yambda-discovery-ranking-v01 / sol-high-0.147.0-exact-r01-2026-09-24 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-24T17:36:16.294Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-24T17:36:17.958Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-24T17:36:17.958Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-24T17:36:17.958Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-24T17:36:17.958Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-24 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-24", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d47d-4b9e-7130-8545-98ca0046b73f", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-24T17:36:17.967Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-24T17:36:21.274Z ### Thinking unavailable — no text recorded I’ll build a leakage-safe validation pipeline, benchmark several ranking signals against the public evaluator, then refit the best blend on train+validation and save a continuously valid top-100 test artifact. I’ll keep all writes confined to `/app/solution/`. ### exec · 2026-09-24T17:36:24.515Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data shapes, sparsity, time ranges, and available libraries\",status:\"in_progress\"},\n {step:\"Build a valid baseline and validation labels\",status:\"pending\"},\n {step:\"Engineer and tune collaborative, popularity, and behavioral rankers\",status:\"pending\"},\n {step:\"Refit selected model on train+validation and write final submission\",status:\"pending\"},\n {step:\"Validate artifact and document reproducible build\",status:\"pending\"}\n]}); text(p)\n" } ### Tool output · 2026-09-24T17:36:24.539Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:36:27.329Z { "code": "const r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:27.403Z 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/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/candidate_catalog.parquet /task/data/val_likes.parquet /task/data/validation_eligible_candidates.npz /task/data/val_listens.parquet /task/data/test_eligible_candidates.npz # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. Python itself # is pinned by task.toml and both Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 idna==3.18 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-09-24T17:36:29.724Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:35.127Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/inspect_data.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path('/task/data')\\n+for p in sorted(DATA.glob('*.parquet')):\\n+ df = pd.read_parquet(p)\\n+ print(p.name, df.shape, df.dtypes.to_dict())\\n+ print(df.head(2).to_dict('records'))\\n+ if 'timestamp' in df:\\n+ print('time', df.timestamp.min(), df.timestamp.max(), 'users', df.uid.nunique(), 'items', df.item_id.nunique())\\n+for p in sorted(DATA.glob('*.npz')):\\n+ z=np.load(p)\\n+ print(p.name, {k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\\n+ packed=z['packed_eligible']; n=len(z['item_ids'])\\n+ cnt=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\\n+ print('eligible count',np.quantile(cnt,[0,.01,.5,.99,1]),'aligned users head',z['uids'][:5])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:36:35.180Z Script completed Wall time 0.0 seconds Output: {} ## Step 9 · agent · 2026-09-24T17:36:37.572Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:38.363Z { "code": "const r = await tools.exec_command({cmd:\"python solution/inspect_data.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:38.495Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "/app/solution/inspect_data.py", line 2, in import numpy as np ModuleNotFoundError: No module named 'numpy' ## Step 10 · agent · 2026-09-24T17:36:40.728Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:42.181Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; ls -la /task; find / -maxdepth 3 -type f -name 'python*' 2>/dev/null | sed -n '1,80p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:42.246Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 total 36 dr-xr-xr-x 1 root root 4096 Sep 24 17:33 . drwxr-xr-x 1 root root 4096 Sep 24 17:36 .. dr-xr-xr-x 1 root root 4096 Sep 24 17:29 data -r--r--r-- 1 root root 5774 Sep 24 17:28 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 24 17:28 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 24 17:28 requirements.txt /usr/bin/python3.11 ## Step 11 · agent · 2026-09-24T17:36:43.823Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:45.632Z { "code": "const r = await tools.exec_command({cmd:\"env | sort; find /opt /venv /app -maxdepth 4 -type f -name python 2>/dev/null | sed -n '1,100p'; /usr/bin/python3 -c \\\"import numpy,pandas; print(numpy.__version__,pandas.__version__)\\\"\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:15000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:45.690Z Script completed Wall time 0.1 seconds Output: CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_MANAGED_BY_NPM=1 CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex CODEX_THREAD_ID=01a0d47d-4b8d-7400-a8de-f1e6dd38d845 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=18648d048817 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin NVM_CD_FLAGS= NVM_DIR=/home/agent/.nvm NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node PAGER=cat PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/home/agent/.nvm/versions/node/v22.23.3/bin/codex Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'numpy' ## Step 12 · agent · 2026-09-24T17:36:47.361Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:49.275Z { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name 'numpy' -o -name 'numpy-*dist-info' -o -name 'site-packages' \\\\) 2>/dev/null | sed -n '1,160p'; find / -type f -name uv 2>/dev/null | sed -n '1,80p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:12000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:50.068Z Script completed Wall time 0.8 seconds Output: /usr/local/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/numpy /opt/task-venv/lib/python3.12/site-packages/numpy /opt/task-venv/lib/python3.12/site-packages/numpy/_core/include/numpy /opt/task-venv/lib/python3.12/site-packages/numpy-2.2.6.dist-info /opt/task-venv/lib/python3.12/site-packages/pandas/compat/numpy /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy ## Step 13 · agent · 2026-09-24T17:36:51.634Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:36:52.879Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python solution/inspect_data.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:36:55.665Z Script completed Wall time 2.8 seconds Output: total 112 dr-xr-xr-x 1 root root 4096 Sep 24 17:33 . dr-xr-xr-x 1 root root 4096 Sep 24 17:33 .. -r--r--r-- 1 root root 9033 Sep 24 17:32 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 24 17:32 activate -r--r--r-- 1 root root 920 Sep 24 17:32 activate.csh -r--r--r-- 1 root root 2195 Sep 24 17:32 activate.fish -r-xr-xr-x 1 root root 219 Sep 24 17:32 chardetect -r-xr-xr-x 1 root root 221 Sep 24 17:32 dotenv -r-xr-xr-x 1 root root 225 Sep 24 17:32 f2py -r-xr-xr-x 1 root root 216 Sep 24 17:32 idna -r-xr-xr-x 1 root root 222 Sep 24 17:33 jsonschema -r-xr-xr-x 1 root root 228 Sep 24 17:32 jupyter -r-xr-xr-x 1 root root 228 Sep 24 17:32 jupyter-migrate -r-xr-xr-x 1 root root 233 Sep 24 17:32 jupyter-troubleshoot -r-xr-xr-x 1 root root 261 Sep 24 17:33 jupyter-trust -r-xr-xr-x 1 root root 228 Sep 24 17:33 jupytext -r-xr-xr-x 1 root root 232 Sep 24 17:33 jupytext-config -r-xr-xr-x 1 root root 218 Sep 24 17:33 kaggle -r-xr-xr-x 1 root root 229 Sep 24 17:32 markdown-it -r-xr-xr-x 1 root root 242 Sep 24 17:32 normalizer -r-xr-xr-x 1 root root 225 Sep 24 17:32 numpy-config -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip3 -r-xr-xr-x 1 root root 230 Sep 24 17:32 pip3.12 lrwxrwxrwx 1 root root 21 Sep 24 17:32 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 24 17:32 python3 -> python lrwxrwxrwx 1 root root 6 Sep 24 17:32 python3.12 -> python -r-xr-xr-x 1 root root 224 Sep 24 17:32 slugify -r-xr-xr-x 1 root root 216 Sep 24 17:32 tqdm 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}] 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}] 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'}] 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}] time 75 24786580 users 5732 items 50876 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}] time 80 24786790 users 8144 items 172303 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}] time 0 24786795 users 9238 items 845395 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}] time 15950 24785395 users 2769 items 14725 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}] time 250 24786570 users 6197 items 113072 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}] time 24789920 25393280 users 955 items 3135 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}] time 24788815 25393390 users 4114 items 22902 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}] time 24788600 25393395 users 8196 items 191747 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}] time 24810715 25393035 users 205 items 579 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}] time 24789005 25393385 users 1313 items 8080 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'}] test_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} eligible count [24552. 26672.7 29220. 29975. 30000. ] aligned users head [100 200 300 600 700] validation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} eligible count [24657. 26724.2 29242. 29979. 30000. ] aligned users head [100 200 300 600 700] ## Step 14 · agent · 2026-09-24T17:37:05.782Z ### Thinking unavailable — no text recorded The dataset is large but unusually favorable for personalized modeling: 43.5M playback events, only 8,456 target users, and a fixed 30k catalog. I’m now measuring how much validation relevance is repeat-listening versus genuinely new discovery, since that determines whether the strongest backbone should be personal history, collaborative filtering, or a mixture. ### exec · 2026-09-24T17:37:07.617Z { "code": "const r = await tools.exec_command({cmd:\"free -h; sed -n '1,260p' /task/evaluate_public.py; sed -n '1,300p' /task/ranking_evaluation.