "const r = await tools.write_stdin({session_id:87536,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:57:03.364Z Script completed Wall time 1.8 seconds Output: {"chunk_id":"e36034","wall_time_seconds":1.714730498,"exit_code":0,"original_token_count":36,"output":"/app/solution/validation_svdtrend_b4p0.parquet\n/app/solution/validation_svdtrend_b5p0.parquet\n/app/solution/validation_svdtrend_b7p0.parquet\n"} ## Step 71 · agent · 2026-09-24T17:57:05.380Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:07.741Z { "code": "const r = await tools.exec_command({cmd:\"for b in 2p5 3p0 4p0 5p0 7p0; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_svdtrend_b${b}.parquet\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:57:08.944Z Script completed Wall time 1.2 seconds Output: SESSION_ID=40969 ### exec · 2026-09-24T17:57:11.934Z { "code": "const r = await tools.write_stdin({session_id:40969,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:57:22.976Z Script running with cell ID 73 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:57:25.934Z { "cell_id": "73", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:57:26.978Z Script completed Wall time 1.0 seconds Output: {"chunk_id":"9d1a2b","wall_time_seconds":15.000964671,"session_id":40969,"original_token_count":72,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b2p5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039155, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtrend_b3p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039622, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T17:57:29.667Z { "code": "const r = await tools.write_stdin({session_id:40969,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:57:40.755Z Script running with cell ID 74 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:57:42.769Z { "cell_id": "74", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:57:44.759Z Script completed Wall time 2.0 seconds Output: {"chunk_id":"0b80b0","wall_time_seconds":15.000962785,"session_id":40969,"original_token_count":72,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.040266, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtrend_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.040333, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T17:57:47.134Z { "code": "const r = await tools.write_stdin({session_id:40969,chars:\"\",yield_time_ms:10000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:57:50.142Z Script completed Wall time 3.0 seconds Output: {"chunk_id":"f8460a","wall_time_seconds":2.966621847,"exit_code":0,"original_token_count":36,"output":"{ \"model\": \"/app/solution/validation_svdtrend_b7p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.038599, \"quality_users\": 4263 } } \n"} ## Step 72 · agent · 2026-09-24T17:57:58.569Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:17.150Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/multisvd_experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Blend listen- and explicit-like collaborative factors with item momentum.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import gc\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+from sklearn.preprocessing import normalize\\n+\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+users=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy()\\n+uidx={int(v):i for i,v in enumerate(users)}; iidx={int(v):i for i,v in enumerate(items)}\\n+lf=np.load(O/'train_svd32_factors.npz'); Ul,Vl=lf['U'],lf['V']\\n+\\n+cache=O/'train_like_svd_factors.npz'\\n+if cache.exists():\\n+ q=np.load(cache); Ue,Ve=q['U'],q['V']\\n+else:\\n+ p=pd.read_parquet(D/'validation_preference_state.parquet'); p=p[p.state.eq('liked') & p.uid.isin(uidx) & p.item_id.isin(iidx)]\\n+ rr=p.uid.map(uidx).to_numpy(); cc=p.item_id.map(iidx).to_numpy(); vv=np.ones(len(p),dtype='float32')\\n+ X=sparse.csr_matrix((vv,(rr,cc)),shape=(len(users),len(items)),dtype='float32')\\n+ df=np.diff(X.tocsc().indptr).astype('float32'); idf=np.log1p(len(users)/(1+df))\\n+ X=normalize(X.multiply(idf**0.45).tocsr(),axis=1,copy=False)\\n+ svd=TruncatedSVD(n_components=64,n_iter=4,random_state=1847)\\n+ Ue=svd.fit_transform(X).astype('float32'); Ve=svd.components_.astype('float32')\\n+ print('like explained',svd.explained_variance_ratio_.sum(),flush=True);np.savez_compressed(cache,U=Ue,V=Ve)\\n+ del X,p,svd;gc.collect()\\n+\\n+t=np.load(O/'train_item_temporal.npz'); pop=np.log1p(t['l7'].astype('float32'))+.5*np.log1p(t['l30'].astype('float32'))+.35*np.log1p(t['ev7'].astype('float32'))+.2*np.log1p(cat.organic_positive_listens.to_numpy(dtype='float32'))\\n+pop=(pop-pop.mean())/pop.std()\\n+z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'].copy()\\n+combos=[(0.25,4.0),(0.5,4.0),(0.75,4.0),(1.0,4.0),(0.5,5.0),(0.75,5.0),(1.0,5.0),(1.5,5.0)]\\n+outs={c:[] for c in combos}\\n+for lo in range(0,len(users),128):\\n+ hi=min(lo+128,len(users)); sl=Ul[lo:hi]@Vl; se=Ue[lo:hi]@Ve\\n+ sl=(sl-sl.mean(1,keepdims=True))/(sl.std(1,keepdims=True)+1e-7)\\n+ se=(se-se.mean(1,keepdims=True))/(se.std(1,keepdims=True)+1e-7)\\n+ elig=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ for combo in combos:\\n+ ew,beta=combo; score=sl+ew*se+beta*pop[None,:];score[~elig]=-np.inf\\n+ top=np.argpartition(score,-100,axis=1)[:,-100:]; ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1)\\n+ for r,row in enumerate(top): outs[combo].extend((int(users[lo+r]),int(items[c]),rank) for rank,c in enumerate(row,1))\\n+ if lo%1024==0:print('scored',hi,flush=True)\\n+for (ew,beta),rows in outs.items():\\n+ path=O/f\\\"validation_multi_e{str(ew).replace('.','p')}_b{str(beta).replace('.','p')}.parquet\\\"\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False);print(path,flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T17:58:17.274Z Script completed Wall time 0.0 seconds Output: {} ## Step 73 · agent · 2026-09-24T17:58:19.134Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:21.034Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/multisvd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:58:22.239Z Script completed Wall time 1.2 seconds Output: SESSION_ID=86548 ### exec · 2026-09-24T17:58:25.034Z { "code": "const r = await tools.write_stdin({session_id:86548,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:58:36.072Z Script running with cell ID 78 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:37.834Z { "cell_id": "78", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:58:40.139Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"d4b889","wall_time_seconds":15.003243227,"session_id":86548,"original_token_count":10,"output":"like explained 0.07261053\nscored 128\n"} ### exec · 2026-09-24T17:58:44.334Z { "code": "const r = await tools.write_stdin({session_id:86548,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:58:55.372Z Script running with cell ID 79 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:57.234Z { "cell_id": "79", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:58:59.376Z Script completed Wall time 2.1 seconds Output: {"chunk_id":"df746b","wall_time_seconds":15.000423191,"session_id":86548,"original_token_count":12,"output":"scored 1152\nscored 2176\nscored 3200\nscored 4224\n"} ### exec · 2026-09-24T17:59:01.337Z { "code": "const r = await tools.write_stdin({session_id:86548,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:59:12.433Z Script running with cell ID 80 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:59:14.164Z { "cell_id": "80", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:59:16.437Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"208da7","wall_time_seconds":15.000788971,"session_id":86548,"original_token_count":9,"output":"scored 5248\nscored 6272\nscored 7296\n"} ### exec · 2026-09-24T17:59:18.959Z { "code": "const r = await tools.write_stdin({session_id:86548,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:59:30.050Z Script running with cell ID 81 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:59:32.179Z { "cell_id": "81", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:59:34.056Z Script completed Wall time 1.8 seconds Output: {"chunk_id":"7b3f2f","wall_time_seconds":15.001237815,"session_id":86548,"original_token_count":90,"output":"scored 8320\n/app/solution/validation_multi_e0p25_b4p0.parquet\n/app/solution/validation_multi_e0p5_b4p0.parquet\n/app/solution/validation_multi_e0p75_b4p0.parquet\n/app/solution/validation_multi_e1p0_b4p0.parquet\n/app/solution/validation_multi_e0p5_b5p0.parquet\n/app/solution/validation_multi_e0p75_b5p0.parquet\n/app/solution/validation_multi_e1p0_b5p0.parquet\n"} ### exec · 2026-09-24T17:59:36.268Z { "code": "const r = await tools.write_stdin({session_id:86548,chars:\"\",yield_time_ms:5000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:59:36.840Z Script completed Wall time 0.6 seconds Output: {"chunk_id":"37582b","wall_time_seconds":0.479321161,"exit_code":0,"original_token_count":13,"output":"/app/solution/validation_multi_e1p5_b5p0.parquet\n"} ## Step 74 · agent · 2026-09-24T17:59:38.083Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:59:40.053Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_multi_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T17:59:41.237Z Script completed Wall time 1.2 seconds Output: SESSION_ID=15293 ### exec · 2026-09-24T17:59:43.363Z { "code": "const r = await tools.write_stdin({session_id:15293,chars:\"\",yield_time_ms:20000,max_output_tokens:15000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T17:59:54.456Z Script running with cell ID 84 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:59:56.434Z { "cell_id": "84", "yield_time_ms": 20000, "max_tokens": 15000 } ### Tool output · 2026-09-24T18:00:03.467Z Script completed Wall time 7.0 seconds Output: {"chunk_id":"a8155f","wall_time_seconds":20.001321338,"session_id":15293,"original_token_count":73,"output":"{ \"model\": \"/app/solution/validation_multi_e0p25_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039713, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_multi_e0p5_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.037456, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T18:00:06.534Z { "code": "const r = await tools.write_stdin({session_id:15293,chars:\"\",yield_time_ms:20000,max_output_tokens:15000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:00:17.572Z Script running with cell ID 85 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:19.534Z { "cell_id": "85", "yield_time_ms": 20000, "max_tokens": 15000 } ### Tool output · 2026-09-24T18:00:26.638Z Script completed Wall time 7.1 seconds Output: {"chunk_id":"6f8518","wall_time_seconds":20.001549855,"session_id":15293,"original_token_count":110,"output":"{ \"model\": \"/app/solution/validation_multi_e0p5_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.038043, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_multi_e0p75_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.034517, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_multi_e0p75_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.036033, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T18:00:28.934Z { "code": "const r = await tools.write_stdin({session_id:15293,chars:\"\",yield_time_ms:20000,max_output_tokens:15000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:00:39.975Z Script running with cell ID 86 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:42.268Z { "cell_id": "86", "yield_time_ms": 20000, "max_tokens": 15000 } ### Tool output · 2026-09-24T18:00:47.840Z Script completed Wall time 5.6 seconds Output: {"chunk_id":"8792b3","wall_time_seconds":18.861264571,"exit_code":0,"original_token_count":109,"output":"{ \"model\": \"/app/solution/validation_multi_e1p0_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031665, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_multi_e1p0_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.033677, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_multi_e1p5_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029201, \"quality_users\": 4263 } } \n"} ## Step 75 · agent · 2026-09-24T18:01:03.334Z ### Thinking unavailable — no text recorded ## Step 76 · agent · 2026-09-24T18:01:17.737Z ### Thinking unavailable — no text recorded ## Step 77 · agent · 2026-09-24T18:01:29.880Z ### Thinking unavailable — no text recorded The hybrid peaks at 0.040333 with collaborative taste acting as a modest personalization term over strong temporal momentum; a separate explicit-like factor did not help. I’m using the remaining validation budget to tune the global momentum formula on held-out user splits, which should improve robustness without fitting validation outcomes into user features. ### exec · 2026-09-24T18:01:46.733Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_pop.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune global temporal-item weights with a fixed validation user split.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data');O=Path('/app/solution')\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy();icol={int(x):i for i,x in enumerate(items)}\\n+targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\n+z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'].copy();urow={int(x):i for i,x in enumerate(uids)};packed=z['packed_eligible'].copy()\\n+vl=pd.read_parquet(D/'val_listens.parquet');vl=vl[vl.uid.isin(targets)&vl.is_organic.eq(1)&vl.played_ratio_pct.ge(80)][['uid','item_id']].drop_duplicates()\\n+lk=pd.read_parquet(D/'val_likes.parquet');lk=lk[lk.uid.isin(targets)&lk.is_organic.eq(1)][['uid','item_id']].drop_duplicates()\\n+gold={}\\n+for u,i in vl.itertuples(index=False,name=None):\\n+ c=icol.get(int(i));r=urow.get(int(u));\\n+ if c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[c]=1\\n+for u,i in lk.itertuples(index=False,name=None):\\n+ c=icol.get(int(i));r=urow.get(int(u));\\n+ if c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[c]=3\\n+gu=np.array(sorted(gold),dtype='int64'); rr=np.array([urow[int(x)] for x in gu]); elig=np.unpackbits(packed[rr],axis=1,count=len(items),bitorder='little').astype(bool)\\n+grades=np.zeros((len(gu),len(items)),dtype='uint8')\\n+idcg=np.empty(len(gu),dtype='float32');disc=1/np.log2(np.arange(2,12))\\n+for r,u in enumerate(gu):\\n+ for c,v in gold[int(u)].items():grades[r,c]=v\\n+ ideal=sorted(gold[int(u)].values(),reverse=True)[:10];idcg[r]=np.sum((2**np.array(ideal)-1)*disc[:len(ideal)])\\n+split=(gu*2654435761%10)<7\\n+\\n+t=np.load(O/'train_item_temporal.npz')\\n+F=np.column_stack([np.log1p(t[x].astype('float32')) for x in ['l7','l30','l90','ev7','ev30','ev90']]+[np.log1p(cat.organic_positive_listens.to_numpy(dtype='float32'))])\\n+def metric(w,mask):\\n+ order=np.argsort(-(F@w))[:300]\\n+ ok=elig[:,order];take=ok&(np.cumsum(ok,axis=1)<=10)\\n+ pos=np.cumsum(ok,axis=1)-1\\n+ gain=(2.0**grades[:,order]-1)*take\\n+ dcg=np.sum(gain*disc[np.minimum(pos,9)],axis=1)\\n+ return float(np.mean((dcg/idcg)[mask]))\\n+\\n+rng=np.random.default_rng(48291); candidates=[]\\n+base=np.array([1,.5,0,.35,0,0,.2],dtype='float32');candidates.append(base)\\n+# Positive mixtures plus controlled subtraction of stale windows (momentum).