{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "sol-high-0.147.0-exact-r03-2026-09-24", "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f", "source": { "format": "codex-rollout", "sha256": "bd20338415c646a2d89641d4cbe38853b9d9b7d58ec071802f4636ddc1709e8b", "records": 368, "bookkeepingRecords": { "task_started": 1, "user_message": 1, "agent_message": 11, "token_count": 85, "patch_apply_end": 19, "task_complete": 1 } }, "sessionId": "01a0d47e-668b-7612-a3fc-eff74959dc0c", "agentVersion": "0.147.0", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-24T17:37:28.744Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:28.744Z", "text": "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.\n\n# Personality\n\nAs 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.\n\nYou 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.\n\nConversations 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.\n\n## Writing style\n\nAvoid 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.\n\nIf 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.\n\n## Technical communication\n\nLead 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.\n\nYou 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.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nThe 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.\n\nWhen 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.\n\n## Intermediate commentary\n\nAs 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.\n\nIf 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.\n\nDo 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.\n\nNever 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 \".\n\n## Final answer\n\nIn 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.\n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\n### Visualizations\n\nUse 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.\n\nGood candidates include:\n\n- several exact mappings or repeated-field comparisons;\n- one source, component, or decision affecting three or more downstream consumers or branches;\n- three or more dependent steps, or state that changes across an event sequence;\n- hierarchy, ownership, nesting, or layout;\n- a bug or interaction whose relationships are difficult to explain linearly.\n\nPrefer 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.\n\nUsually 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.\n\n# Rules for getting work done\n\n- 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.\n- When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.\n- 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.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n\n## File editing constraints\n\nUse `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.\n\nYou 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.\n\nNever 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.\n\n## Autonomy and persistence\n\nAdapt accordingly based on the user’s request type. When asked to:\n\n- 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.\n- 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.\n- 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.\n- 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.\n\nYou avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances:\na) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope.\nb) 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).\n\nA 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.\n\nYou 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.\n\nWhen 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.\n\nIf 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.\n\n# Destructive actions\n\nBe cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.\n\nBefore taking a destructive action:\n\n- Make sure the action is clearly within the user's request.\n- Resolve the exact targets with read-only checks when necessary.\n- Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command.\n- When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.\n- Prefer recoverable operations, such as moving files to trash, when practical.\n- If the target or scope is unclear, stop and ask the user.\n\nNever run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.\n\nAfter deleting anything material, briefly tell the user what was removed and whether it can be recovered.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.\n\n### How to use skills\n\n- Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n- Trigger rules: If the user names an available skill (with `$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.\n- 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.\n- How to use a skill:\n 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.\n 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.\n 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.\n 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.\n 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist).\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skills you're using and why. If you skip an obvious skill, say why.\n- Context hygiene:\n - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked.\n - When variants exist, select only the relevant references and note the choice.\n- Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.\n\nWhen 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.\n\nExplicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work.\n\nWhen using a skill the user did not explicitly name, follow this procedure:\n\n- First, tell the user in the commentary channel **why** you are using the skill.\n- Then, use the skill as long as it stays within the scope of the task.\n- 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).\n\nIf 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.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-24T17:37:30.404Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "\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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn 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.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-24T17:37:30.404Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll 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.\n\nYou 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.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote 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.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-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." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-24T17:37:30.404Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "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." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-24T17:37:30.404Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n- GitHub (github@openai-curated-remote)\n- Gmail (gmail@openai-curated-remote)\n- Google Calendar (google-calendar@openai-curated-remote)\n- Google Drive (google-drive@openai-curated-remote)\n- Linear (linear@openai-curated-remote)\n- Notion (notion@openai-curated-remote)\n- OpenAI Developers (openai-developers@openai-curated-remote)\n- Outlook Calendar (outlook-calendar@openai-curated-remote)\n- Outlook Email (outlook-email@openai-curated-remote)\n- SharePoint (sharepoint@openai-curated-remote)\n- Slack (slack@openai-curated-remote)\n- Teams (teams@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-24T17:37:30.404Z", "text": "\n /app\n sh\n 2026-09-24\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-24T17:37:30.405Z", "data": { "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 } } } }, { "kind": "context", "timestamp": "2026-09-24T17:37:30.405Z", "data": { "type": "turn_context", "turn_id": "01a0d47e-669f-7b12-8df6-5b6d65fdd1a5", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-24T17:37:30.414Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T17:37:30.414Z", "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-24T17:37:32.361Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:32.361Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:37:33.649Z", "text": "I’ll first profile the data, eligibility density, and public-window signal, then build a validation-safe ensemble, tune it against the provided evaluator, and refit the winning recipe on train+validation. I’ll keep a structurally valid fallback submission saved early, then overwrite it only after checks improve." }, { "kind": "tool", "timestamp": "2026-09-24T17:37:36.877Z", "callId": "call_WSRn9TJGwXNuIi9hLaqkSupa", "name": "exec", "input": { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data schemas, scale, time ranges, and candidate/eligibility structure\",\"status\":\"in_progress\"},\n {step:\"Build a valid baseline and validation feature pipeline\",\"status\":\"pending\"},\n {step:\"Tune ranking ensemble on the public temporal window\",\"status\":\"pending\"},\n {step:\"Refit on train+validation and generate final test ranking\",\"status\":\"pending\"},\n {step:\"Validate deliverables and document reproduction\",\"status\":\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,220p' /task/requirements.txt\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:37:36.945Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\n/task/requirements.txt\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/train_listens.