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:37:07.663Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: free: not found #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } ## Step 15 · agent · 2026-09-24T17:37:09.215Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:10.411Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,20p' /proc/meminfo; du -h /task/data/*.parquet | sort -h\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:37:10.458Z Script completed Wall time 0.0 seconds Output: MemTotal: 15988968 kB MemFree: 4338860 kB MemAvailable: 14838540 kB Buffers: 228868 kB Cached: 10010016 kB SwapCached: 0 kB Active: 540452 kB Inactive: 10011000 kB Active(anon): 339092 kB Inactive(anon): 0 kB Active(file): 201360 kB Inactive(file): 10011000 kB Unevictable: 43872 kB Mlocked: 27240 kB SwapTotal: 0 kB SwapFree: 0 kB Zswap: 0 kB Zswapped: 0 kB Dirty: 8580 kB Writeback: 0 kB 8.0K /task/data/val_undislikes.parquet 36K /task/data/val_dislikes.parquet 72K /task/data/val_unlikes.parquet 84K /task/data/target_users.parquet 156K /task/data/train_undislikes.parquet 292K /task/data/val_likes.parquet 344K /task/data/candidate_catalog.parquet 840K /task/data/train_dislikes.parquet 1.1M /task/data/test_preference_state.parquet 1.1M /task/data/validation_preference_state.parquet 1.8M /task/data/train_unlikes.parquet 5.2M /task/data/train_likes.parquet 9.5M /task/data/val_listens.parquet 305M /task/data/train_listens.parquet ## Step 16 · agent · 2026-09-24T17:37:20.713Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:37:35.764Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/analyze.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data')\\n+targets=pd.read_parquet(D/'target_users.parquet').uid\\n+tset=set(targets.astype(int))\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+cset=set(cat.item_id.astype(int))\\n+z=np.load(D/'validation_eligible_candidates.npz')\\n+uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\n+urow={int(x):i for i,x in enumerate(uids)}; icol={int(x):i for i,x in enumerate(items)}\\n+\\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+strong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)][['uid','item_id']].drop_duplicates()\\n+likes=vk[(vk.is_organic==1)&vk.uid.isin(tset)][['uid','item_id']].drop_duplicates()\\n+truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\n+truth=truth[truth.item_id.isin(cset)]\\n+truth=truth[[bits[urow[int(u)],icol[int(i)]] for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\\n+print('truth',len(truth),'users',truth.uid.nunique(),'grades',truth.grade.value_counts().to_dict(),'peruser',truth.groupby('uid').size().describe().to_dict())\\n+\\n+tl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+tl=tl[tl.uid.isin(tset)&tl.item_id.isin(cset)]\\n+print('target catalog listens',len(tl),'unique pairs',tl[['uid','item_id']].drop_duplicates().shape)\\n+agg=tl.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),strong=('played_ratio_pct',lambda x:(x>=80).sum()),maxratio=('played_ratio_pct','max')).reset_index()\\n+m=truth.merge(agg,on=['uid','item_id'],how='left')\\n+state=pd.read_parquet(D/'validation_preference_state.parquet')\\n+wide=state.assign(v=1).pivot_table(index=['uid','item_id'],columns='state',values='v',aggfunc='max',fill_value=0).reset_index()\\n+m=m.merge(wide,on=['uid','item_id'],how='left')\\n+print('truth history coverage', {c:float(m[c].notna().mean()) for c in ['n','liked','disliked']})\\n+for g, x in m.groupby('grade'):\\n+ print('grade',g,'n',len(x),'pastpair',x.n.notna().mean(),'liked',x.get('liked',pd.Series()).fillna(0).mean(),'stats',x[['n','last','org','strong']].describe().to_dict())\\n+print('truth poprank',m.merge(cat,on='item_id').groupby('grade').popularity_rank.describe())\\n+\\n+# Candidate exclusions vs history\\n+histpairs=agg[['uid','item_id']].drop_duplicates()\\n+histpairs['eligible']=[bits[urow[int(u)],icol[int(i)]] for u,i in histpairs.itertuples(index=False,name=None)]\\n+print('historical candidate eligibility',histpairs.eligible.mean(),histpairs.groupby('uid').eligible.mean().describe())\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:37:35.798Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:37:38.283Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/analyze.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:37:49.297Z Script running with cell ID 12 Wall time 11.0 seconds Output: ## Step 17 · agent · 2026-09-24T17:37:50.936Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:37:51.349Z { "cell_id": "12", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:38:08.459Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"d8162c","wall_time_seconds":30.001254336,"session_id":82896,"original_token_count":0,"output":""} ## Step 18 · agent · 2026-09-24T17:38:10.386Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:11.085Z { "code": "const r = await tools.write_stdin({session_id:82896,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:38:22.102Z Script running with cell ID 13 Wall time 11.0 seconds Output: ## Step 19 · agent · 2026-09-24T17:38:23.772Z ### Thinking unavailable — no text recorded ### wait · 2026-09-24T17:38:24.166Z { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:38:41.105Z Script completed Wall time 16.9 seconds Output: ## Step 20 · agent · 2026-09-24T17:38:43.324Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:44.020Z { "code": "const r = await tools.write_stdin({session_id:82896,chars:\"\",yield_time_ms:1000,max_output_tokens:30000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:38:49.039Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"a06daa","wall_time_seconds":5.001435628,"session_id":82896,"original_token_count":0,"output":""} ## Step 21 · agent · 2026-09-24T17:38:51.156Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:38:51.851Z { "code": "const r = await tools.write_stdin({session_id:82896,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:39:02.865Z Script running with cell ID 15 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:39:04.953Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:39:21.868Z Script completed Wall time 16.9 seconds Output: ## Step 22 · agent · 2026-09-24T17:39:23.149Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:25.370Z { "code": "const r = await tools.write_stdin({session_id:82896,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:39:36.384Z Script running with cell ID 16 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:39:38.333Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:39:55.387Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"981cc8","wall_time_seconds":30.001171952,"session_id":82896,"original_token_count":0,"output":""} ## Step 23 · agent · 2026-09-24T17:39:57.018Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:39:57.789Z { "code": "const r = await tools.write_stdin({session_id:82896,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:39:57.975Z Script completed Wall time 0.2 seconds Output: truth 37769 users 4263 grades {1: 33352, 3: 4417} peruser {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, 'max': 442.0} target catalog listens 31062540 unique pairs (7868664, 2) Traceback (most recent call last): File "/app/solution/analyze.py", line 26, in agg=tl.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),strong=('played_ratio_pct',lambda x:(x>=80).sum()),maxratio=('played_ratio_pct','max')).reset_index() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 1432, in aggregate result = op.agg() ^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/apply.py", line 190, in agg return self.agg_dict_like() ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/apply.py", line 423, in agg_dict_like return self.agg_or_apply_dict_like(op_name="agg") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/apply.py", line 1603, in agg_or_apply_dict_like result_index, result_data = self.compute_dict_like( ^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/apply.py", line 497, in compute_dict_like getattr(obj._gotitem(key, ndim=1), op_name)(how, **kwargs) File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 257, in aggregate ret = self._aggregate_multiple_funcs(func, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 362, in _aggregate_multiple_funcs results[key] = self.aggregate(func, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 291, in aggregate return self._python_agg_general(func, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 327, in _python_agg_general result = self._grouper.agg_series(obj, f) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/ops.py", line 873, in agg_series result = self._aggregate_series_pure_python(obj, func) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/ops.py", line 894, in _aggregate_series_pure_python res = func(group) ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 324, in f = lambda x: func(x, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/analyze.py", line 26, in agg=tl.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),strong=('played_ratio_pct',lambda x:(x>=80).sum()),maxratio=('played_ratio_pct','max')).reset_index() ^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method return method(self, other) ^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 60, in __ge__ return self._cmp_method(other, operator.ge) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py", line 6140, in _cmp_method return self._construct_result(res_values, name=res_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py", line 6252, in _construct_result out = self._constructor(result, index=self.index, dtype=dtype, copy=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py", line 587, in __init__ data = sanitize_array(data, index, dtype, copy) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/construction.py", line 656, in sanitize_array subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/construction.py", line 709, in _sanitize_ndim result = _maybe_repeat(result, index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/construction.py", line 761, in _maybe_repeat if 1 == len(arr) != len(index): ^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 912, in __len__ def __len__(self) -> int: KeyboardInterrupt ## Step 24 · agent · 2026-09-24T17:39:59.447Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:40:03.122Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/analyze.py\\n@@\\n print('target catalog listens',len(tl),'unique pairs',tl[['uid','item_id']].drop_duplicates().shape)\\n-agg=tl.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),strong=('played_ratio_pct',lambda x:(x>=80).sum()),maxratio=('played_ratio_pct','max')).reset_index()\\n+tl['strong_flag']=(tl.played_ratio_pct>=80).astype('uint8')\\n+agg=tl.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),strong=('strong_flag','sum'),maxratio=('played_ratio_pct','max')).reset_index()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:40:03.194Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:40:05.633Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/analyze.