\\n+for _ in range(500):\\n+ w=np.exp(rng.uniform(-3,1,size=7));w/=np.linalg.norm(w);candidates.append(w)\\n+for _ in range(500):\\n+ w=np.zeros(7);w[[0,1,3,4,6]]=np.exp(rng.uniform(-2,1,size=5));w[[2,5]]=-rng.uniform(0,.4,size=2);w/=np.linalg.norm(w);candidates.append(w)\\n+res=[]\\n+for j,w in enumerate(candidates):\\n+ tr=metric(w,split);te=metric(w,~split);res.append((tr,te,(tr+te)/2,w))\\n+ if j%100==0:print(j,max(x[2] for x in res),flush=True)\\n+res.sort(key=lambda x:x[2],reverse=True)\\n+for x in res[:20]:print(x[0],x[1],x[2],','.join(f'{v:.6g}' for v in x[3]))\\n+np.save(O/'best_pop_weights.npy',res[0][3])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T18:01:46.836Z Script completed Wall time 0.0 seconds Output: {} ## Step 78 · agent · 2026-09-24T18:01:48.789Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:50.289Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/tune_pop.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T18:01:51.537Z Script completed Wall time 1.2 seconds Output: SESSION_ID=56660 ### exec · 2026-09-24T18:01:54.234Z { "code": "const r = await tools.write_stdin({session_id:56660,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:05.333Z Script running with cell ID 89 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:07.434Z { "cell_id": "89", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:02:09.283Z Script completed Wall time 1.8 seconds Output: {"chunk_id":"8bae44","wall_time_seconds":15.000925818,"session_id":56660,"original_token_count":85,"output":"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\n return _methods._mean(a, axis=axis, dtype=dtype,\n0 nan\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\n ret = ret.dtype.type(ret / rcount)\n"} ## Step 79 · agent · 2026-09-24T18:02:16.167Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:16.843Z { "code": "const r = await tools.write_stdin({session_id:56660,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:17.170Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"debb41","wall_time_seconds":0.229247999,"exit_code":130,"original_token_count":92,"output":"100 nan\nTraceback (most recent call last):\n File \"/app/solution/tune_pop.py\", line 47, in \n tr=metric(w,split);te=metric(w,~split);res.append((tr,te,(tr+te)/2,w))\n ^^^^^^^^^^^^^^^\n File \"/app/solution/tune_pop.py\", line 35, in metric\n dcg=np.sum(gain*disc[np.minimum(pos,9)],axis=1)\n ^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"} ## Step 80 · agent · 2026-09-24T18:02:20.639Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:22.557Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_pop.py\\n@@\\n-split=(gu*2654435761%10)<7\\n+split_rng=np.random.default_rng(713)\\n+split=split_rng.random(len(gu))<0.7\\n@@\\n-for _ in range(500):\\n+for _ in range(150):\\n@@\\n-for _ in range(500):\\n+for _ in range(150):\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T18:02:22.680Z Script completed Wall time 0.0 seconds Output: {} ## Step 81 · agent · 2026-09-24T18:02:24.275Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:25.782Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/tune_pop.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T18:02:27.039Z Script completed Wall time 1.2 seconds Output: SESSION_ID=88753 ### exec · 2026-09-24T18:02:30.034Z { "code": "const r = await tools.write_stdin({session_id:88753,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:41.075Z Script running with cell ID 93 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:43.840Z { "cell_id": "93", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:02:45.136Z Script completed Wall time 1.3 seconds Output: {"chunk_id":"23635b","wall_time_seconds":15.00093668,"session_id":88753,"original_token_count":12,"output":"0 0.03202170120816564\n100 0.032837277005641576\n"} ### exec · 2026-09-24T18:02:48.534Z { "code": "const r = await tools.write_stdin({session_id:88753,chars:\"\",yield_time_ms:20000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:02:59.579Z Script running with cell ID 94 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:03:02.434Z { "cell_id": "94", "yield_time_ms": 20000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:03:08.637Z Script completed Wall time 6.2 seconds Output: {"chunk_id":"ce2a73","wall_time_seconds":20.001505692,"session_id":88753,"original_token_count":7,"output":"200 0.032885550737597316\n"} ### exec · 2026-09-24T18:03:12.734Z { "code": "const r = await