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-24T17:37:38.930Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:38.930Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:37:43.977Z", "callId": "call_o2FQhPEYnHOsMmNWJnem7r7n", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n print(os.path.basename(f), os.path.getsize(f)/1e6, 'MB')\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(' ',pf.metadata.num_rows, pf.schema_arrow)\\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\\n z=np.load(f)\\n print(os.path.basename(f), {k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog same head',z['item_ids'][:10])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:37:44.090Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-24T17:37:45.799Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:45.799Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:37:47.539Z", "callId": "call_GScPAUtV8bw6nyTEH0flGaxq", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; python --version; python3 --version; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | sed -n '1,40p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:37:47.606Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\nPython 3.12.13\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 17:34 .\ndrwxr-xr-x 1 root root 4096 Sep 24 17:37 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 17:30 data\n-r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 17:30 requirements.txt\n", "isError": null } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-24T17:37:49.305Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:49.305Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:37:50.456Z", "callId": "call_YHexFHvL7FNQbbM4pvakbbmo", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"python -m pip list | sed -n '1,160p'; env | sort | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:37:51.215Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\nPackage Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_THREAD_ID=01a0d47e-668b-7612-a3fc-eff74959dc0c\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=63ea770e5a15\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/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\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "isError": null } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-24T17:37:53.102Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:53.102Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:37:56.272Z", "callId": "call_9C41PjeMNbejrgyQrqbzEpSv", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name 'site-packages' -o -name 'dist-packages' \\\\) 2>/dev/null | sed -n '1,100p'; find / -type f -name 'pyarrow*.so' 2>/dev/null | sed -n '1,40p'\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:37:57.049Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/usr/lib/python3/dist-packages\n/usr/local/lib/python3.11/dist-packages\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n", "isError": null } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-24T17:37:58.445Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:37:58.445Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:37:59.263Z", "text": "The default shell Python is intentionally bare; the task’s pinned environment is available separately, so I’m switching to that interpreter. No package installation or network access is needed." }, { "kind": "tool", "timestamp": "2026-09-24T17:38:04.312Z", "callId": "call_7xWgfP4JO8UnRefEUN4NyJL3", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os, glob, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n print(os.path.basename(f), round(os.path.getsize(f)/1e6,2), 'MB')\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(' ',pf.metadata.num_rows, pf.schema_arrow.names)\\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\\n z=np.load(f)\\n print(os.path.basename(f), {k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog head',z['item_ids'][:10])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:38:05.509Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\ncandidate_catalog.parquet 0.35 MB\n 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 0.08 MB\n 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_eligible_candidates.npz 7.74 MB\ntest_preference_state.parquet 1.09 MB\n 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 0.86 MB\n 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 5.37 MB\n 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 319.18 MB\n 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 0.16 MB\n 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 1.86 MB\n 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 0.04 MB\n 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 0.3 MB\n 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 9.95 MB\n 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 0.01 MB\n 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 0.07 MB\n 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_eligible_candidates.npz 7.57 MB\nvalidation_preference_state.parquet 1.06 MB\n 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24657 [28038.5 29242. 29852. ] 30000 29067.306291390727 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24552 [28001. 29220. 29835.] 30000 29041.3178807947 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\n", "isError": null } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-24T17:38:08.985Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:38:08.985Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:38:10.219Z", "callId": "call_k3WF7vpj79QrdPJMZelXIJCJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,320p' /task/ranking_evaluation.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:38:10.283Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "isError": null } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-24T17:38:21.354Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:38:21.354Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:38:27.779Z", "callId": "call_rvkcgMBuWfLVWWAeeXPOdk6T", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build cutoff-valid catalog-popularity rankings for both stages.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def rank_from_npz(name: str, output: str) -> None:\\n+ with np.load(DATA / name, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[r], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[eligible][:100]\\n+ rows.append(pd.DataFrame({\\n+ \\\"uid\\\": uid,\\n+ \\\"item_id\\\": chosen,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }))\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ rank_from_npz(\\\"validation_eligible_candidates.npz\\\", \\\"validation_baseline.parquet\\\")\\n+ rank_from_npz(\\\"test_eligible_candidates.npz\\\", \\\"submission.parquet\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:38:27.819Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-24T17:38:29.396Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:38:29.396Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:38:31.580Z", "callId": "call_ox0xiyqGxD6LDjSnZm4nOcow", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/build_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_baseline.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:38:37.748Z", "content": "Script completed\nWall time 6.2 seconds\nOutput:\n\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 17:38 .\ndrwxr-xr-x 1 agent agent 4096 Sep 24 17:34 ..\n-rw-r--r-- 1 agent agent 1163 Sep 24 17:38 build_baseline.py\n{\n \"model\": \"/app/solution/validation_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-24T17:38:48.679Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:38:48.679Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:38:49.759Z", "text": "The first valid fallback is saved. Plain catalog popularity scores `0.010710` NDCG@10 over 4,263 scored users, which leaves substantial room. I’m now measuring three likely sources of lift separately: repeat affinity, recent item momentum, and collaborative similarity." }, { "kind": "tool", "timestamp": "2026-09-24T17:39:10.145Z", "callId": "call_xFS316wk3mcRLQSsy18CUcTs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/analyze.