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:40:16.647Z Script running with cell ID 19 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:40:18.228Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:40:28.619Z Script completed Wall time 10.4 seconds Output: truth 37769 users 4263 grades {1: 33352, 3: 4417} peruser {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, 'max': 442.0} target catalog listens 31062540 unique pairs (7868664, 2) truth history coverage {'n': 0.0, 'liked': 0.0, 'disliked': 0.0} grade 1 n 33352 pastpair 0.0 liked 0.0 stats {'n': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'last': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'org': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'strong': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}} grade 3 n 4417 pastpair 0.0 liked 0.0 stats {'n': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'last': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'org': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}, 'strong': {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '25%': nan, '50%': nan, '75%': nan, 'max': nan}} truth poprank count mean std ... 50% 75% max grade ... 1 33352.0 8406.287659 8124.885100 ... 5545.0 13274.0 30000.0 3 4417.0 8891.975323 8053.409263 ... 6371.0 14181.0 29989.0 [2 rows x 8 columns] historical candidate eligibility 0.0 count 8452.0 mean 0.0 std 0.0 min 0.0 25% 0.0 50% 0.0 75% 0.0 max 0.0 Name: eligible, dtype: float64 ## Step 25 · agent · 2026-09-24T17:40:41.036Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-09-24T17:40:52.644Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-09-24T17:41:01.962Z ### Thinking unavailable — no text recorded The key structural finding is decisive: validation eligibility excludes every catalog track the user has previously played. So repeat-history heuristics are invalid for this shelf; the model must transfer taste across users/items. I’m switching the core to a leakage-safe collaborative model that learns user similarity from the entire 845k-track listening graph, then scores unseen catalog tracks from neighboring listeners’ behavior. ### exec · 2026-09-24T17:41:44.102Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build validation or final Made-for-You rankings with user-user collaborative filtering.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import gc\\n+import json\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.preprocessing import normalize\\n+\\n+DATA = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+TRAIN_END = 24_786_800\\n+VAL_END = 25_393_395\\n+\\n+\\n+def read_events(stage: str, family: str, columns: list[str]) -> pd.DataFrame:\\n+ parts = [pd.read_parquet(DATA / f'train_{family}.parquet', columns=columns)]\\n+ if stage == 'final':\\n+ parts.append(pd.read_parquet(DATA / f'val_{family}.parquet', columns=columns))\\n+ return pd.concat(parts, ignore_index=True)\\n+\\n+\\n+def build(stage: str, output: Path, neighbors: int, idf_power: float,\\n+ similarity_power: float, organic_weight: float, like_weight: float,\\n+ pop_weight: float) -> None:\\n+ eligibility_path = DATA / ('validation_eligible_candidates.npz' if stage == 'validation' else 'test_eligible_candidates.npz')\\n+ with np.load(eligibility_path) as z:\\n+ target_uids = z['uids'].astype(np.int64)\\n+ candidate_ids = z['item_ids'].astype(np.int64)\\n+ eligible_packed = z['packed_eligible'].copy()\\n+ target_row = {int(u): r for r, u in enumerate(target_uids)}\\n+ cand_col = {int(i): c for c, i in enumerate(candidate_ids)}\\n+ cutoff = TRAIN_END if stage == 'validation' else VAL_END\\n+\\n+ print('reading listens', flush=True)\\n+ listens = read_events(stage, 'listens', ['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ listens = listens[listens.played_ratio_pct >= 50].copy()\\n+ likes = read_events(stage, 'likes', ['uid','timestamp','item_id','is_organic'])\\n+ all_uids = np.union1d(listens.uid.unique(), likes.uid.unique()).astype(np.int64)\\n+ uid_index = pd.Index(all_uids)\\n+ all_items = np.union1d(listens.item_id.unique(), likes.item_id.unique()).astype(np.int64)\\n+ item_index = pd.Index(all_items)\\n+ print(f'positive events={len(listens):,} users={len(all_uids):,} items={len(all_items):,}', flush=True)\\n+\\n+ # Aggregate repeated plays before sparse construction. Recency-weighted counts let\\n+ # profiles follow current taste without discarding older, useful long-tail overlap.\\n+ listens['recent30'] = (listens.timestamp >= cutoff - 30*86400).astype(np.uint8)\\n+ listens['recent7'] = (listens.timestamp >= cutoff - 7*86400).astype(np.uint8)\\n+ agg = listens.groupby(['uid','item_id'], sort=False, observed=True).agg(\\n+ n=('timestamp','size'), org=('is_organic','sum'), r30=('recent30','sum'), r7=('recent7','sum')\\n+ ).reset_index()\\n+ del listens\\n+ gc.collect()\\n+ rows = uid_index.get_indexer(agg.uid).astype(np.int32)\\n+ cols = item_index.get_indexer(agg.item_id).astype(np.int32)\\n+ strength = np.log1p(\\n+ agg.n.to_numpy(np.float32)\\n+ + organic_weight * agg.org.to_numpy(np.float32)\\n+ + 0.55 * agg.r30.to_numpy(np.float32)\\n+ + 0.75 * agg.r7.to_numpy(np.float32)\\n+ ).astype(np.float32)\\n+ X = sparse.coo_matrix((strength,(rows,cols)), shape=(len(all_uids),len(all_items)), dtype=np.float32).tocsr()\\n+ del rows, cols, strength\\n+\\n+ # Likes are valuable profile anchors. They are additive and also enter the\\n+ # neighbor-to-catalog response matrix below.\\n+ lr = uid_index.get_indexer(likes.uid).astype(np.int32)\\n+ lc = item_index.get_indexer(likes.item_id).astype(np.int32)\\n+ lv = (like_weight * (1.0 + 0.35*likes.is_organic.to_numpy(np.float32))).astype(np.float32)\\n+ X += sparse.coo_matrix((lv,(lr,lc)), shape=X.shape, dtype=np.float32).tocsr()\\n+ X.sum_duplicates()\\n+\\n+ # TF-IDF cosine profile: rare shared tracks are far more diagnostic than hits.\\n+ df = np.diff(X.tocsc().indptr).astype(np.float32)\\n+ idf = np.log1p(len(all_uids) / (1.0 + df)).astype(np.float32)\\n+ X.data *= np.power(idf[X.indices], idf_power).astype(np.float32)\\n+ normalize(X, norm='l2', axis=1, copy=False)\\n+\\n+ print('computing exact user similarities', flush=True)\\n+ sim = (X @ X.T).toarray().astype(np.float32, copy=False)\\n+ del X\\n+ np.fill_diagonal(sim, 0.0)\\n+ # Keep only strongest neighbors, with an optional sharpening transform.\\n+ k = min(neighbors, sim.shape[1]-1)\\n+ keep = np.argpartition(sim, -k, axis=1)[:, -k:]\\n+ mask = np.zeros(sim.shape, dtype=bool)\\n+ np.put_along_axis(mask, keep, True, axis=1)\\n+ sim[~mask] = 0\\n+ del mask, keep\\n+ if similarity_power != 1.0:\\n+ np.power(sim, similarity_power, out=sim)\\n+ sim = sparse.csr_matrix(sim)\\n+\\n+ # Build neighbor response on catalog tracks. Unlike eligibility, this includes\\n+ # other users' historical events and therefore transfers preference safely.\\n+ ac = agg.item_id.map(cand_col).fillna(-1).to_numpy(np.int32)\\n+ use = ac >= 0\\n+ ar = uid_index.get_indexer(agg.uid).astype(np.int32)\\n+ response = np.log1p(\\n+ agg.n.to_numpy(np.float32)\\n+ + 1.25*organic_weight*agg.org.to_numpy(np.float32)\\n+ + 0.8*agg.r30.to_numpy(np.float32)\\n+ + 1.1*agg.r7.to_numpy(np.float32)\\n+ ).astype(np.float32)\\n+ Y = sparse.coo_matrix((response[use],(ar[use],ac[use])),shape=(len(all_uids),len(candidate_ids)),dtype=np.float32).tocsr()\\n+ lcc = likes.item_id.map(cand_col).fillna(-1).to_numpy(np.int32)\\n+ lu = lcc >= 0\\n+ Y += sparse.coo_matrix((lv[lu],(lr[lu],lcc[lu])),shape=Y.shape,dtype=np.float32).tocsr()\\n+ Y.sum_duplicates()\\n+ del agg, likes, ar, ac, response, lr, lc, lv, lcc\\n+ gc.collect()\\n+\\n+ # Bayesian-smoothed global organic popularity is only a weak prior/fallback.\\n+ global_pop = np.asarray(Y.sum(axis=0)).ravel().astype(np.float32)\\n+ global_pop = np.log1p(global_pop)\\n+ global_pop = (global_pop - global_pop.mean()) / (global_pop.std() + 1e-6)\\n+\\n+ all_uid_row = {int(u): r for r,u in enumerate(all_uids)}\\n+ src_rows = np.asarray([all_uid_row.get(int(u), -1) for u in target_uids], dtype=np.int32)\\n+ out_uid=[]; out_item=[]; out_rank=[]\\n+ batch=128\\n+ print('scoring candidates', flush=True)\\n+ for start in range(0,len(target_uids),batch):\\n+ end=min(start+batch,len(target_uids))\\n+ valid = src_rows[start:end] >= 0\\n+ scores=np.tile((pop_weight*global_pop)[None,:],(end-start,1)).astype(np.float32)\\n+ if valid.any():\\n+ pred=(sim[src_rows[start:end][valid]] @ Y).toarray().astype(np.float32)\\n+ # Normalize by total neighbor similarity so sparse-profile users are comparable.\\n+ denom=np.asarray(sim[src_rows[start:end][valid]].sum(axis=1)).ravel()+1e-6\\n+ pred/=denom[:,None]\\n+ scores[valid]+=pred\\n+ elig=np.unpackbits(eligible_packed[start:end],axis=1,count=len(candidate_ids),bitorder='little').astype(bool)\\n+ scores[~elig]=-np.inf\\n+ top=np.argpartition(scores,-100,axis=1)[:,-100:]\\n+ top_scores=np.take_along_axis(scores,top,axis=1)\\n+ order=np.argsort(-top_scores,axis=1,kind='stable')\\n+ top=np.take_along_axis(top,order,axis=1)\\n+ for j,u in enumerate(target_uids[start:end]):\\n+ out_uid.extend([int(u)]*100)\\n+ out_item.extend(candidate_ids[top[j]].tolist())\\n+ out_rank.extend(range(1,101))\\n+ frame=pd.DataFrame({'uid':np.asarray(out_uid,dtype=np.int64),'item_id':np.asarray(out_item,dtype=np.int64),'rank':np.asarray(out_rank,dtype=np.int64)})\\n+ frame.to_parquet(output,index=False)\\n+ meta={'stage':stage,'neighbors':neighbors,'idf_power':idf_power,'similarity_power':similarity_power,'organic_weight':organic_weight,'like_weight':like_weight,'pop_weight':pop_weight}\\n+ output.with_suffix('.json').write_text(json.dumps(meta,indent=2))\\n+ print(f'wrote {output} rows={len(frame):,}', flush=True)\\n+\\n+\\n+if __name__ == '__main__':\\n+ ap=argparse.ArgumentParser()\\n+ ap.add_argument('--stage',choices=['validation','final'],required=True)\\n+ ap.add_argument('--output',type=Path,required=True)\\n+ ap.add_argument('--neighbors',type=int,default=180)\\n+ ap.add_argument('--idf-power',type=float,default=0.75)\\n+ ap.add_argument('--similarity-power',type=float,default=2.0)\\n+ ap.add_argument('--organic-weight',type=float,default=1.0)\\n+ ap.add_argument('--like-weight',type=float,default=2.5)\\n+ ap.add_argument('--pop-weight',type=float,default=0.035)\\n+ a=ap.parse_args()\\n+ build(a.stage,a.output,a.neighbors,a.idf_power,a.similarity_power,a.organic_weight,a.like_weight,a.pop_weight)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:41:44.179Z Script completed Wall time 0.0 seconds Output: {} ## Step 28 · agent · 2026-09-24T17:41:46.459Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:41:47.901Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/validation_knn.