tools.write_stdin({session_id:88753,chars:\"\",yield_time_ms:20000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:03:14.978Z Script completed Wall time 2.2 seconds Output: {"chunk_id":"a24a49","wall_time_seconds":2.204341452,"exit_code":0,"original_token_count":627,"output":"300 0.032885550737597316\n0.03190733653237263 0.03386376494282201 0.032885550737597316 0.815826,0.228818,-0.0055682,0.362672,0.202894,-0.12877,0.304568\n0.0314498081652853 0.034296665796334294 0.032873236980809795 0.845576,0.2477,-0.211682,0.1509,0.186402,-0.108935,0.330837\n0.03195240442595675 0.0337221495853264 0.032837277005641576 0.91201,0.118919,0.0273643,0.291259,0.0556966,0.0267199,0.254359\n0.03174607568577594 0.033825818027028005 0.032785946856401973 0.87824,0.138954,-0.0376281,0.282019,0.164054,-0.0884229,0.306112\n0.03141355934448806 0.03399296373586106 0.032703261540174555 0.857624,0.312449,-0.13231,0.200235,0.208841,-0.0662041,0.247506\n0.031882963092387646 0.033423238455712004 0.03265310077404983 0.525013,0.217851,-0.164905,0.657003,0.369075,-0.13587,0.251752\n0.032012284859634484 0.033116734618799715 0.032564509739217096 0.653033,0.216855,-0.289724,0.168024,0.627471,-0.0450706,0.136377\n0.030899471657008022 0.03413211933413589 0.03251579549557196 0.871323,0.201795,-0.080042,0.390432,0.11774,-0.00131072,0.165425\n0.03155511129437372 0.03346409750788459 0.032509604401129155 0.860786,0.30102,-0.011074,0.163188,0.367289,-0.00979844,0.0817545\n0.031532342529980634 0.033260730243260844 0.03239653638662074 0.768182,0.440784,0.0484991,0.418417,0.177172,0.0631324,0.0529658\n0.031189617376326507 0.03312597282810322 0.032157795102214864 0.524561,0.359612,-0.449642,0.288431,0.486518,-0.131545,0.23694\n0.031102367407686383 0.03319840349116245 0.03215038544942442 0.629093,0.174039,-0.277182,0.419055,0.533018,-0.115112,0.155423\n0.03155371513560563 0.03271927034572726 0.03213649274066645 0.940124,0.165456,0.252037,0.0819983,0.103376,0.0732823,0.0498772\n0.031178333860616754 0.0329489915498373 0.03206366270522702 0.128291,0.748895,-0.148945,0.616636,0.0949334,-0.0658845,0.0831842\n0.03146552927132134 0.03264000695559904 0.03205276811346019 0.872765,0.134593,-0.126487,0.0849244,0.147631,-0.0942578,0.407769\n0.031020254582349593 0.03308448441829566 0.03205236950032263 0.291564,0.482206,-0.45781,0.349085,0.361796,-0.269901,0.383765\n0.030939006724356732 0.03312903765389609 0.03203402218912641 0.497645,0.684551,-0.147532,0.378903,0.221745,-0.189662,0.18238\n0.03075415543963298 0.033289246976698286 0.03202170120816564 1,0.5,0,0.35,0,0,0.2\n0.031438465571078315 0.03256925047808721 0.03200385802458276 0.816061,0.126959,-0.139361,0.123855,0.340989,-0.173148,0.370014\n0.03156919007574731 0.032345576354067165 0.03195738321490724 0.766891,0.122708,-0.173442,0.201183,0.211065,-0.206514,0.488945\n"} ## Step 82 · agent · 2026-09-24T18:03:18.634Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:25.234Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/svd_experiment.py\\n@@\\n- ev7 = np.log1p(temporal[\\\"ev7\\\"].astype(np.float32))\\n- l7 = np.log1p(temporal[\\\"l7\\\"].astype(np.float32))\\n- l30 = np.log1p(temporal[\\\"l30\\\"].astype(np.float32))\\n- total = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\\n- pop = 1.0*l7 + 0.5*l30 + 0.35*ev7 + 0.2*total\\n+ feature_names = [\\\"l7\\\", \\\"l30\\\", \\\"l90\\\", \\\"ev7\\\", \\\"ev30\\\", \\\"ev90\\\"]\\n+ pop_features = np.column_stack([np.log1p(temporal[n].astype(np.float32)) for n in feature_names] + [np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))])\\n+ pop = pop_features @ np.load(OUT / \\\"best_pop_weights.npy\\\")\\n@@\\n- betas = [2.5, 3.0, 4.0, 5.0, 7.0]\\n+ betas = [3.0, 4.0, 5.0, 6.0, 7.0]\\n@@\\n- path = OUT / f\\\"validation_svdtrend_b{tag}.parquet\\\"\\n+ path = OUT / f\\\"validation_svdtuned_b{tag}.parquet\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T18:03:25.339Z Script completed Wall time 0.0 seconds Output: {} ## Step 83 · agent · 2026-09-24T18:03:32.389Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:32.435Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/svd_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T18:03:33.642Z Script completed Wall time 1.2 seconds Output: SESSION_ID=10482 ### exec · 2026-09-24T18:03:37.137Z { "code": "const r = await tools.write_stdin({session_id:10482,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:03:48.243Z Script running with cell ID 98 Wall time 11.1 seconds Output: ### wait · 2026-09-24T18:03:50.334Z { "cell_id": "98", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:03:52.264Z Script completed Wall time 1.9 seconds Output: {"chunk_id":"6c1fc9","wall_time_seconds":15.000814662,"session_id":10482,"original_token_count":26,"output":"matrix (8456, 30000) 5514505\nloaded factors\nscored 128\nscored 1152\nscored 2176\nscored 3200\nscored 4224\n"} ### exec · 2026-09-24T18:03:54.937Z { "code": "const r = await