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D=Path('/task/data')\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+catalog=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+tmap=pd.Index(targets)\\n+imap=pd.Index(catalog)\\n+\\n+def ranges(path, col='timestamp'):\\n+ pf=pq.ParquetFile(path)\\n+ vals=[]\\n+ for i in range(pf.num_row_groups):\\n+ s=pf.metadata.row_group(i).column(pf.schema_arrow.get_field_index(col)).statistics\\n+ vals += [s.min,s.max]\\n+ return min(vals),max(vals)\\n+\\n+for stem in ['train_listens','train_likes','val_listens','val_likes']:\\n+ print(stem, ranges(D/(stem+'.parquet')))\\n+\\n+# validation truth in compact integer form\\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)]\\n+vk=vk[vk.is_organic==1]\\n+lab=pd.concat([vl[['uid','item_id']].assign(grade=1),vk[['uid','item_id']].assign(grade=3)])\\n+lab=lab[lab.uid.isin(targets)&lab.item_id.isin(catalog)].groupby(['uid','item_id'],as_index=False).grade.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\\n+ok=[bool((int(p[ur[int(u)],ic[int(i)]//8])>>(ic[int(i)]%8))&1) for u,i in lab[['uid','item_id']].itertuples(index=False,name=None)]\\n+lab=lab[np.asarray(ok)]\\n+print('labels',len(lab),'users',lab.uid.nunique(),'grade',lab.grade.value_counts().to_dict(),'items',lab.item_id.nunique())\\n+print('label catalog ranks',np.quantile(imap.get_indexer(lab.item_id),[0,.1,.25,.5,.75,.9,.99,1]))\\n+\\n+# Target-user candidate positive history pairs, streamed.\\n+pair_parts=[]; event_parts=[]\\n+pf=pq.ParquetFile(D/'train_listens.parquet')\\n+for bi,b in enumerate(pf.iter_batches(batch_size=1_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\\n+ x=b.to_pandas()\\n+ r=tmap.get_indexer(x.uid); c=imap.get_indexer(x.item_id)\\n+ m=(r>=0)&(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\\n+ if m.any():\\n+ xx=pd.DataFrame({'r':r[m].astype(np.int32),'c':c[m].astype(np.int32),'ts':x.timestamp.to_numpy()[m],\\n+ 'organic':x.is_organic.to_numpy()[m].astype(np.int8),'ratio':x.played_ratio_pct.to_numpy()[m].astype(np.float32)})\\n+ pair_parts.append(xx.groupby(['r','c'],as_index=False).agg(n=('ts','size'),last=('ts','max'),organic=('organic','sum'),ratio=('ratio','max')))\\n+ if bi%10==0: print('batch',bi,'parts rows',sum(map(len,pair_parts)))\\n+hist=pd.concat(pair_parts).groupby(['r','c'],as_index=False).agg(n=('n','sum'),last=('last','max'),organic=('organic','sum'),ratio=('ratio','max'))\\n+hist['uid']=targets[hist.r]; hist['item_id']=catalog[hist.c]\\n+hist.to_parquet('/app/solution/target_history.parquet',index=False)\\n+labkey=pd.MultiIndex.from_frame(lab[['uid','item_id']])\\n+hkey=pd.MultiIndex.from_frame(hist[['uid','item_id']])\\n+print('history pairs',len(hist),'events',hist.n.sum(),'label repeat',labkey.isin(hkey).mean())\\n+matched=lab[labkey.isin(hkey)].merge(hist,on=['uid','item_id'])\\n+print('repeat label stats',matched[['n','last','organic','ratio']].describe(percentiles=[.1,.5,.9,.99]))\\n+print('labels/user',lab.groupby('uid').size().describe())\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:39:10.193Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:39:12.732Z", "callId": "call_K8YzY3riItt5S8WButP3LKNn", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/analyze.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:39:20.610Z", "content": "Script completed\nWall time 7.9 seconds\nOutput:\n\ntrain_listens (0, 24786795)\ntrain_likes (80, 24786790)\nval_listens (24788600, 25393395)\nval_likes (24788815, 25393390)\nlabels 37769 users 4263 grade {1: 33352, 3: 4417} items 14124\nlabel catalog ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ]\nbatch 0 parts rows 131932\nbatch 10 parts rows 1390687\nbatch 20 parts rows 2665765\nbatch 30 parts rows 3928994\nbatch 40 parts rows 5209799\nhistory pairs 5514505 events 19959853 label repeat 0.0\nrepeat label stats n last organic ratio\ncount 0.0 0.0 0.0 0.0\nmean NaN NaN NaN NaN\nstd NaN NaN NaN NaN\nmin NaN NaN NaN NaN\n10% NaN NaN NaN NaN\n50% NaN NaN NaN NaN\n90% NaN NaN NaN NaN\n99% NaN NaN NaN NaN\nmax NaN NaN NaN NaN\nlabels/user count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\nmax 442.000000\ndtype: float64\n", "isError": null } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-24T17:39:31.435Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:39:31.435Z", "text": "" } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-24T17:39:40.501Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:39:40.501Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:39:48.778Z", "callId": "call_oIgq1uxbLFlxrwsoMMRhfosR", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_global.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+h=pd.read_parquet(O/'target_history.parquet')\\n+g=h.groupby('c').agg(users=('r','size'),events=('n','sum'),organic=('organic','sum'),last=('last','max'))\\n+for days in [3,7,14,30,60,90]:\\n+ g[f'u{days}']=(h['last'] >= CUT-days*86400).groupby(h.c).sum()\\n+g=g.reindex(np.arange(len(cat)),fill_value=0)\\n+features={\\n+ 'target_users':g.users.to_numpy(),\\n+ 'target_events':g.events.to_numpy(),\\n+ 'target_organic':g.organic.to_numpy(),\\n+ 'recent3':g.u3.to_numpy(), 'recent7':g.u7.to_numpy(), 'recent14':g.u14.to_numpy(),\\n+ 'recent30':g.u30.to_numpy(), 'recent60':g.u60.to_numpy(), 'recent90':g.u90.to_numpy(),\\n+}\\n+# popularity rank prior as small tie-breaker\\n+for name,x in features.items():\\n+ order=np.lexsort((np.arange(len(x)), -x))\\n+ with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids']; items=z['item_ids']; p=z['packed_eligible']\\n+ out=[]\\n+ for r,u in enumerate(uids):\\n+ mask=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\\n+ pick=order[mask[order]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\\n+ pd.concat(out,ignore_index=True).to_parquet(O/f'validation_global_{name}.parquet',index=False)\\n+ print(name, x.max(), np.count_nonzero(x))\\n+g.to_parquet(O/'global_features.parquet')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:39:48.800Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:39:53.050Z", "callId": "call_QaTxxnwroT4Q0MQL0BEK7M09", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/tune_global.py >/app/solution/global_build.log && for f in /app/solution/validation_global_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:40:04.065Z", "content": "Script running with cell ID 13\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-24T17:40:05.533Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:40:05.533Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:40:05.961Z", "callId": "call_NF7aGtv2JOu6qaVGyGJzuloI", "name": "wait", "input": { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:40:23.226Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n{ \"model\": \"/app/solution/validation_global_recent14.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019299, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_recent3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.024131, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_recent30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016953, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_recent60.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015001, \"quality_users\": 4263 } } \nSESSION_ID=54524", "isError": null } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-24T17:40:26.163Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:40:26.163Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:40:26.874Z", "callId": "call_P8zZk5W7E0RAWV7kDZ7aTEhr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:54524,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:40:34.204Z", "content": "Script completed\nWall time 7.3 seconds\nOutput:\n\n{ \"model\": \"/app/solution/validation_global_recent7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.02466, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_recent90.parquet\", \"scores\": { \"quality_ndcg@10\": 0.014074, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_target_events.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010769, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_target_organic.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010582, \"quality_users\": 4263 } } { \"model\": \"/app/solution/validation_global_target_users.parquet\", \"scores\": { \"quality_ndcg@10\": 0.011037, \"quality_users\": 4263 } } ", "isError": null } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-24T17:40:45.851Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:40:45.851Z", "text": "" } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-24T17:40:53.189Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:40:53.189Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:40:54.480Z", "text": "A clear temporal pattern emerged: popularity among target users in only the last seven training days reaches `0.024660`, more than doubling the catalog baseline; older/global popularity is much weaker. I’m using that seven-day momentum as the ensemble prior while fitting latent user–track affinity from 5.5 million cutoff-safe history pairs." }, { "kind": "tool", "timestamp": "2026-09-24T17:41:14.612Z", "callId": "call_5m9pXzduRm6WHpkDMSBWgGNK", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_svd.