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:41:58.916Z Script running with cell ID 21 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:42:00.675Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:42:13.906Z Script completed Wall time 13.2 seconds Output: reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities scoring candidates wrote solution/validation_knn.parquet rows=845,600 ## Step 29 · agent · 2026-09-24T17:42:16.313Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:17.359Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_knn.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:42:19.924Z Script completed Wall time 2.6 seconds Output: { "model": "solution/validation_knn.parquet", "scores": { "quality_ndcg@10": 0.018061, "quality_users": 4263 } } ## Step 30 · agent · 2026-09-24T17:42:32.516Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:42:40.397Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/make_baselines.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+z=np.load(D/'validation_eligible_candidates.npz')\\n+uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\n+catalog=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\\n+\\n+def save(name, score):\\n+ us=[]; its=[]; rs=[]\\n+ for r,u in enumerate(uids):\\n+ ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n+ s=score.copy(); s[~ok]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(-s[top],kind='stable')]\\n+ us.extend([int(u)]*100); its.extend(items[top]); rs.extend(range(1,101))\\n+ pd.DataFrame({'uid':us,'item_id':its,'rank':rs}).to_parquet(O/f'{name}.parquet',index=False)\\n+\\n+save('validation_pop',-catalog.popularity_rank.to_numpy(np.float32))\\n+\\n+# Organic, strong, recent event popularity variants.\\n+l=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\\n+for name,mask in {\\n+ 'validation_orgpop':(l.is_organic==1)&(l.played_ratio_pct>=80),\\n+ 'validation_recentorgpop':(l.is_organic==1)&(l.played_ratio_pct>=80)&(l.timestamp>=24786800-30*86400),\\n+ 'validation_recentallpop':(l.played_ratio_pct>=80)&(l.timestamp>=24786800-30*86400),\\n+}.items():\\n+ cnt=l.loc[mask].groupby('item_id').size().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ save(name,np.log1p(cnt))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:42:40.438Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:42:43.041Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/make_baselines.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:42:51.847Z Script completed Wall time 8.8 seconds Output: ### exec · 2026-09-24T17:42:54.695Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_pop.parquet solution/validation_orgpop.parquet solution/validation_recentorgpop.parquet solution/validation_recentallpop.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:43:04.744Z Script completed Wall time 10.0 seconds Output: {"chunk_id":"826f54","wall_time_seconds":9.877686103,"exit_code":0,"original_token_count":132} { "model": "solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } { "model": "solution/validation_orgpop.parquet", "scores": { "quality_ndcg@10": 0.010551, "quality_users": 4263 } } { "model": "solution/validation_recentorgpop.parquet", "scores": { "quality_ndcg@10": 0.013314, "quality_users": 4263 } } { "model": "solution/validation_recentallpop.parquet", "scores": { "quality_ndcg@10": 0.01674, "quality_users": 4263 } } ## Step 31 · agent · 2026-09-24T17:43:12.261Z ### Thinking unavailable — no text recorded The first collaborative model scores 0.018061 NDCG@10, beating the strongest simple popularity baseline (0.016740). Recent listening trends matter much more than lifetime popularity, so I’m tuning neighbor concentration and separating the temporal popularity prior from the collaborative score. ### exec · 2026-09-24T17:43:16.058Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- global_pop = np.asarray(Y.sum(axis=0)).ravel().astype(np.float32)\\n+ recent_pop = sparse.coo_matrix(\\n+ ((agg.r30.to_numpy(np.float32) + 1.5*agg.r7.to_numpy(np.float32))[use],\\n+ (ar[use],ac[use])), shape=Y.shape, dtype=np.float32\\n+ ).tocsr()\\n+ global_pop = np.asarray(recent_pop.sum(axis=0)).ravel().astype(np.float32)\\n@@\\n- del agg, likes, ar, ac, response, lr, lc, lv, lcc\\n+ del agg, likes, ar, ac, response, recent_pop, lr, lc, lv, lcc\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:43:16.122Z Script failed Wall time 0.0 seconds Output: Script error: apply_patch verification failed: Failed to find expected lines in /app/solution/build_ranker.py: del agg, likes, ar, ac, response, lr, lc, lv, lcc ## Step 32 · agent · 2026-09-24T17:43:21.204Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:21.205Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"global_pop|del agg\\\" solution/build_ranker.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:43:21.291Z Script completed Wall time 0.0 seconds Output: 115: del agg, likes, ar, ac, response, lr, lc, lv, lcc 119: global_pop = np.asarray(Y.sum(axis=0)).ravel().astype(np.float32) 120: global_pop = np.log1p(global_pop) 121: global_pop = (global_pop - global_pop.mean()) / (global_pop.std() + 1e-6) 131: scores=np.tile((pop_weight*global_pop)[None,:],(end-start,1)).astype(np.float32) ## Step 33 · agent · 2026-09-24T17:43:22.971Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:27.073Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n Y += sparse.coo_matrix((lv[lu],(lr[lu],lcc[lu])),shape=Y.shape,dtype=np.float32).tocsr()\\n Y.sum_duplicates()\\n- del agg, likes, ar, ac, response, lr, lc, lv, lcc\\n+ recent_pop = np.bincount(\\n+ ac[use],\\n+ weights=(agg.r30.to_numpy(np.float32) + 1.5*agg.r7.to_numpy(np.float32))[use],\\n+ minlength=len(candidate_ids),\\n+ ).astype(np.float32)\\n+ del agg, likes, ar, ac, response, lr, lc, lv, lcc\\n@@\\n- global_pop = np.asarray(Y.sum(axis=0)).ravel().astype(np.float32)\\n+ global_pop = recent_pop\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:43:27.148Z Script completed Wall time 0.0 seconds Output: {} ## Step 34 · agent · 2026-09-24T17:43:29.833Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:43:33.614Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k50p2.parquet --neighbors 50 --similarity-power 2 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k180p2np.parquet --neighbors 180 --similarity-power 2 --pop-weight 0\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k500p2.parquet --neighbors 500 --similarity-power 2 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k180p1.parquet --neighbors 180 --similarity-power 1 --pop-weight 0.04\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:43:44.631Z Script running with cell ID 29 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:43:46.403Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:44:03.791Z Script completed Wall time 17.4 seconds Output: {"chunk_id":"9bea1e","wall_time_seconds":30.001079542,"session_id":72270,"original_token_count":68} reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities scoring candidates wrote solution/val_k50p2.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities SESSION_ID=72270 ## Step 35 · agent · 2026-09-24T17:44:05.197Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:44:06.169Z { "code": "const r = await tools.write_stdin({session_id:72270,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:44:17.185Z Script running with cell ID 30 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:44:20.579Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:44:36.188Z Script completed Wall time 15.6 seconds Output: scoring candidates wrote solution/val_k180p2np.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities SESSION_ID=72270 ### exec · 2026-09-24T17:44:38.831Z { "code": "const r = await tools.write_stdin({session_id:72270,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:44:49.846Z Script running with cell ID 31 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:44:52.955Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:45:08.849Z Script completed Wall time 15.9 seconds Output: scoring candidates wrote solution/val_k500p2.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities SESSION_ID=72270 ### exec · 2026-09-24T17:45:11.660Z { "code": "const r = await tools.write_stdin({session_id:72270,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:45:20.094Z Script completed Wall time 8.4 seconds Output: scoring candidates wrote solution/val_k180p1.parquet rows=845,600 ## Step 36 · agent · 2026-09-24T17:45:21.495Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:23.361Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/val_k50p2.parquet solution/val_k180p2np.parquet solution/val_k500p2.parquet solution/val_k180p1.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:45:33.534Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"865d66","wall_time_seconds":10.000993038,"exit_code":0,"original_token_count":124} { "model": "solution/val_k50p2.parquet", "scores": { "quality_ndcg@10": 0.018801, "quality_users": 4263 } } { "model": "solution/val_k180p2np.parquet", "scores": { "quality_ndcg@10": 0.018406, "quality_users": 4263 } } { "model": "solution/val_k500p2.parquet", "scores": { "quality_ndcg@10": 0.017658, "quality_users": 4263 } } { "model": "solution/val_k180p1.parquet", "scores": { "quality_ndcg@10": 0.018355, "quality_users": 4263 } } ## Step 37 · agent · 2026-09-24T17:45:37.384Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:45:43.491Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k20p2.parquet --neighbors 20 --similarity-power 2 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k80p2.parquet --neighbors 80 --similarity-power 2 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k50p3.parquet --neighbors 50 --similarity-power 3 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k50p1.parquet --neighbors 50 --similarity-power 1 --pop-weight 0.04\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k50pop02.parquet --neighbors 50 --similarity-power 2 --pop-weight 0.02\\n/opt/task-venv/bin/python -u solution/build_ranker.py --stage validation --output solution/val_k50pop08.parquet --neighbors 50 --similarity-power 2 --pop-weight 0.08\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:45:54.510Z Script running with cell ID 34 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:45:56.540Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:46:13.671Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"bb79a7","wall_time_seconds":30.000959398,"session_id":91754,"original_token_count":68} reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities scoring candidates wrote solution/val_k20p2.