tools.write_stdin({session_id:10482,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:05.980Z Script running with cell ID 99 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:04:10.874Z { "cell_id": "99", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:04:10.963Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"a0a4bb","wall_time_seconds":15.0009402,"session_id":10482,"original_token_count":71,"output":"scored 5248\nscored 6272\nscored 7296\nscored 8320\n/app/solution/validation_svdtuned_b3p0.parquet\n/app/solution/validation_svdtuned_b4p0.parquet\n/app/solution/validation_svdtuned_b5p0.parquet\n/app/solution/validation_svdtuned_b6p0.parquet\n/app/solution/validation_svdtuned_b7p0.parquet\n"} ## Step 84 · agent · 2026-09-24T18:04:13.193Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:04:13.934Z { "code": "const r = await tools.write_stdin({session_id:10482,chars:\"\",yield_time_ms:1000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:14.138Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"41524b","wall_time_seconds":0.00000131,"exit_code":0,"original_token_count":0,"output":""} ## Step 85 · agent · 2026-09-24T18:04:15.850Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:04:17.841Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_svdtuned_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T18:04:19.043Z Script completed Wall time 1.2 seconds Output: SESSION_ID=95160 ### exec · 2026-09-24T18:04:21.034Z { "code": "const r = await tools.write_stdin({session_id:95160,chars:\"\",yield_time_ms:20000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:32.071Z Script running with cell ID 102 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:04:34.234Z { "cell_id": "102", "yield_time_ms": 20000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:04:41.076Z Script completed Wall time 6.8 seconds Output: {"chunk_id":"59120d","wall_time_seconds":20.001330848,"session_id":95160,"original_token_count":72,"output":"{ \"model\": \"/app/solution/validation_svdtuned_b3p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.038726, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtuned_b4p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039184, \"quality_users\": 4263 } } \n"} ### exec · 2026-09-24T18:04:43.247Z { "code": "const r = await tools.write_stdin({session_id:95160,chars:\"\",yield_time_ms:20000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:04:54.336Z Script running with cell ID 103 Wall time 11.1 seconds Output: ### wait · 2026-09-24T18:04:56.634Z { "cell_id": "103", "yield_time_ms": 20000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:05:01.066Z Script completed Wall time 4.4 seconds Output: {"chunk_id":"81ce9d","wall_time_seconds":17.779973096,"exit_code":0,"original_token_count":108,"output":"{ \"model\": \"/app/solution/validation_svdtuned_b5p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039889, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtuned_b6p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039651, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_svdtuned_b7p0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.039074, \"quality_users\": 4263 } } \n"} ## Step 86 · agent · 2026-09-24T18:05:15.438Z ### Thinking unavailable — no text recorded ## Step 87 · agent · 2026-09-24T18:05:29.934Z ### Thinking unavailable — no text recorded ## Step 88 · agent · 2026-09-24T18:05:34.475Z ### Thinking unavailable — no text recorded The robust choice remains the simpler temporal blend (public NDCG@10 0.040333); the more aggressively tuned formula slipped slightly, so I’m rejecting it. I’m now refitting that selected model on the full observed history (`train + val`), recomputing seven-day momentum at the final cutoff, rebuilding user factors with validation interactions included, and writing the test-eligible artifact. ### exec · 2026-09-24T18:06:02.934Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build the final Made-for-You ranking from all cutoff-observed history.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import gc\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+from sklearn.preprocessing import normalize\\n+\\n+DATA=Path('/task/data'); OUT=Path('/app/solution'); DAY=86_400; CUTOFF=25_393_400\\n+\\n+def main():\\n+ users=pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(); cat=pd.read_parquet(DATA/'candidate_catalog.parquet');items=cat.item_id.to_numpy()\\n+ uidx={int(x):i for i,x in enumerate(users)};iidx={int(x):i for i,x in enumerate(items)};uset=set(users);iset=set(items)\\n+ # The training aggregate was built cutoff-correctly by experiment.py. Build\\n+ # it on demand so this entry point remains reproducible from raw inputs.