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fit latent implicit-feedback models and emit validation blend variants.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse, time\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--mode',default='bm25'); ap.add_argument('--rank',type=int,default=96)\\n+ a=ap.parse_args(); t=time.time()\\n+ h=pd.read_parquet(O/'target_history.parquet')\\n+ n_users=int(h.r.max())+1; n_items=30000\\n+ rows=h.r.to_numpy(np.int32); cols=h.c.to_numpy(np.int32)\\n+ age=(CUT-h['last'].to_numpy())/86400\\n+ if a.mode=='binary':\\n+ vals=np.ones(len(h),np.float32)\\n+ elif a.mode=='count':\\n+ vals=(1+np.log1p(h.n.to_numpy())).astype(np.float32)\\n+ elif a.mode=='recent':\\n+ vals=((1+np.log1p(h.n.to_numpy()))*(0.35+0.65*np.exp(-age/120))).astype(np.float32)\\n+ elif a.mode=='organic':\\n+ vals=(1+np.log1p(h.n.to_numpy())+0.5*np.log1p(h.organic.to_numpy())).astype(np.float32)\\n+ else:\\n+ vals=np.ones(len(h),np.float32)\\n+ x=sparse.csr_matrix((vals,(rows,cols)),shape=(n_users,n_items),dtype=np.float32)\\n+ df=np.diff(x.tocsc().indptr).astype(np.float32)\\n+ if a.mode in ('bm25','tfidf'):\\n+ idf=np.log((n_users+1)/(df+1)).astype(np.float32)\\n+ x=x.multiply(idf).tocsr()\\n+ norm=np.sqrt(np.asarray(x.multiply(x).sum(1)).ravel()).astype(np.float32)\\n+ x=sparse.diags(1/np.maximum(norm,1e-6))@x\\n+ print('matrix',x.shape,x.nnz,'build sec',time.time()-t,flush=True)\\n+ svd=TruncatedSVD(n_components=a.rank,n_iter=5,random_state=2026)\\n+ user=svd.fit_transform(x).astype(np.float32); comp=svd.components_.astype(np.float32)\\n+ print('fit sec',time.time()-t,'explained',svd.explained_variance_ratio_.sum(),flush=True)\\n+ gf=pd.read_parquet(O/'global_features.parquet')\\n+ gp=np.log1p(gf.u7.to_numpy()).astype(np.float32)\\n+ gp=(gp-gp.mean())/gp.std()\\n+ alphas=[0,.03,.06,.1,.16,.25,.4,.65,1.0]\\n+ outs={z:[] for z in alphas}\\n+ with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\n+ bs=128\\n+ for st in range(0,n_users,bs):\\n+ en=min(st+bs,n_users)\\n+ s=user[st:en]@comp\\n+ # Scale latent scores per listener so alpha is comparable.\\n+ s=(s-s.mean(1,keepdims=True))/np.maximum(s.std(1,keepdims=True),1e-6)\\n+ eligible=np.unpackbits(packed[st:en],axis=1,count=n_items,bitorder='little').astype(bool)\\n+ for alpha in alphas:\\n+ q=s+alpha*gp[None,:]\\n+ q[~eligible]=-np.inf\\n+ ix=np.argpartition(q,-100,axis=1)[:,-100:]\\n+ vv=np.take_along_axis(q,ix,axis=1)\\n+ ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):\\n+ outs[alpha].append(pd.DataFrame({'uid':uids[st+j],'item_id':items[ix[j]],'rank':np.arange(1,101)}))\\n+ if st%1024==0: print('rank',st,flush=True)\\n+ for alpha,parts in outs.items():\\n+ pd.concat(parts,ignore_index=True).to_parquet(O/f'validation_svd_{a.mode}_r{a.rank}_a{alpha}.parquet',index=False)\\n+ np.savez_compressed(O/f'svd_{a.mode}_r{a.rank}.npz',user=user,components=comp)\\n+ print('total sec',time.time()-t,flush=True)\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:41:14.664Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:41:20.615Z", "callId": "call_UvhsAoWyYFq4nvb3bS4Jmcv6", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode tfidf --rank 96\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:41:31.632Z", "content": "Script running with cell ID 16\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-24T17:41:33.453Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:41:33.453Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:41:33.859Z", "callId": "call_4w8LSAQeILcf4zmfmwnaIUg1", "name": "wait", "input": { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:41:45.593Z", "content": "Script completed\nWall time 11.7 seconds\nOutput:\n\nmatrix (8456, 30000) 5514505 build sec 0.3065223693847656\r\nfit sec 2.341540575027466 explained 0.18627316\r\nrank 0\r\nrank 1024\r\nrank 2048\r\nrank 3072\r\nrank 4096\r\nrank 5120\r\nrank 6144\r\nrank 7168\r\nrank 8192\r\ntotal sec 23.16431212425232\r\n", "isError": null } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-24T17:41:48.826Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:41:48.826Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:41:50.752Z", "callId": "call_KHfnQ9lrvnzEktOvEg5dwVuv", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find /app/solution -maxdepth 1 -name 'validation_svd_tfidf_r96_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:41:59.502Z", "content": "Script completed\nWall time 8.7 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.16.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020148,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.06.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019912,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020425,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.65.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022179,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019989,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019833,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01962,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021004,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_tfidf_r96_a1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023927,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-24T17:42:01.619Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:42:01.619Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:42:03.395Z", "callId": "call_LdyApfC3VU3xdxogU2kt4su1", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 128\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:42:14.410Z", "content": "Script running with cell ID 18\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:42:16.370Z", "callId": "call_Th6nixsR73w44M6Fap7QSTdF", "name": "wait", "input": { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:42:28.856Z", "content": "Script completed\nWall time 12.5 seconds\nOutput:\n\nmatrix (8456, 30000) 5514505 build sec 0.22640752792358398\r\nfit sec 2.741776943206787 explained 0.34042275\r\nrank 0\r\nrank 1024\r\nrank 2048\r\nrank 3072\r\nrank 4096\r\nrank 5120\r\nrank 6144\r\nrank 7168\r\nrank 8192\r\ntotal sec 23.73560380935669\r\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:42:33.923Z", "callId": "call_6UP40Pyvrok54S4Yrc4bjMNf", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find /app/solution -maxdepth 1 -name 'validation_svd_binary_r128_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T17:42:42.456Z", "content": "Script completed\nWall time 8.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.16.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020152,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022085,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02018,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.06.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019771,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019923,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.65.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021227,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019398,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020411,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd_binary_r128_a0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019549,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-24T17:42:52.377Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:42:52.377Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:42:53.504Z", "text": "The latent model is useful but the unpersonalized seven-day trend still wins; TF-IDF SVD alone scores `0.019620`, and its best tested blend reaches `0.023927`. That suggests near-term discovery demand is dominated by item momentum. I’m tightening the prior to the exact objective—organic ≥80% listens plus organic likes—across shorter lookback windows before deciding how much personalization to retain." }, { "kind": "tool", "timestamp": "2026-09-24T17:43:10.466Z", "callId": "call_8Y8IJCStfQvBB12DpPLGAZKJ", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_recent.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune cutoff-safe popularity features that mirror the discovery label.