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities SESSION_ID=91754 ## Step 38 · agent · 2026-09-24T17:46:15.340Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:46:16.290Z { "code": "const r = await tools.write_stdin({session_id:91754,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:46:27.306Z Script running with cell ID 35 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:46:29.580Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:46:46.308Z Script completed Wall time 16.7 seconds Output: scoring candidates wrote solution/val_k80p2.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities scoring candidates SESSION_ID=91754 ### exec · 2026-09-24T17:46:49.237Z { "code": "const r = await tools.write_stdin({session_id:91754,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:47:00.252Z Script running with cell ID 36 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:01.960Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:47:19.254Z Script completed Wall time 17.3 seconds Output: wrote solution/val_k50p3.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities scoring candidates wrote solution/val_k50p1.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 SESSION_ID=91754 ### exec · 2026-09-24T17:47:21.701Z { "code": "const r = await tools.write_stdin({session_id:91754,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:47:32.715Z Script running with cell ID 37 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:47:34.775Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:47:51.718Z Script completed Wall time 16.9 seconds Output: computing exact user similarities scoring candidates wrote solution/val_k50pop02.parquet rows=845,600 reading listens positive events=27,500,755 users=9,967 items=621,550 computing exact user similarities SESSION_ID=91754 ### exec · 2026-09-24T17:47:54.719Z { "code": "const r = await tools.write_stdin({session_id:91754,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:47:57.221Z Script completed Wall time 2.5 seconds Output: scoring candidates wrote solution/val_k50pop08.parquet rows=845,600 ### exec · 2026-09-24T17:48:01.035Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/val_k20p2.parquet solution/val_k80p2.parquet solution/val_k50p3.parquet solution/val_k50p1.parquet solution/val_k50pop02.parquet solution/val_k50pop08.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:48:12.049Z Script running with cell ID 39 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:13.885Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:48:16.340Z Script completed Wall time 2.5 seconds Output: {"chunk_id":"8a7a1c","wall_time_seconds":15.122998905,"exit_code":0,"original_token_count":185} { "model": "solution/val_k20p2.parquet", "scores": { "quality_ndcg@10": 0.017927, "quality_users": 4263 } } { "model": "solution/val_k80p2.parquet", "scores": { "quality_ndcg@10": 0.018732, "quality_users": 4263 } } { "model": "solution/val_k50p3.parquet", "scores": { "quality_ndcg@10": 0.018691, "quality_users": 4263 } } { "model": "solution/val_k50p1.parquet", "scores": { "quality_ndcg@10": 0.019022, "quality_users": 4263 } } { "model": "solution/val_k50pop02.parquet", "scores": { "quality_ndcg@10": 0.018542, "quality_users": 4263 } } { "model": "solution/val_k50pop08.parquet", "scores": { "quality_ndcg@10": 0.018684, "quality_users": 4263 } } ## Step 39 · agent · 2026-09-24T17:48:22.693Z ### Thinking unavailable — no text recorded Tuning shows that about 50 nearest listeners with unsharpened cosine weights is the best local model so far (0.019022). The variants make different top-10 errors, so I’m testing rank-fused ensembles and, in parallel, a low-rank factor model to capture broader taste structure that exact-neighbor overlap misses. ### exec · 2026-09-24T17:48:34.255Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/fuse_rankings.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+O=Path('/app/solution')\\n+\\n+def load(name):\\n+ x=pd.read_parquet(O/name).sort_values(['uid','rank'])\\n+ return x\\n+\\n+def fuse(output, specs, mode='rrf', constant=30):\\n+ frames=[load(n) for n,w in specs]\\n+ uids=frames[0].uid.unique(); rows=[]\\n+ grouped=[dict(tuple(f.groupby('uid'))) for f in frames]\\n+ for u in uids:\\n+ scores={}\\n+ for (name,w),g in zip(specs,grouped):\\n+ for item,rank in g[int(u)][['item_id','rank']].itertuples(index=False,name=None):\\n+ value=1/(constant+rank) if mode=='rrf' else 101-rank\\n+ scores[int(item)]=scores.get(int(item),0)+w*value\\n+ top=sorted(scores,key=lambda i:(-scores[i],i))[:100]\\n+ rows.extend((int(u),item,r+1) for r,item in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/output,index=False)\\n+\\n+base='val_k50p1.parquet'\\n+configs={\\n+ 'fuse_p1_p2':[(base,1),('val_k50p2.parquet',1)],\\n+ 'fuse_p1_k80':[(base,1),('val_k80p2.parquet',1)],\\n+ 'fuse_p1_pop10':[(base,1),('validation_recentallpop.parquet',.10)],\\n+ 'fuse_p1_pop25':[(base,1),('validation_recentallpop.parquet',.25)],\\n+ 'fuse_p1_pop50':[(base,1),('validation_recentallpop.parquet',.50)],\\n+ 'fuse_3knn':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5)],\\n+ 'fuse_all':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5),('validation_recentallpop.parquet',.25)],\\n+}\\n+for stem,spec in configs.items():\\n+ fuse(stem+'.parquet',spec,'rrf',30)\\n+ fuse(stem+'_borda.parquet',spec,'borda')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:48:34.310Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:48:37.731Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/fuse_rankings.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:48:48.745Z Script running with cell ID 41 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:48:50.578Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:49:07.920Z Script completed Wall time 17.3 seconds Output: SESSION_ID=41001 ## Step 40 · agent · 2026-09-24T17:49:09.494Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:10.187Z { "code": "const r = await tools.write_stdin({session_id:41001,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:49:21.201Z Script running with cell ID 42 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:23.139Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:49:40.204Z Script completed Wall time 17.1 seconds Output: ## Step 41 · agent · 2026-09-24T17:49:41.620Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:49:42.315Z { "code": "const r = await tools.write_stdin({session_id:41001,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:49:53.329Z Script running with cell ID 43 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:49:55.503Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:50:12.333Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"df037c","wall_time_seconds":30.001582623,"session_id":41001,"original_token_count":0,"output":""} ## Step 42 · agent · 2026-09-24T17:50:14.986Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:15.683Z { "code": "const r = await tools.write_stdin({session_id:41001,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:50:26.699Z Script running with cell ID 44 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:50:28.549Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:50:45.702Z Script completed Wall time 17.2 seconds Output: {"chunk_id":"255105","wall_time_seconds":30.001168744,"session_id":41001,"original_token_count":0,"output":""} ## Step 43 · agent · 2026-09-24T17:50:47.273Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:48.004Z { "code": "const r = await tools.write_stdin({session_id:41001,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:50:48.080Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b8fe4f","wall_time_seconds":8.6e-7,"exit_code":0,"original_token_count":0,"output":""} ## Step 44 · agent · 2026-09-24T17:50:49.734Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:50:51.294Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/fuse*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:51:02.308Z Script running with cell ID 46 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:51:04.115Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:51:21.517Z Script completed Wall time 17.4 seconds Output: {"chunk_id":"06a74a","wall_time_seconds":30.00110425,"session_id":17814,"original_token_count":347} { "model": "solution/fuse_3knn.parquet", "scores": { "quality_ndcg@10": 0.019285, "quality_users": 4263 } } { "model": "solution/fuse_3knn_borda.parquet", "scores": { "quality_ndcg@10": 0.019272, "quality_users": 4263 } } { "model": "solution/fuse_all.parquet", "scores": { "quality_ndcg@10": 0.019498, "quality_users": 4263 } } { "model": "solution/fuse_all_borda.parquet", "scores": { "quality_ndcg@10": 0.019647, "quality_users": 4263 } } { "model": "solution/fuse_p1_k80.parquet", "scores": { "quality_ndcg@10": 0.019169, "quality_users": 4263 } } { "model": "solution/fuse_p1_k80_borda.parquet", "scores": { "quality_ndcg@10": 0.019286, "quality_users": 4263 } } { "model": "solution/fuse_p1_p2.parquet", "scores": { "quality_ndcg@10": 0.018913, "quality_users": 4263 } } { "model": "solution/fuse_p1_p2_borda.parquet", "scores": { "quality_ndcg@10": 0.018812, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop10.parquet", "scores": { "quality_ndcg@10": 0.019395, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop10_borda.parquet", "scores": { "quality_ndcg@10": 0.019323, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop25.parquet", "scores": { "quality_ndcg@10": 0.019762, "quality_users": 4263 } } SESSION_ID=17814 ## Step 45 · agent · 2026-09-24T17:51:25.275Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:25.969Z { "code": "const r = await tools.write_stdin({session_id:17814,chars:\"\",yield_time_ms:30000,max_output_tokens:30000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:51:27.071Z Script completed Wall time 1.1 seconds Output: { "model": "solution/fuse_p1_pop25_borda.parquet", "scores": { "quality_ndcg@10": 0.019505, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop50.parquet", "scores": { "quality_ndcg@10": 0.020494, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop50_borda.parquet", "scores": { "quality_ndcg@10": 0.020864, "quality_users": 4263 } } ## Step 46 · agent · 2026-09-24T17:51:30.390Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:38.438Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/fuse_rankings.py\\n@@\\n-configs={\\n- 'fuse_p1_p2':[(base,1),('val_k50p2.parquet',1)],\\n- 'fuse_p1_k80':[(base,1),('val_k80p2.parquet',1)],\\n- 'fuse_p1_pop10':[(base,1),('validation_recentallpop.parquet',.10)],\\n- 'fuse_p1_pop25':[(base,1),('validation_recentallpop.parquet',.25)],\\n- 'fuse_p1_pop50':[(base,1),('validation_recentallpop.parquet',.50)],\\n- 'fuse_3knn':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5)],\\n- 'fuse_all':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5),('validation_recentallpop.parquet',.25)],\\n-}\\n+configs={\\n+ 'fuse_p1_pop75':[(base,1),('validation_recentallpop.parquet',.75)],\\n+ 'fuse_p1_pop100':[(base,1),('validation_recentallpop.parquet',1.0)],\\n+ 'fuse_p1_pop150':[(base,1),('validation_recentallpop.parquet',1.5)],\\n+ 'fuse_3_pop50':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5),('validation_recentallpop.parquet',.5)],\\n+ 'fuse_3_pop75':[(base,1),('val_k50p2.parquet',.5),('val_k80p2.parquet',.5),('validation_recentallpop.parquet',.75)],\\n+}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:51:38.522Z Script completed Wall time 0.0 seconds Output: {} ## Step 47 · agent · 2026-09-24T17:51:40.920Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:51:42.159Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/fuse_rankings.