\\n+ cache=OUT/'train_pair_features.parquet'\\n+ if not cache.exists():\\n+ from experiment import aggregate_history\\n+ aggregate_history()\\n+ g=pd.read_parquet(cache,columns=['uid','item_id','cnt','org','sorg'])\\n+ v=pd.read_parquet(DATA/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+ v=v[v.uid.isin(uset)&v.item_id.isin(iset)&v.played_ratio_pct.ge(50)].copy()\\n+ v['cnt']=1;v['org']=v.is_organic.astype('uint8');v['sorg']=(v.is_organic.eq(1)&v.played_ratio_pct.ge(80)).astype('uint8')\\n+ v=v.groupby(['uid','item_id'],sort=False)[['cnt','org','sorg']].sum().reset_index()\\n+ g=pd.concat([g,v],ignore_index=True).groupby(['uid','item_id'],sort=False)[['cnt','org','sorg']].sum().reset_index();del v;gc.collect()\\n+ pref=pd.read_parquet(DATA/'test_preference_state.parquet');liked=pref[pref.state.eq('liked')][['uid','item_id']].drop_duplicates().assign(liked=np.uint8(1))\\n+ g=g.merge(liked,how='left');g['liked']=g.liked.fillna(0).astype('uint8')\\n+ rr=g.uid.map(uidx).to_numpy();cc=g.item_id.map(iidx).to_numpy()\\n+ vv=(1+.45*np.log1p(g.cnt)+.45*np.log1p(g.org)+.35*np.log1p(g.sorg)+1.25*g.liked).to_numpy(dtype='float32')\\n+ X=sparse.csr_matrix((vv,(rr,cc)),shape=(len(users),len(items)),dtype='float32');del g,rr,cc,vv,pref,liked;gc.collect()\\n+ df=np.diff(X.tocsc().indptr).astype('float32');idf=np.log1p(len(users)/(1+df)).astype('float32')\\n+ X=normalize(X.multiply(idf**.55).tocsr(),axis=1,copy=False)\\n+ svd=TruncatedSVD(n_components=32,n_iter=2,random_state=20260924);U=svd.fit_transform(X).astype('float32');V=svd.components_.astype('float32')\\n+ print('refit factors',float(svd.explained_variance_ratio_.sum()),flush=True);del X,svd;gc.collect()\\n+\\n+ # Cutoff-correct item momentum. The chosen public formula emphasizes\\n+ # organic likes in the immediately preceding week and month.\\n+ sorter=np.argsort(items);sorted_items=items[sorter]\\n+ def cols(raw):\\n+ pos=np.searchsorted(sorted_items,raw);good=pos=CUTOFF-7*DAY)&(org==1)&(ratio>=80)\\n+ if m.any():\\n+ c,ok=cols(raw[m]);ev7+=np.bincount(c,minlength=len(items))\\n+ for fn in ['train_likes.parquet','val_likes.parquet']:\\n+ for b in pq.ParquetFile(DATA/fn).iter_batches(batch_size=500_000,columns=['timestamp','item_id','is_organic']):\\n+ t=b.column(0).to_numpy();raw=b.column(1).to_numpy();org=b.column(2).to_numpy();m=(t>=CUTOFF-30*DAY)&(org==1)\\n+ if m.any():\\n+ c,ok=cols(raw[m]);tt=t[m][ok];l30+=np.bincount(c,minlength=len(items));l7+=np.bincount(c[tt>=CUTOFF-7*DAY],minlength=len(items))\\n+ pop=np.log1p(l7.astype('float32'))+.5*np.log1p(l30.astype('float32'))+.35*np.log1p(ev7.astype('float32'))+.2*np.log1p(cat.organic_positive_listens.to_numpy(dtype='float32'))\\n+ pop=(pop-pop.mean())/(pop.std()+1e-7)\\n+\\n+ z=np.load(DATA/'test_eligible_candidates.npz');assert np.array_equal(z['uids'],users) and np.array_equal(z['item_ids'],items);packed=z['packed_eligible'].copy();out=[]\\n+ for lo in range(0,len(users),128):\\n+ hi=min(lo+128,len(users));score=U[lo:hi]@V;score=(score-score.mean(1,keepdims=True))/(score.std(1,keepdims=True)+1e-7);score+=5.0*pop[None,:]\\n+ elig=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool);score[~elig]=-np.inf\\n+ top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1)\\n+ for r,row in enumerate(top):out.extend((int(users[lo+r]),int(items[c]),rank) for rank,c in enumerate(row,1))\\n+ if lo%1024==0:print('ranked',hi,flush=True)\\n+ sub=pd.DataFrame(out,columns=['uid','item_id','rank']);path=OUT/'submission.parquet';sub.to_parquet(path,index=False)\\n+ # Local structural and eligibility assertions mirror the verifier contract.\\n+ assert len(sub)==len(users)*100 and not sub.duplicated(['uid','item_id']).any()\\n+ assert sub.groupby('uid')['rank'].agg(lambda x:set(x)==set(range(1,101))).all()\\n+ print(path,flush=True)\\n+\\n+if __name__=='__main__':main()\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+`build_submission.py` builds `submission.parquet` from the supplied data using\\n+only information observed at the final cutoff. It refits a confidence-weighted\\n+implicit collaborative SVD on `train + val` positive playbacks, enriches user\\n+profiles with cutoff-specific active likes, and blends the resulting unseen-item\\n+affinity with recent organic like/listen momentum. Every score is masked by the\\n+per-user test eligibility set before ranking.\\n+\\n+The selected blend scored **0.040333 NDCG@10** on the public temporal window.\\n+The popularity-only baseline scored 0.010710. Explicit-like-only factors and a\\n+more aggressively validation-tuned trend formula were tested and rejected.