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); imap=pd.Index(items)\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); tset=set(targets.tolist())\\n+start=CUT-35*86400\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic'])\\n+li=li[li.item_id.isin(items)]; la=la[la.item_id.isin(items)]\\n+li['day']=(CUT-li.timestamp)//86400; la['day']=(CUT-la.timestamp)//86400\\n+print('recent rows',len(li),len(la),li.day.min(),li.day.max())\\n+features={}\\n+for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\\n+ for scope,tm in [('t',li.uid.isin(tset)),('a',np.ones(len(li),bool))]:\\n+ for typ,cm in [('o80',(li.is_organic==1)&(li.played_ratio_pct>=80)),\\n+ ('o50',(li.is_organic==1)&(li.played_ratio_pct>=50)),\\n+ ('x80',(li.played_ratio_pct>=80)),('x50',(li.played_ratio_pct>=50))]:\\n+ q=li[(li.day=80)][['uid','item_id']].assign(grade=1),\\n+ vk[vk.is_organic==1][['uid','item_id']].assign(grade=3)])\\n+truth=truth[truth.uid.isin(tset)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).grade.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as zz:\\n+ euids=zz['uids'].copy(); ep=zz['packed_eligible'].copy(); eitems=zz['item_ids'].copy()\\n+ur={int(u):r for r,u in enumerate(euids)}; ic={int(i):c for c,i in enumerate(eitems)}\\n+gold={}\\n+for (u,i),grade in truth.items():\\n+ r=ur[int(u)]; c=ic[int(i)]\\n+ if (int(ep[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(grade)\\n+\\n+best=(-1,None,None)\\n+def write_rank(name,score):\\n+ global best\\n order=np.lexsort((np.arange(len(score)), -score))\\n- with np.load(D/'validation_eligible_candidates.npz') as z:\\n- out=[]\\n- for r,u in enumerate(z['uids']):\\n- m=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\\n- ix=order[m[order]][:100]\\n- out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][ix],'rank':np.arange(1,101)}))\\n- pd.concat(out,ignore_index=True).to_parquet(O/f'validation_recent_{name}.parquet',index=False)\\n+ nd=[]\\n+ disc=1/np.log2(np.arange(2,12))\\n+ for u,g in gold.items():\\n+ r=ur[u]\\n+ chosen=[]\\n+ for c in order:\\n+ if (int(ep[r,c//8])>>(c%8))&1:\\n+ chosen.append(int(eitems[c]))\\n+ if len(chosen)==10: break\\n+ gains=np.array([2**g.get(i,0)-1 for i in chosen])\\n+ ideal=np.array(sorted((2**v-1 for v in g.values()),reverse=True)[:10])\\n+ nd.append((gains*disc).sum()/(ideal*disc[:len(ideal)]).sum())\\n+ val=float(np.mean(nd)); print(name,round(val,6),flush=True)\\n+ if val>best[0]: best=(val,name,order.copy())\\n@@\\n np.savez_compressed(O/'recent_features.npz',**features)\\n+print('BEST',best[0],best[1])\\n+order=best[2]; out=[]\\n+for r,u in enumerate(euids):\\n+ m=np.unpackbits(ep[r],count=len(items),bitorder='little').astype(bool)\\n+ ix=order[m[order]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_recent_best.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:43:32.724Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-24T17:43:34.576Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:43:34.576Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:43:36.168Z", "callId": "call_dxsdNs8Uc0MNYcJ4dvQ7kFf4", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:43:47.182Z", "content": "Script running with cell ID 22\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:43:49.063Z", "callId": "call_i76VDsPE6AXqq9MTokjLvjwI", "name": "wait", "input": { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 30000 }, "result": { "timestamp": "2026-09-24T17:44:01.417Z", "content": "Script completed\nWall time 12.4 seconds\nOutput:\n\nrecent rows 5037944 94894 0 35\r\nto80_1 0.032906\r\nto50_1 0.033452\r\ntx80_1 0.027216\r\nao80_1 0.032991\r\ngrade_1_w1 0.033949\r\ngrade_1_w3 0.034619\r\ngrade_1_w7 0.034226\r\nto80_2 0.032848\r\nto50_2 0.03283\r\ntx80_2 0.025839\r\nao80_2 0.032849\r\ngrade_2_w1 0.032905\r\ngrade_2_w3 0.032931\r\ngrade_2_w7 0.033914\r\nto80_3 0.031497\r\nto50_3 0.031293\r\ntx80_3 0.023418\r\nao80_3 0.031654\r\ngrade_3_w1 0.031443\r\ngrade_3_w3 0.033033\r\ngrade_3_w7 0.033901\r\nto80_4 0.031912\r\nto50_4 0.031605\r\ntx80_4 0.0223\r\nao80_4 0.031387\r\ngrade_4_w1 0.032051\r\ngrade_4_w3 0.033273\r\ngrade_4_w7 0.033985\r\nto80_5 0.030616\r\nto50_5 0.030861\r\ntx80_5 0.023153\r\nao80_5 0.030652\r\ngrade_5_w1 0.031414\r\ngrade_5_w3 0.032279\r\ngrade_5_w7 0.033217\r\nto80_6 0.030494\r\nto50_6 0.030812\r\ntx80_6 0.022576\r\nao80_6 0.030407\r\ngrade_6_w1 0.031421\r\ngrade_6_w3 0.032039\r\ngrade_6_w7 0.033831\r\nto80_7 0.030288\r\nto50_7 0.031196\r\ntx80_7 0.022852\r\nao80_7 0.030286\r\ngrade_7_w1 0.031402\r\ngrade_7_w3 0.031467\r\ngrade_7_w7 0.031983\r\nto80_8 0.029116\r\nto50_8 0.029966\r\ntx80_8 0.02185\r\nao80_8 0.02904\r\ngrade_8_w1 0.030797\r\ngrade_8_w3 0.031843\r\ngrade_8_w7 0.03242\r\nto80_10 0.028041\r\nto50_10 0.028148\r\ntx80_10 0.020168\r\nao80_10 0.028248\r\ngrade_10_w1 0.029015\r\ngrade_10_w3 0.031529\r\ngrade_10_w7 0.032188\r\nto80_12 0.027358\r\nto50_12 0.027829\r\ntx80_12 0.019644\r\nao80_12 0.027402\r\ngrade_12_w1 0.028556\r\ngrade_12_w3 0.029499\r\ngrade_12_w7 0.031744\r\nto80_14 0.025493\r\nto50_14 0.025983\r\ntx80_14 0.019168\r\nao80_14 0.025481\r\ngrade_14_w1 0.026943\r\ngrade_14_w3 0.027997\r\ngrade_14_w7 0.029309\r\nto80_18 0.022502\r\nto50_18 0.02305\r\ntx80_18 0.019326\r\nao80_18 0.022345\r\ngrade_18_w1 0.023931\r\ngrade_18_w3 0.026165\r\ngrade_18_w7 0.028175\r\nto80_21 0.020376\r\nto50_21 0.02067\r\ntx80_21 0.017744\r\nao80_21 0.020384\r\ngrade_21_w1 0.021564\r\ngrade_21_w3 0.023368\r\ngrade_21_w7 0.024848\r\nto80_28 0.019033\r\nto50_28 0.018943\r\ntx80_28 0.017123\r\nao80_28 0.019032\r\ngrade_28_w1 0.019478\r\ngrade_28_w3 0.020093\r\ngrade_28_w7 0.020607\r\nto80_35 0.01867\r\nto50_35 0.018652\r\ntx80_35 0.016402\r\nao80_35 0.018672\r\ngrade_35_w1 0.019267\r\ngrade_35_w3 0.019516\r\ngrade_35_w7 0.020098\r\nBEST 0.0346188748576378 grade_1_w3\r\n", "isError": null } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-24T17:44:04.623Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:44:04.623Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:44:05.651Z", "callId": "call_seoEr0jd0lXwJrWr7PxBOJDc", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:44:08.239Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_recent_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034619,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-24T17:44:20.089Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:44:20.089Z", "text": "" } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-24T17:44:23.619Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:44:23.619Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:44:25.538Z", "text": "Matching the target definition changed the picture sharply: one-day target-cohort organic activity, with organic likes weighted 3×, scores `0.034619`—a 223% lift over the fallback and 40% over the earlier seven-day proxy. I’m now optimizing tie-breaking and decay around that signal; integer count ties are common, so secondary momentum can matter materially without disturbing the strongest primary ordering." }, { "kind": "tool", "timestamp": "2026-09-24T17:44:46.808Z", "callId": "call_uoEChJ3e2hUtUPsGlNPsV1Hf", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_blends.py\\n+#!/usr/bin/env python3\\n+from pathlib import Path\\n+import numpy as np, pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+with np.load(O/'recent_features.npz') as z: F={k:z[k] for k in z.files}\\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ uids=z['uids'].copy(); packed=z['packed_eligible'].copy(); eitems=z['item_ids'].copy()\\n+ur={int(u):r for r,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(eitems)}\\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\n+truth=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\\n+truth=truth[truth.uid.isin(targets)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\\n+gold={}\\n+for (u,i),g in truth.items():\\n+ r=ur[int(u)]; c=ic[int(i)]\\n+ if (int(packed[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(g)\\n+disc=1/np.log2(np.arange(2,12))\\n+def evaluate(score):\\n+ order=np.lexsort((np.arange(len(score)),-score)); vals=[]\\n+ for u,g in gold.items():\\n+ r=ur[u]; chosen=[]\\n+ for c in order:\\n+ if (int(packed[r,c//8])>>(c%8))&1:\\n+ chosen.append(int(eitems[c]))\\n+ if len(chosen)==10: break\\n+ gain=np.array([2**g.get(i,0)-1 for i in chosen]); ideal=np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])\\n+ vals.append((gain*disc).sum()/(ideal*disc[:len(ideal)]).sum())\\n+ return np.mean(vals),order\\n+def scaled(x):\\n+ x=np.log1p(x.astype(float)); return (x-x.min())/(x.max()-x.min()+1e-9)\\n+best=(-1,'',None)\\n+secondaries={\\n+ 'cat':np.linspace(1,0,len(items)),\\n+ 'to50_1':scaled(F['to50_1']), 'ao80_1':scaled(F['ao80_1']),\\n+ 'g2':scaled(F['to80_2']+3*F['tlike_2']), 'g3':scaled(F['to80_3']+3*F['tlike_3']),\\n+ 'g7':scaled(F['to80_7']+3*F['tlike_7']), 'o7':scaled(F['to80_7']),\\n+}\\n+for w in [1.5,2,2.5,3,3.5,4,5,6,7]:\\n+ p=F['to80_1']+w*F['tlike_1']\\n+ for sn,s in secondaries.items():\\n+ for eps in [.01,.05,.15,.3,.49]:\\n+ val,order=evaluate(p+eps*s)\\n+ name=f'w{w}_{sn}_e{eps}'\\n+ print(name,round(val,6),flush=True)\\n+ if val>best[0]:best=(val,name,order.copy())\\n+# multi-level lexical: day-1 primary, day-2 then catalog.