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:51:53.173Z Script running with cell ID 49 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:51:55.093Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:52:12.340Z Script completed Wall time 17.2 seconds Output: SESSION_ID=37303 ### exec · 2026-09-24T17:52:14.688Z { "code": "const r = await tools.write_stdin({session_id:37303,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:52:25.702Z Script running with cell ID 50 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:52:28.986Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:52:44.706Z Script completed Wall time 15.7 seconds Output: SESSION_ID=37303 ### exec · 2026-09-24T17:52:46.755Z { "code": "const r = await tools.write_stdin({session_id:37303,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:52:57.771Z Script running with cell ID 51 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:00.018Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:53:16.773Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"70a0f4","wall_time_seconds":30.000959718,"session_id":37303,"original_token_count":0,"output":""} ### exec · 2026-09-24T17:53:18.888Z { "code": "const r = await tools.write_stdin({session_id:37303,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:53:29.901Z Script running with cell ID 52 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:31.586Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-09-24T17:53:32.488Z Script completed Wall time 0.9 seconds Output: {"chunk_id":"8170e0","wall_time_seconds":13.583359162,"exit_code":0,"original_token_count":0,"output":""} ### exec · 2026-09-24T17:53:36.281Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/fuse_p1_pop75*.parquet solution/fuse_p1_pop100*.parquet solution/fuse_p1_pop150*.parquet solution/fuse_3_pop50*.parquet solution/fuse_3_pop75*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:53:47.295Z Script running with cell ID 53 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:53:50.087Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-09-24T17:54:01.696Z Script completed Wall time 11.6 seconds Output: {"chunk_id":"d79464","wall_time_seconds":25.240487678,"exit_code":0,"original_token_count":323} { "model": "solution/fuse_p1_pop75.parquet", "scores": { "quality_ndcg@10": 0.02156, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop75_borda.parquet", "scores": { "quality_ndcg@10": 0.021359, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop100.parquet", "scores": { "quality_ndcg@10": 0.021537, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop100_borda.parquet", "scores": { "quality_ndcg@10": 0.020785, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop150.parquet", "scores": { "quality_ndcg@10": 0.021009, "quality_users": 4263 } } { "model": "solution/fuse_p1_pop150_borda.parquet", "scores": { "quality_ndcg@10": 0.020564, "quality_users": 4263 } } { "model": "solution/fuse_3_pop50.parquet", "scores": { "quality_ndcg@10": 0.019601, "quality_users": 4263 } } { "model": "solution/fuse_3_pop50_borda.parquet", "scores": { "quality_ndcg@10": 0.019909, "quality_users": 4263 } } { "model": "solution/fuse_3_pop75.parquet", "scores": { "quality_ndcg@10": 0.020106, "quality_users": 4263 } } { "model": "solution/fuse_3_pop75_borda.parquet", "scores": { "quality_ndcg@10": 0.020287, "quality_users": 4263 } } ## Step 48 · agent · 2026-09-24T17:54:15.582Z ### Thinking unavailable — no text recorded ## Step 49 · agent · 2026-09-24T17:54:18.296Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:40.199Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_svd.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Low-rank catalog interaction model used as a complementary ranker.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse, gc\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+END={'validation':24786800,'final':25393395}\\n+\\n+def read(stage,fam,cols):\\n+ x=[pd.read_parquet(D/f'train_{fam}.parquet',columns=cols)]\\n+ if stage=='final': x.append(pd.read_parquet(D/f'val_{fam}.parquet',columns=cols))\\n+ return pd.concat(x,ignore_index=True)\\n+\\n+def main(stage,components,idf_power):\\n+ ep=D/('validation_eligible_candidates.npz' if stage=='validation' else 'test_eligible_candidates.npz')\\n+ z=np.load(ep); targets=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\\n+ iidx=pd.Index(items)\\n+ l=read(stage,'listens',['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ c=iidx.get_indexer(l.item_id); use=(c>=0)&(l.played_ratio_pct.to_numpy()>=50)\\n+ l=l.loc[use].copy(); l['col']=c[use]; cutoff=END[stage]\\n+ l['r30']=(l.timestamp>=cutoff-30*86400).astype(np.uint8); l['r7']=(l.timestamp>=cutoff-7*86400).astype(np.uint8)\\n+ likes=read(stage,'likes',['uid','item_id','is_organic']); lc=iidx.get_indexer(likes.item_id); likes=likes.loc[lc>=0].copy(); likes['col']=lc[lc>=0]\\n+ uids=np.union1d(l.uid.unique(),likes.uid.unique()).astype(np.int64); uidx=pd.Index(uids)\\n+ a=l.groupby(['uid','col'],sort=False).agg(n=('timestamp','size'),org=('is_organic','sum'),r30=('r30','sum'),r7=('r7','sum')).reset_index()\\n+ rows=uidx.get_indexer(a.uid); vals=np.log1p(a.n.to_numpy(np.float32)+a.org.to_numpy(np.float32)+.55*a.r30.to_numpy(np.float32)+.75*a.r7.to_numpy(np.float32))\\n+ X=sparse.coo_matrix((vals,(rows,a.col)),shape=(len(uids),len(items)),dtype=np.float32).tocsr()\\n+ lr=uidx.get_indexer(likes.uid); lv=2.5*(1+.35*likes.is_organic.to_numpy(np.float32))\\n+ X+=sparse.coo_matrix((lv,(lr,likes.col)),shape=X.shape,dtype=np.float32).tocsr(); X.sum_duplicates()\\n+ recent=np.bincount(a.col,weights=a.r30.to_numpy(np.float32)+1.5*a.r7.to_numpy(np.float32),minlength=len(items)).astype(np.float32)\\n+ pop=np.log1p(recent); pop=(pop-pop.mean())/(pop.std()+1e-6)\\n+ if idf_power:\\n+ df=np.diff(X.tocsc().indptr).astype(np.float32); idf=np.log1p(len(uids)/(1+df)); X.data*=np.power(idf[X.indices],idf_power)\\n+ del l,likes,a,rows,vals,lr,lv; gc.collect()\\n+ print('matrix',X.shape,'nnz',X.nnz,'fitting SVD',flush=True)\\n+ svd=TruncatedSVD(n_components=components,n_iter=6,random_state=2026)\\n+ user_f=svd.fit_transform(X).astype(np.float32); item_f=svd.components_.astype(np.float32)\\n+ print('explained',svd.explained_variance_ratio_.sum(),flush=True)\\n+ ur={int(u):r for r,u in enumerate(uids)}; tr=np.array([ur.get(int(u),-1) for u in targets])\\n+ for alpha in [0,.1,.25,.5,1.0]:\\n+ out=[]\\n+ for start in range(0,len(targets),128):\\n+ end=min(start+128,len(targets)); s=np.tile((alpha*pop)[None,:],(end-start,1)).astype(np.float32)\\n+ good=tr[start:end]>=0\\n+ if good.any():\\n+ pred=user_f[tr[start:end][good]]@item_f\\n+ pred=(pred-pred.mean(1,keepdims=True))/(pred.std(1,keepdims=True)+1e-6)\\n+ s[good]+=pred\\n+ elig=np.unpackbits(packed[start:end],axis=1,count=len(items),bitorder='little').astype(bool); s[~elig]=-np.inf\\n+ top=np.argpartition(s,-100,axis=1)[:,-100:]; sc=np.take_along_axis(s,top,axis=1); top=np.take_along_axis(top,np.argsort(-sc,axis=1,kind='stable'),axis=1)\\n+ for j,u in enumerate(targets[start:end]): out.extend((int(u),int(items[x]),r+1) for r,x in enumerate(top[j]))\\n+ pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet(O/f'{stage}_svd{components}_idf{idf_power}_a{alpha}.parquet',index=False)\\n+\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser(); p.add_argument('--stage',choices=['validation','final'],required=True); p.add_argument('--components',type=int,default=128); p.add_argument('--idf-power',type=float,default=.25); a=p.parse_args(); main(a.stage,a.components,a.idf_power)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:54:40.282Z Script completed Wall time 0.0 seconds Output: {} ## Step 50 · agent · 2026-09-24T17:54:42.171Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:54:43.605Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:54:54.624Z Script running with cell ID 55 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:54:58.513Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:55:13.828Z Script completed Wall time 15.3 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.28867668 SESSION_ID=90756 ## Step 51 · agent · 2026-09-24T17:55:15.664Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:16.442Z { "code": "const r = await tools.write_stdin({session_id:90756,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:55:21.746Z Script completed Wall time 5.3 seconds Output: {"chunk_id":"c82ea6","wall_time_seconds":5.210644643,"exit_code":0,"original_token_count":0,"output":""} ## Step 52 · agent · 2026-09-24T17:55:25.225Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:25.702Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T17:55:36.716Z Script running with cell ID 57 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:55:38.506Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T17:55:38.600Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"150f13","wall_time_seconds":12.61931081,"exit_code":0,"original_token_count":179} { "model": "solution/validation_svd128_idf0.25_a0.1.parquet", "scores": { "quality_ndcg@10": 0.02401, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a0.25.parquet", "scores": { "quality_ndcg@10": 0.024429, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a0.5.parquet", "scores": { "quality_ndcg@10": 0.024927, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a0.parquet", "scores": { "quality_ndcg@10": 0.023627, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a1.0.parquet", "scores": { "quality_ndcg@10": 0.026562, "quality_users": 4263 } } ## Step 53 · agent · 2026-09-24T17:55:42.449Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:55:43.333Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_svd.py\\n@@\\n- for alpha in [0,.1,.25,.5,1.0]:\\n+ for alpha in [1.5,2.0,3.0,5.