\\n+\\n+Run with the task environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_submission.py\\n+```\\n+\\n+The script asserts 100 unique ranks per target user and emits only candidates\\n+from `test_eligible_candidates.npz`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-09-24T18:06:03.039Z Script completed Wall time 0.1 seconds Output: {} ## Step 89 · agent · 2026-09-24T18:06:05.534Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:06:06.850Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\nif (r.session_id) { text(`SESSION_ID=${r.session_id}\\n${r.output}`); } else { text(r.output); }" } ### Tool output · 2026-09-24T18:06:08.044Z Script completed Wall time 1.2 seconds Output: SESSION_ID=15870 ### exec · 2026-09-24T18:06:10.834Z { "code": "const r = await tools.write_stdin({session_id:15870,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:06:21.873Z Script running with cell ID 106 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:06:23.834Z { "cell_id": "106", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:06:25.879Z Script completed Wall time 2.0 seconds Output: {"chunk_id":"bd881f","wall_time_seconds":15.001411544,"session_id":15870,"original_token_count":0,"output":""} ### exec · 2026-09-24T18:06:28.137Z { "code": "const r = await tools.write_stdin({session_id:15870,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:06:39.169Z Script running with cell ID 107 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:06:41.334Z { "cell_id": "107", "yield_time_ms": 15000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:06:43.236Z Script completed Wall time 1.9 seconds Output: {"chunk_id":"b161c9","wall_time_seconds":15.004871456,"session_id":15870,"original_token_count":33,"output":"refit factors 0.13587729632854462\nranked 128\nranked 1152\nranked 2176\nranked 3200\nranked 4224\nranked 5248\nranked 6272\nranked 7296\n"} ### exec · 2026-09-24T18:06:46.138Z { "code": "const r = await tools.write_stdin({session_id:15870,chars:\"\",yield_time_ms:15000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-09-24T18:06:47.784Z Script completed Wall time 1.6 seconds Output: {"chunk_id":"d3fe43","wall_time_seconds":1.6047277,"exit_code":0,"original_token_count":12,"output":"ranked 8320\n/app/solution/submission.parquet\n"} ## Step 90 · agent · 2026-09-24T18:06:54.434Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:07:00.434Z { "code": "const p = await tools.update_plan({explanation:\"Selected the strongest public model and completed the train+validation refit; final artifact is now generated. Running an independent contract check before handoff.\",plan:[\n {step:\"Inspect datasets, eligibility layout, and available libraries\",status:\"completed\"},\n {step:\"Build a cutoff-correct baseline and save valid submission\",status:\"completed\"},\n {step:\"Engineer and tune ranking models on public validation\",status:\"completed\"},\n {step:\"Refit selected model on train plus validation history\",status:\"completed\"},\n {step:\"Generate and validate final submission and documentation\",status:\"in_progress\"}\n]});\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys, pandas as pd, numpy as np\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD='/task/data';p='/app/solution/submission.parquet'\\nt=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); x=validate_ranking(pd.read_parquet(p),t); c=CandidateSet(__import__('pathlib').Path(D+'/test_eligible_candidates.npz'))\\nbad=sum(not c.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'duplicate_pairs':int(x.duplicated(['uid','item_id']).sum()),'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/build_submission.py /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);" } ### Tool output · 2026-09-24T18:07:06.684Z Script completed Wall time 6.2 seconds Output: {'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100} -rw-r--r-- 1 agent agent 921 Sep 24 18:06 /app/solution/README.md -rw-r--r-- 1 agent agent 5.3K Sep 24 18:06 /app/solution/build_submission.py -rw-r--r-- 1 agent agent 1.3M Sep 24 18:06 /app/solution/submission.parquet ## Step 91 · agent · 2026-09-24T18:07:13.263Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:07:15.282Z { "code": "const r = await tools.update_plan({explanation:\"Independent validation confirms the final artifact has the exact schema, complete user coverage, ranks 1–100, unique items, and zero test-eligibility violations.\",plan:[\n {step:\"Inspect datasets, eligibility layout, and available libraries\",status:\"completed\"},\n {step:\"Build a cutoff-correct baseline and save valid submission\",status:\"completed\"},\n {step:\"Engineer and tune ranking models on public validation\",status:\"completed\"},\n {step:\"Refit selected model on train plus validation history\",status:\"completed\"},\n {step:\"Generate and validate final submission and documentation\",status:\"completed\"}\n]});\ntext(r);" } ### Tool output · 2026-09-24T18:07:15.439Z Script completed Wall time 0.1 seconds Output: {} ## Step 92 · agent · 2026-09-24T18:07:20.366Z Built and validated the final music-discovery ranker. - Final artifact: [submission.parquet](/app/solution/submission.parquet) - Rebuild script: [build_submission.py](/app/solution/build_submission.py) - Documentation: [README.md](/app/solution/README.md) - Public validation NDCG@10: `0.040333` - Rows: `845,600` - Users: `8,456` - Eligibility violations: `0` - Duplicate user-item pairs: `0` - Exact ranks `1–100` for every user.