\\n+for w in [2,2.5,3,3.5,4,5]:\\n+ p=F['to80_1']+w*F['tlike_1']\\n+ for a in [.05,.15,.3,.49]:\\n+ score=p+a*scaled(F['to80_2']+w*F['tlike_2'])+.01*secondaries['cat']\\n+ val,order=evaluate(score); name=f'multi_w{w}_a{a}'; print(name,round(val,6),flush=True)\\n+ if val>best[0]:best=(val,name,order.copy())\\n+print('BEST',best[0],best[1])\\n+out=[]\\n+for r,u in enumerate(uids):\\n+ m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); ix=best[2][m[best[2]]][:100]\\n+ out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_blend_best.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:44:46.878Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-24T17:44:49.076Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:44:49.076Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:44:51.213Z", "callId": "call_JoQQIwWhCJ6DW7iRFzoIHrzj", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/tune_blends.py > /app/solution/blend_scores.txt && tail -n 25 /app/solution/blend_scores.txt && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:45:02.228Z", "content": "Script running with cell ID 25\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:45:06.577Z", "callId": "call_0D4X2rh57r3kSI10qYqEq88N", "name": "wait", "input": { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 10000 }, "result": { "timestamp": "2026-09-24T17:45:21.390Z", "content": "Script completed\nWall time 14.8 seconds\nOutput:\n\n\nSESSION_ID=7346", "isError": null } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-24T17:45:22.902Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:45:22.902Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:45:23.775Z", "callId": "call_MxBbxjq8V6nnt7P3ozQS4YDz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:7346,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:45:26.222Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\nmulti_w2_a0.05 0.034175\nmulti_w2_a0.15 0.034114\nmulti_w2_a0.3 0.034222\nmulti_w2_a0.49 0.034222\nmulti_w2.5_a0.05 0.034609\nmulti_w2.5_a0.15 0.034588\nmulti_w2.5_a0.3 0.034588\nmulti_w2.5_a0.49 0.034588\nmulti_w3_a0.05 0.03456\nmulti_w3_a0.15 0.03457\nmulti_w3_a0.3 0.034605\nmulti_w3_a0.49 0.034589\nmulti_w3.5_a0.05 0.034731\nmulti_w3.5_a0.15 0.034735\nmulti_w3.5_a0.3 0.034735\nmulti_w3.5_a0.49 0.034735\nmulti_w4_a0.05 0.034497\nmulti_w4_a0.15 0.03449\nmulti_w4_a0.3 0.034472\nmulti_w4_a0.49 0.034476\nmulti_w5_a0.05 0.034474\nmulti_w5_a0.15 0.034479\nmulti_w5_a0.3 0.034457\nmulti_w5_a0.49 0.034457\nBEST 0.03478709902101945 w3_g7_e0.01\n{\n \"model\": \"/app/solution/validation_blend_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034787,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-24T17:45:37.681Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:45:37.681Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-24T17:45:47.196Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:45:47.196Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-24T17:45:53.120Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:45:53.120Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:45:53.661Z", "callId": "call_11IAo3vf9WKtj3CQejAOGLGT", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"sort -k2,2nr /app/solution/blend_scores.txt | sed -n '1,25p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:45:53.737Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nBEST 0.03478709902101945 w3_g7_e0.01\nw3_g7_e0.01 0.034787\nw3_g7_e0.05 0.034787\nw3_g7_e0.15 0.034787\nw3_g7_e0.3 0.034787\nw3_g7_e0.49 0.034787\nw3_to50_1_e0.01 0.034744\nw3_to50_1_e0.05 0.034744\nw3_to50_1_e0.15 0.034744\nw3_to50_1_e0.3 0.034744\nw3_to50_1_e0.49 0.034744\nw3.5_g7_e0.01 0.034739\nw3.5_g7_e0.05 0.034739\nw3.5_g7_e0.15 0.034739\nw3.5_g7_e0.3 0.034739\nw3.5_g7_e0.49 0.034739\nw3.5_g2_e0.01 0.034736\nw3.5_g2_e0.05 0.034736\nw3.5_g2_e0.15 0.034736\nw3.5_g2_e0.3 0.034736\nw3.5_g2_e0.49 0.034736\nmulti_w3.5_a0.15 0.034735\nmulti_w3.5_a0.3 0.034735\nmulti_w3.5_a0.49 0.034735\nw3.5_g3_e0.01 0.034735\n", "isError": null } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-24T17:45:59.267Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:45:59.267Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:46:29.615Z", "callId": "call_SNjisRqhHkXcyOoSYVCZxw3M", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_collab.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Personalize trending candidates by similarity to their recent adopters.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np, pandas as pd\\n+from scipy import sparse\\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64); imap=pd.Index(items)\\n+uids=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); umap=pd.Index(uids); uset=set(uids.tolist())\\n+# Objective-matched recent adopter matrix.\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\\n+li=li[(li.is_organic==1)&(li.played_ratio_pct>=80)&li.uid.isin(uset)&li.item_id.isin(items)][['uid','item_id']].assign(w=1.)\\n+la=la[(la.is_organic==1)&la.uid.isin(uset)&la.item_id.isin(items)][['uid','item_id']].assign(w=3.)\\n+a=pd.concat([li,la]).groupby(['uid','item_id'],as_index=False).w.max()\\n+rr=umap.get_indexer(a.uid); cc=imap.get_indexer(a.item_id)\\n+A=sparse.csr_matrix((a.w.to_numpy(np.float32),(rr,cc)),shape=(len(uids),len(items)))\\n+with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\\n+U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+P=(A.T@U).astype(np.float32); P/=np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\\n+with np.load(O/'recent_features.npz') as z:\\n+ base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+tie=np.log1p(tie); tie=(tie-tie.min())/(tie.max()-tie.min())\\n+# Truth and denominator.\\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\n+tr=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\\n+tr=tr[tr.uid.isin(uset)&tr.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\\n+with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ep=z['packed_eligible'].copy(); ei=z['item_ids'].copy()\\n+eur={int(u):r for r,u in enumerate(eu)}; eic={int(i):c for c,i in enumerate(ei)}; gold={}\\n+for (u,i),g in tr.items():\\n+ r=eur[int(u)]; c=eic[int(i)]\\n+ if (int(ep[r,c//8])>>(c%8))&1:gold.setdefault(int(u),{})[int(i)]=int(g)\\n+disc=1/np.log2(np.arange(2,12)); denom={u:(np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])*disc[:min(10,len(g))]).sum() for u,g in gold.items()}\\n+variants=[]\\n+for eps in [-.49,-.2,-.05,0,.02,.05,.1,.2,.35,.49,.75,1.0]: variants.append(('raw',eps))\\n+for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\\n+tot={v:[] for v in variants}; bs=128\\n+for st in range(0,len(uids),bs):\\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T\\n+ elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6)\\n+ bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\\n+ for v in variants:\\n+ typ,x=v\\n+ q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az)\\n+ q=np.where(elig,q,-np.inf)\\n+ ix=np.argpartition(q,-10,axis=1)[:,-10:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):\\n+ u=int(eu[st+j]); g=gold.get(u)\\n+ if g:tot[v].append(sum((2**g.get(int(ei[c]),0)-1)*disc[k] for k,c in enumerate(ix[j]))/denom[u])\\n+for v in variants:print(v,round(float(np.mean(tot[v])),6))\\n+best=max(variants,key=lambda v:np.mean(tot[v])); print('BEST',best,np.mean(tot[best]))\\n+# Generate the selected full ranking.\\n+out=[]\\n+for st in range(0,len(uids),bs):\\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T; elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6); bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\\n+ typ,x=best; q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az); q=np.where(elig,q,-np.inf)\\n+ ix=np.argpartition(q,-100,axis=1)[:,-100:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\\n+ for j in range(en-st):out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101)}))\\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\\n+np.savez_compressed(O/'collab_model.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:46:29.694Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-24T17:46:32.812Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:46:32.812Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:46:34.692Z", "callId": "call_kpjaMRwEyo7QsIWXZJKeMWyW", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_best.