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:55:43.405Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:55:47.421Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:55:58.439Z Script running with cell ID 59 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:56:01.220Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:56:17.626Z Script completed Wall time 16.4 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.28867668 SESSION_ID=16914 ### exec · 2026-09-24T17:56:20.104Z { "code": "const r = await tools.write_stdin({session_id:16914,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:56:20.198Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T17:56:24.907Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.25_a1.5.parquet solution/validation_svd128_idf0.25_a2.0.parquet solution/validation_svd128_idf0.25_a3.0.parquet solution/validation_svd128_idf0.25_a5.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:56:35.179Z Script completed Wall time 10.3 seconds Output: {"chunk_id":"0a0530","wall_time_seconds":10.064675391,"exit_code":0,"original_token_count":143} { "model": "solution/validation_svd128_idf0.25_a1.5.parquet", "scores": { "quality_ndcg@10": 0.027557, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a2.0.parquet", "scores": { "quality_ndcg@10": 0.028766, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a3.0.parquet", "scores": { "quality_ndcg@10": 0.02987, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a5.0.parquet", "scores": { "quality_ndcg@10": 0.030385, "quality_users": 4263 } } ## Step 54 · agent · 2026-09-24T17:56:40.224Z ### Thinking unavailable — no text recorded The low-rank catalog model is substantially stronger: 0.030385 at 128 factors, versus 0.02156 for the best neighbor/popularity fusion. Its best setting still benefits from a strong recent-trend prior, so I’m probing higher trend weights and 256 factors before locking the final refit. ### exec · 2026-09-24T17:56:44.544Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_svd.py\\n@@\\n- for alpha in [1.5,2.0,3.0,5.0]:\\n+ for alpha in [5.0,7.5,10.0,15.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:56:44.631Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:56:47.837Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 256 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:56:58.851Z Script running with cell ID 63 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:57:01.322Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:57:18.034Z Script completed Wall time 16.7 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.36369377 SESSION_ID=99913 ## Step 55 · agent · 2026-09-24T17:57:20.116Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:21.256Z { "code": "const r = await tools.write_stdin({session_id:99913,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-09-24T17:57:27.565Z Script completed Wall time 6.3 seconds Output: {"chunk_id":"54f27b","wall_time_seconds":6.244822454,"exit_code":0,"original_token_count":0,"output":""} ## Step 56 · agent · 2026-09-24T17:57:29.057Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:30.460Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd256*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:57:40.583Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"904fa9","wall_time_seconds":9.951042111,"exit_code":0,"original_token_count":144} { "model": "solution/validation_svd256_idf0.25_a10.0.parquet", "scores": { "quality_ndcg@10": 0.027815, "quality_users": 4263 } } { "model": "solution/validation_svd256_idf0.25_a15.0.parquet", "scores": { "quality_ndcg@10": 0.026032, "quality_users": 4263 } } { "model": "solution/validation_svd256_idf0.25_a5.0.parquet", "scores": { "quality_ndcg@10": 0.029185, "quality_users": 4263 } } { "model": "solution/validation_svd256_idf0.25_a7.5.parquet", "scores": { "quality_ndcg@10": 0.02865, "quality_users": 4263 } } ## Step 57 · agent · 2026-09-24T17:57:42.876Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:57:44.354Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:57:55.421Z Script running with cell ID 66 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:57:57.612Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:58:14.541Z Script completed Wall time 16.9 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.28867668 SESSION_ID=29779 ### exec · 2026-09-24T17:58:18.130Z { "code": "const r = await tools.write_stdin({session_id:29779,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:58:18.260Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T17:58:25.275Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.25_a7.5.parquet solution/validation_svd128_idf0.25_a10.0.parquet solution/validation_svd128_idf0.25_a15.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:15000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:58:32.915Z Script completed Wall time 7.6 seconds Output: {"chunk_id":"4a728c","wall_time_seconds":7.466789685,"exit_code":0,"original_token_count":108} { "model": "solution/validation_svd128_idf0.25_a7.5.parquet", "scores": { "quality_ndcg@10": 0.029399, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a10.0.parquet", "scores": { "quality_ndcg@10": 0.028605, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a15.0.parquet", "scores": { "quality_ndcg@10": 0.027124, "quality_users": 4263 } } ## Step 58 · agent · 2026-09-24T17:58:37.429Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:58:38.956Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_svd.py\\n@@\\n- for alpha in [5.0,7.5,10.0,15.0]:\\n+ for alpha in [3.0,4.0,5.0,6.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T17:58:39.055Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T17:58:42.606Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 64 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:58:53.626Z Script running with cell ID 70 Wall time 11.0 seconds Output: ### wait · 2026-09-24T17:58:56.269Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T17:59:12.781Z Script completed Wall time 16.5 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.23454264 SESSION_ID=26730 ### exec · 2026-09-24T17:59:14.850Z { "code": "const r = await tools.write_stdin({session_id:26730,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T17:59:15.031Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T17:59:20.265Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd64*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T17:59:30.425Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"b63147","wall_time_seconds":9.98745257,"exit_code":0,"original_token_count":142} { "model": "solution/validation_svd64_idf0.25_a3.0.parquet", "scores": { "quality_ndcg@10": 0.028501, "quality_users": 4263 } } { "model": "solution/validation_svd64_idf0.25_a4.0.parquet", "scores": { "quality_ndcg@10": 0.028721, "quality_users": 4263 } } { "model": "solution/validation_svd64_idf0.25_a5.0.parquet", "scores": { "quality_ndcg@10": 0.029182, "quality_users": 4263 } } { "model": "solution/validation_svd64_idf0.25_a6.0.parquet", "scores": { "quality_ndcg@10": 0.029608, "quality_users": 4263 } } ## Step 59 · agent · 2026-09-24T17:59:33.302Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T17:59:34.762Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T17:59:45.821Z Script running with cell ID 73 Wall time 11.1 seconds Output: ### wait · 2026-09-24T17:59:48.513Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:00:04.954Z Script completed Wall time 16.4 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.28867668 SESSION_ID=19196 ### exec · 2026-09-24T18:00:07.338Z { "code": "const r = await tools.write_stdin({session_id:19196,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:00:07.449Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T18:00:11.375Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.25_a3.0.parquet solution/validation_svd128_idf0.25_a4.0.parquet solution/validation_svd128_idf0.25_a5.0.parquet solution/validation_svd128_idf0.25_a6.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T18:00:21.554Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"739486","wall_time_seconds":10.006491478,"exit_code":0,"original_token_count":143} { "model": "solution/validation_svd128_idf0.25_a3.0.parquet", "scores": { "quality_ndcg@10": 0.02987, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a4.0.parquet", "scores": { "quality_ndcg@10": 0.030502, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a5.0.parquet", "scores": { "quality_ndcg@10": 0.030385, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.25_a6.0.parquet", "scores": { "quality_ndcg@10": 0.030025, "quality_users": 4263 } } ## Step 60 · agent · 2026-09-24T18:00:24.013Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:00:26.010Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0\\n/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.5\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:00:37.030Z Script running with cell ID 76 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:00:43.112Z { "cell_id": "76", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:00:56.226Z Script completed Wall time 13.1 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.3051686 SESSION_ID=22732 ## Step 61 · agent · 2026-09-24T18:00:58.397Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:00:59.394Z { "code": "const r = await tools.write_stdin({session_id:22732,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:01:10.421Z Script running with cell ID 77 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:12.812Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:01:29.430Z Script completed Wall time 16.6 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.2756275 SESSION_ID=22732 ### exec · 2026-09-24T18:01:32.046Z { "code": "const r = await tools.write_stdin({session_id:22732,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:01:32.170Z Script completed Wall time 0.0 seconds Output: ## Step 62 · agent · 2026-09-24T18:01:34.873Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:36.842Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0_a*.parquet solution/validation_svd128_idf0.5_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:25000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T18:01:47.857Z Script running with cell ID 79 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:01:50.436Z { "cell_id": "79", "yield_time_ms": 30000, "max_tokens": 25000 } ### Tool output · 