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:46:45.706Z", "content": "Script running with cell ID 29\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:46:47.384Z", "callId": "call_LuATg4KWXisQzjybGIBH0kVw", "name": "wait", "input": { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:47:00.430Z", "content": "Script completed\nWall time 13.0 seconds\nOutput:\n\n('raw', -0.49) 0.034522\r\n('raw', -0.2) 0.034522\r\n('raw', -0.05) 0.034494\r\n('raw', 0) 0.034787\r\n('raw', 0.02) 0.03485\r\n('raw', 0.05) 0.034848\r\n('raw', 0.1) 0.034831\r\n('raw', 0.2) 0.034835\r\n('raw', 0.35) 0.034835\r\n('raw', 0.49) 0.034835\r\n('raw', 0.75) 0.034835\r\n('raw', 1.0) 0.034835\r\n('log', 0.02) 0.034966\r\n('log', 0.05) 0.035576\r\n('log', 0.1) 0.036818\r\n('log', 0.2) 0.039314\r\n('log', 0.3) 0.04129\r\n('log', 0.5) 0.043651\r\n('log', 1.0) 0.043156\r\nBEST ('log', 0.5) 0.04365137646605114\r\n{\r\n \"model\": \"/app/solution/validation_collab_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.04365,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "isError": null } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-24T17:47:09.259Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:47:09.259Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:47:10.803Z", "text": "Collaborative momentum produced the largest gain: tracks are represented by the latent profiles of their most recent organic adopters, then matched to each listener. Blending that affinity with objective-matched momentum scores `0.043650` publicly, about 4× the catalog baseline. I’m narrowing the blend coefficient and then will refit the same cutoff-safe recipe using validation as observed history for the hidden window." }, { "kind": "tool", "timestamp": "2026-09-24T17:47:11.975Z", "callId": "call_6JacnosZgEcCUmCLsPgdkQ1O", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:47:12.059Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:47:15.346Z", "callId": "call_f6zU5RnQ2LQQtcRwByl84qms", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py | tee /app/solution/collab_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:47:26.360Z", "content": "Script running with cell ID 31\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:47:28.223Z", "callId": "call_YPzctwmIh0zZWE3YNTmIPDn0", "name": "wait", "input": { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:47:45.420Z", "content": "Script completed\nWall time 17.2 seconds\nOutput:\n\n('raw', -0.49) 0.034522\r\n('raw', -0.2) 0.034522\r\n('raw', -0.05) 0.034494\r\n('raw', 0) 0.034787\r\n('raw', 0.02) 0.03485\r\n('raw', 0.05) 0.034848\r\n('raw', 0.1) 0.034831\r\n('raw', 0.2) 0.034835\r\n('raw', 0.35) 0.034835\r\n('raw', 0.49) 0.034835\r\n('raw', 0.75) 0.034835\r\n('raw', 1.0) 0.034835\r\n('log', 0.02) 0.034966\r\n('log', 0.05) 0.035576\r\n('log', 0.1) 0.036818\r\n('log', 0.2) 0.039314\r\n('log', 0.3) 0.04129\r\n('log', 0.4) 0.042445\r\n('log', 0.45) 0.043012\r\n('log', 0.5) 0.043651\r\n('log', 0.55) 0.043845\r\n('log', 0.6) 0.044065\r\n('log', 0.65) 0.044083\r\n('log', 0.7) 0.044247\r\n('log', 0.8) 0.044413\r\n('log', 0.9) 0.044056\r\n('log', 1.0) 0.043156\r\nBEST ('log', 0.8) 0.0444126936936653\r\n", "isError": null } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-24T17:47:49.943Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:47:49.943Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:47:55.222Z", "callId": "call_WqSpdh2SGINETnxnvFVdtrYM", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-from pathlib import Path\\n-import numpy as np, pandas as pd\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np, pandas as pd\\n@@\\n D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\\n@@\\n-with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\\n-np.savez_compressed(O/'collab_model.npz',U=U,P=P)\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:47:55.316Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-24T17:48:00.717Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:48:00.717Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:48:02.447Z", "callId": "call_BahZ2rXTAQycivKQFkNj788i", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:48:13.460Z", "content": "Script running with cell ID 33\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:48:15.281Z", "callId": "call_ZNMA2k9qjo1lEn0uqjSuTpXy", "name": "wait", "input": { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:48:32.622Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n\nSESSION_ID=7200", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:48:35.033Z", "callId": "call_Vo07x5ZSqt5HjKnV35sHHzTQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:7200,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:48:35.144Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n('raw', -0.49) 0.034478\r\n('raw', -0.2) 0.034478\r\n('raw', -0.05) 0.034476\r\n('raw', 0) 0.034787\r\n('raw', 0.02) 0.034851\r\n('raw', 0.05) 0.034856\r\n('raw', 0.1) 0.034853\r\n('raw', 0.2) 0.034853\r\n('raw', 0.35) 0.034853\r\n('raw', 0.49) 0.034853\r\n('raw', 0.75) 0.034853\r\n('raw', 1.0) 0.034853\r\n('log', 0.02) 0.034932\r\n('log', 0.05) 0.035388\r\n('log', 0.1) 0.036414\r\n('log', 0.2) 0.038163\r\n('log', 0.3) 0.039865\r\n('log', 0.4) 0.041577\r\n('log', 0.45) 0.041996\r\n('log', 0.5) 0.042468\r\n('log', 0.55) 0.042735\r\n('log', 0.6) 0.043161\r\n('log', 0.65) 0.043585\r\n('log', 0.7) 0.044399\r\n('log', 0.8) 0.044531\r\n('log', 0.9) 0.044584\r\n('log', 1.0) 0.044654\r\nBEST ('log', 1.0) 0.04465410532862228\r\n", "isError": null } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-24T17:48:37.263Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:48:37.263Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:48:40.241Z", "callId": "call_4LNPSJap7BecKlhnOwsnUGT8", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0,1.1,1.2,1.35,1.5,1.75,2.0]:variants.append(('log',beta))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:48:40.331Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:48:44.078Z", "callId": "call_2Xz2mr4M11881SjULHmXQ2xj", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores2.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:48:55.092Z", "content": "Script running with cell ID 36\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:48:57.748Z", "callId": "call_ccp9wJwGjZTQP4WZzndrtB1q", "name": "wait", "input": { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 20000 }, "result": { "timestamp": "2026-09-24T17:49:14.253Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\n\nSESSION_ID=68238", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:49:16.304Z", "callId": "call_slzRBC4TgoJ6pUz0W3uqNr6d", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:68238,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:49:20.903Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\n('raw', -0.49) 0.034478\r\n('raw', -0.2) 0.034478\r\n('raw', -0.05) 0.034476\r\n('raw', 0) 0.034787\r\n('raw', 0.02) 0.034851\r\n('raw', 0.05) 0.034856\r\n('raw', 0.1) 0.034853\r\n('raw', 0.2) 0.034853\r\n('raw', 0.35) 0.034853\r\n('raw', 0.49) 0.034853\r\n('raw', 0.75) 0.034853\r\n('raw', 1.0) 0.034853\r\n('log', 0.02) 0.034932\r\n('log', 0.05) 0.035388\r\n('log', 0.1) 0.036414\r\n('log', 0.2) 0.038163\r\n('log', 0.3) 0.039865\r\n('log', 0.4) 0.041577\r\n('log', 0.45) 0.041996\r\n('log', 0.5) 0.042468\r\n('log', 0.55) 0.042735\r\n('log', 0.6) 0.043161\r\n('log', 0.65) 0.043585\r\n('log', 0.7) 0.044399\r\n('log', 0.8) 0.044531\r\n('log', 0.9) 0.044584\r\n('log', 1.0) 0.044654\r\n('log', 1.1) 0.044023\r\n('log', 1.2) 0.043852\r\n('log', 1.35) 0.043083\r\n('log', 1.5) 0.041362\r\n('log', 1.75) 0.039758\r\n('log', 2.0) 0.037727\r\nBEST ('log', 1.0) 0.04465410532862228\r\n", "isError": null } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-24T17:49:22.494Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:49:22.494Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:49:24.015Z", "callId": "call_yFRll9KAfoYMUj60FS1BM5Ck", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:49:26.603Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_collab_binary_r128.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.044668,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-24T17:49:37.126Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:49:37.126Z", "text": "" } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-24T17:49:43.416Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:49:43.416Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:49:44.978Z", "text": "The best validated configuration is now binary latent profiles (128 dimensions) plus recent-adopter affinity at a 1:1 standardized blend, scoring `0.044668` publicly. I’m doing one final capacity check, then the production build will reconstruct profiles from train+validation, use only the last observed day for momentum/adopters, and apply test-cutoff eligibility." }, { "kind": "tool", "timestamp": "2026-09-24T17:49:46.628Z", "callId": "call_UXwa7Cf22sJL3vDUyjWlNzeF", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 256\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:49:57.642Z", "content": "Script running with cell ID 39\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:49:59.560Z", "callId": "call_yeou2Khg8bknuklh0sZdL61R", "name": "wait", "input": { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 10000 }, "result": { "timestamp": "2026-09-24T17:50:14.928Z", "content": "Script completed\nWall time 