2026-09-24T18:01:50.547Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b487a3","wall_time_seconds":11.222323489,"exit_code":0,"original_token_count":498} Traceback (most recent call last): File "/task/evaluate_public.py", line 157, in main() File "/task/evaluate_public.py", line 152, in main ranked = load_submission(path, eligibility, targets) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/task/evaluate_public.py", line 113, in load_submission frame = validate_ranking(read_ranking(path), targets) ^^^^^^^^^^^^^^^^^^ File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet return impl.read( ^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read path_or_handle, handles, filesystem = _get_path_or_handle( ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle handles = get_handle( ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle handle = open(handle, ioargs.mode) ^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_svd128_idf0_a*.parquet' { "model": "solution/validation_svd128_idf0.5_a3.0.parquet", "scores": { "quality_ndcg@10": 0.031582, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.5_a4.0.parquet", "scores": { "quality_ndcg@10": 0.031217, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.5_a5.0.parquet", "scores": { "quality_ndcg@10": 0.030761, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.5_a6.0.parquet", "scores": { "quality_ndcg@10": 0.030432, "quality_users": 4263 } } ## Step 63 · agent · 2026-09-24T18:01:53.979Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:55.151Z { "code": "const r = await tools.exec_command({cmd:\"find solution -maxdepth 1 -name 'validation_svd128_idf0*' -printf '%f\\n' | sort\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:01:55.305Z Script completed Wall time 0.0 seconds Output: validation_svd128_idf0.0_a3.0.parquet validation_svd128_idf0.0_a4.0.parquet validation_svd128_idf0.0_a5.0.parquet validation_svd128_idf0.0_a6.0.parquet validation_svd128_idf0.25_a0.1.parquet validation_svd128_idf0.25_a0.25.parquet validation_svd128_idf0.25_a0.5.parquet validation_svd128_idf0.25_a0.parquet validation_svd128_idf0.25_a1.0.parquet validation_svd128_idf0.25_a1.5.parquet validation_svd128_idf0.25_a10.0.parquet validation_svd128_idf0.25_a15.0.parquet validation_svd128_idf0.25_a2.0.parquet validation_svd128_idf0.25_a3.0.parquet validation_svd128_idf0.25_a4.0.parquet validation_svd128_idf0.25_a5.0.parquet validation_svd128_idf0.25_a6.0.parquet validation_svd128_idf0.25_a7.5.parquet validation_svd128_idf0.5_a3.0.parquet validation_svd128_idf0.5_a4.0.parquet validation_svd128_idf0.5_a5.0.parquet validation_svd128_idf0.5_a6.0.parquet ## Step 64 · agent · 2026-09-24T18:01:57.172Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:01:58.630Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.0_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:15000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T18:02:08.804Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"1489dd","wall_time_seconds":9.989456329,"exit_code":0,"original_token_count":142} { "model": "solution/validation_svd128_idf0.0_a3.0.parquet", "scores": { "quality_ndcg@10": 0.028499, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.0_a4.0.parquet", "scores": { "quality_ndcg@10": 0.029113, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.0_a5.0.parquet", "scores": { "quality_ndcg@10": 0.029466, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.0_a6.0.parquet", "scores": { "quality_ndcg@10": 0.029411, "quality_users": 4263 } } ## Step 65 · agent · 2026-09-24T18:02:11.636Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:02:13.174Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.75\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:02:24.220Z Script running with cell ID 82 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:02:26.645Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-09-24T18:02:43.412Z Script completed Wall time 16.8 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.2650971 SESSION_ID=12056 ### exec · 2026-09-24T18:02:45.744Z { "code": "const r = await tools.write_stdin({session_id:12056,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:02:45.905Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T18:02:48.993Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.75_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:15000});text(rest);text(output)\n" } ### Tool output · 2026-09-24T18:02:59.123Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"7a2ba0","wall_time_seconds":9.909045421,"exit_code":0,"original_token_count":143} { "model": "solution/validation_svd128_idf0.75_a3.0.parquet", "scores": { "quality_ndcg@10": 0.033019, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.75_a4.0.parquet", "scores": { "quality_ndcg@10": 0.032594, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.75_a5.0.parquet", "scores": { "quality_ndcg@10": 0.031722, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.75_a6.0.parquet", "scores": { "quality_ndcg@10": 0.031, "quality_users": 4263 } } ## Step 66 · agent · 2026-09-24T18:03:02.311Z ### Thinking unavailable — no text recorded ### exec · 2026-09-24T18:03:03.890Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_svd.py\\n@@\\n- for alpha in [3.0,4.0,5.0,6.0]:\\n+ for alpha in [1.5,2.0,2.5,3.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T18:03:04.010Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T18:03:07.897Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 0.75\\n/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 1.0\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:03:18.920Z Script running with cell ID 86 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:03:20.952Z { "cell_id": "86", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:03:38.127Z Script completed Wall time 17.2 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.2650971 SESSION_ID=69523 ### exec · 2026-09-24T18:03:40.862Z { "code": "const r = await tools.write_stdin({session_id:69523,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:03:51.917Z Script running with cell ID 87 Wall time 11.1 seconds Output: ### wait · 2026-09-24T18:03:54.413Z { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:04:10.930Z Script completed Wall time 16.5 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.25656393 SESSION_ID=69523 ### exec · 2026-09-24T18:04:13.235Z { "code": "const r = await tools.write_stdin({session_id:69523,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:04:13.366Z Script completed Wall time 0.1 seconds Output: ### exec · 2026-09-24T18:04:19.602Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf0.75_a1.5.parquet solution/validation_svd128_idf0.75_a2.0.parquet solution/validation_svd128_idf0.75_a2.5.parquet solution/validation_svd128_idf1.0_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:25000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T18:04:30.618Z Script running with cell ID 89 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:04:32.726Z { "cell_id": "89", "yield_time_ms": 30000, "max_tokens": 25000 } ### Tool output · 2026-09-24T18:04:37.386Z Script completed Wall time 4.7 seconds Output: {"chunk_id":"8c0661","wall_time_seconds":17.565466646,"exit_code":0,"original_token_count":249} { "model": "solution/validation_svd128_idf0.75_a1.5.parquet", "scores": { "quality_ndcg@10": 0.028915, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.75_a2.0.parquet", "scores": { "quality_ndcg@10": 0.03081, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf0.75_a2.5.parquet", "scores": { "quality_ndcg@10": 0.032089, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf1.0_a1.5.parquet", "scores": { "quality_ndcg@10": 0.029285, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf1.0_a2.0.parquet", "scores": { "quality_ndcg@10": 0.031764, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf1.0_a2.5.parquet", "scores": { "quality_ndcg@10": 0.033309, "quality_users": 4263 } } { "model": "solution/validation_svd128_idf1.0_a3.0.parquet", "scores": { "quality_ndcg@10": 0.034446, "quality_users": 4263 } } ## Step 67 · agent · 2026-09-24T18:04:40.323Z ### Thinking unavailable — no text recorded Validation has now reached 0.034446. Stronger inverse-frequency weighting keeps improving the factor model, meaning niche shared tastes are carrying real signal; I’m doing one final local sweep around that regime, then I’ll refit on train+validation and validate the exact test artifact. ### exec · 2026-09-24T18:04:42.848Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_svd.py\\n@@\\n- for alpha in [1.5,2.0,2.5,3.0]:\\n+ for alpha in [3.0,3.5,4.0,5.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-24T18:04:42.937Z Script completed Wall time 0.0 seconds Output: {} ### exec · 2026-09-24T18:04:47.124Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 1.0\\n/opt/task-venv/bin/python -u solution/build_svd.py --stage validation --components 128 --idf-power 1.25\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:04:58.143Z Script running with cell ID 91 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:05:00.731Z { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:05:17.324Z Script completed Wall time 16.6 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.25656393 SESSION_ID=37736 ### exec · 2026-09-24T18:05:21.429Z { "code": "const r = await tools.write_stdin({session_id:37736,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-09-24T18:05:32.454Z Script running with cell ID 92 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:05:34.813Z { "cell_id": "92", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-09-24T18:05:51.516Z Script completed Wall time 16.7 seconds Output: matrix (9910, 30000) nnz 5687487 fitting SVD explained 0.24962488 SESSION_ID=37736 ### exec · 2026-09-24T18:05:53.734Z { "code": "const r = await tools.write_stdin({session_id:37736,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output)\n" } ### Tool output · 2026-09-24T18:05:53.862Z Script completed Wall time 0.0 seconds Output: ### exec · 2026-09-24T18:05:58.421Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"for f in solution/validation_svd128_idf1.0_a3.5.parquet solution/validation_svd128_idf1.0_a4.0.parquet solution/validation_svd128_idf1.0_a5.0.parquet solution/validation_svd128_idf1.25_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:25000});text(rest);text(output);if(rest.session_id)text(`SESSION_ID=${rest.session_id}`)\n" } ### Tool output · 2026-09-24T18:06:09.435Z Script running with cell ID 94 Wall time 11.0 seconds Output: ### wait · 2026-09-24T18:06:11.481Z { "cell_id": "94", "yield_time_ms": 30000, "max_tokens": 25000 } ### Tool output · Timestamp not recorded No response recorded.