15.4 seconds\nOutput:\n\nmatrix (8456, 30000) 5514505 build sec 0.22344207763671875\r\nfit sec 4.9727489948272705 explained 0.40143767\r\nrank 0\r\nrank 1024\r\nrank 2048\r\nrank 3072\r\nrank 4096\r\nrank 5120\r\nrank 6144\r\nrank 7168\r\nrank 8192\r\ntotal sec 26.54409885406494\r\n", "isError": null } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-24T17:50:19.469Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:50:19.469Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:50:22.787Z", "callId": "call_1AQUNBQowtm5MVGqt9NU4hDx", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r256 | tee /app/solution/collab_binary256_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:50:33.804Z", "content": "Script running with cell ID 40\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:50:35.683Z", "callId": "call_SSpm9GJsNiRnuCHQK0HNL1Ox", "name": "wait", "input": { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-09-24T17:50:52.962Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n\nSESSION_ID=36561", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:50:54.929Z", "callId": "call_H3vHDNBna08xrDhOg2M0J9Uc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:36561,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:50:59.801Z", "content": "Script completed\nWall time 4.9 seconds\nOutput:\n\n('raw', -0.49) 0.034477\r\n('raw', -0.2) 0.034477\r\n('raw', -0.05) 0.034476\r\n('raw', 0) 0.034787\r\n('raw', 0.02) 0.034907\r\n('raw', 0.05) 0.034855\r\n('raw', 0.1) 0.034853\r\n('raw', 0.2) 0.034852\r\n('raw', 0.35) 0.034852\r\n('raw', 0.49) 0.034852\r\n('raw', 0.75) 0.034852\r\n('raw', 1.0) 0.034852\r\n('log', 0.02) 0.034944\r\n('log', 0.05) 0.035385\r\n('log', 0.1) 0.036576\r\n('log', 0.2) 0.03836\r\n('log', 0.3) 0.04024\r\n('log', 0.4) 0.041658\r\n('log', 0.45) 0.042193\r\n('log', 0.5) 0.042493\r\n('log', 0.55) 0.042893\r\n('log', 0.6) 0.043472\r\n('log', 0.65) 0.043955\r\n('log', 0.7) 0.044345\r\n('log', 0.8) 0.04456\r\n('log', 0.9) 0.04432\r\n('log', 1.0) 0.044548\r\n('log', 1.1) 0.043749\r\n('log', 1.2) 0.043567\r\n('log', 1.35) 0.042365\r\n('log', 1.5) 0.041035\r\n('log', 1.75) 0.039094\r\n('log', 2.0) 0.037621\r\nBEST ('log', 0.8) 0.044559889656155954\r\n", "isError": null } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-24T17:51:07.377Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:51:07.377Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:51:39.976Z", "callId": "call_s7XQQXOexsGoGXsc3ydeZbPj", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_final.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected discovery ranker on train+validation and rank test candidates.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+DATA = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+FINAL_CUTOFF = 25_393_400 # five seconds after the last validation event\\n+LATENT_DIM = 128\\n+BLEND = 1.0\\n+\\n+\\n+def main() -> None:\\n+ started = time.time()\\n+ items = pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\n+ users = pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64)\\n+ imap, umap = pd.Index(items), pd.Index(users)\\n+ user_set, item_set = set(users.tolist()), set(items.tolist())\\n+\\n+ # The streamed train aggregation is produced by analyze.py and retained as\\n+ # a reproducible intermediate. Add validation positive listens, then refit.\\n+ train = pd.read_parquet(OUT/'target_history.parquet', columns=['r','c'])\\n+ val = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','item_id','played_ratio_pct'])\\n+ val = val[(val.played_ratio_pct >= 50) & val.uid.isin(user_set) & val.item_id.isin(item_set)]\\n+ vr, vc = umap.get_indexer(val.uid), imap.get_indexer(val.item_id)\\n+ pairs = pd.concat([\\n+ train.rename(columns={'r':'r','c':'c'})[['r','c']],\\n+ pd.DataFrame({'r':vr.astype(np.int32), 'c':vc.astype(np.int32)}),\\n+ ]).drop_duplicates()\\n+ X = sparse.csr_matrix(\\n+ (np.ones(len(pairs),np.float32),(pairs.r.to_numpy(),pairs.c.to_numpy())),\\n+ shape=(len(users),len(items)), dtype=np.float32,\\n+ )\\n+ svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\\n+ U = svd.fit_transform(X).astype(np.float32)\\n+ U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+ print('latent fit',X.nnz,round(time.time()-started,2),'sec',flush=True)\\n+\\n+ # The selected momentum window is the final observed 24 hours. Organic\\n+ # strong listens and likes mirror the evaluation labels exactly.\\n+ li = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ la = pd.read_parquet(DATA/'val_likes.parquet', columns=['uid','timestamp','item_id','is_organic'])\\n+ common_li = li.uid.isin(user_set) & li.item_id.isin(item_set) & (li.is_organic == 1)\\n+ common_la = la.uid.isin(user_set) & la.item_id.isin(item_set) & (la.is_organic == 1)\\n+\\n+ def recent_pairs(hours: int) -> tuple[pd.DataFrame,pd.DataFrame]:\\n+ floor = FINAL_CUTOFF-hours*3600\\n+ l = li[common_li & (li.timestamp >= floor) & (li.played_ratio_pct >= 80)][['uid','item_id']].drop_duplicates()\\n+ k = la[common_la & (la.timestamp >= floor)][['uid','item_id']].drop_duplicates()\\n+ return l,k\\n+\\n+ l1,k1 = recent_pairs(24)\\n+ l7,k7 = recent_pairs(24*7)\\n+ base = (\\n+ l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ )\\n+ # Written separately to keep the like channel's predictive weight explicit.\\n+ base = l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ base += 3*k1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie = l7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie += 3*k7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ tie = np.log1p(tie); tie = (tie-tie.min())/max(float(tie.max()-tie.min()),1e-7)\\n+\\n+ adopters = pd.concat([l1.assign(w=1.),k1.assign(w=3.)]).groupby(['uid','item_id'],as_index=False).w.max()\\n+ ar, ac = umap.get_indexer(adopters.uid), imap.get_indexer(adopters.item_id)\\n+ A = sparse.csr_matrix((adopters.w.to_numpy(np.float32),(ar,ac)),shape=X.shape)\\n+ P = (A.T@U).astype(np.float32)\\n+ P /= np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\\n+ bz = np.log1p(base); bz = (bz-bz.mean())/max(float(bz.std()),1e-7)\\n+\\n+ with np.load(DATA/'test_eligible_candidates.npz',allow_pickle=False) as z:\\n+ eu, ei, packed = z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\\n+ assert np.array_equal(eu.astype(np.int64),users)\\n+ assert np.array_equal(ei.astype(np.int64),items)\\n+ out=[]; batch=128\\n+ for st in range(0,len(users),batch):\\n+ en=min(st+batch,len(users))\\n+ affinity=U[st:en]@P.T\\n+ az=(affinity-affinity.mean(1,keepdims=True))/np.maximum(affinity.std(1,keepdims=True),1e-7)\\n+ score=bz[None,:]+BLEND*az+1e-6*tie[None,:]\\n+ eligible=np.unpackbits(packed[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ score=np.where(eligible,score,-np.inf)\\n+ ix=np.argpartition(score,-100,axis=1)[:,-100:]\\n+ vv=np.take_along_axis(score,ix,axis=1)\\n+ ix=np.take_along_axis(ix,np.argsort(-vv,axis=1,kind='stable'),axis=1)\\n+ for j in range(en-st):\\n+ out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\\n+ submission=pd.concat(out,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\\n+ submission.to_parquet(OUT/'submission.parquet',index=False)\\n+ print('wrote',len(submission),'rows in',round(time.time()-started,2),'sec',flush=True)\\n+\\n+\\n+if __name__ == '__main__':\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:51:40.118Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-24T17:51:42.248Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:51:42.248Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:51:44.900Z", "callId": "call_zYsfsxunY3h1kPrY6CrlrgJR", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n- base = (\\n- l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n- + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n- )\\n- # Written separately to keep the like channel's predictive weight explicit.\\n+ # Written separately to keep the like channel's predictive weight explicit.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:51:45.016Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-24T17:51:46.813Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:51:46.813Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:51:48.408Z", "callId": "call_XIRVMASlEHutVUlDAxulOmtz", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:51:57.077Z", "content": "Script completed\nWall time 8.7 seconds\nOutput:\n\nlatent fit 5669201 3.75 sec\r\nwrote 845600 rows in 7.17 sec\r\n", "isError": null } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-24T17:52:08.206Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:52:08.206Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:52:13.986Z", "callId": "call_d1XyT4RkW6sOKQf8XFhsvD5Y", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_recent.py\\n@@\\n for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\\n@@\\n base=features[f'to80_{days}']; lk=features[f'tlike_{days}']\\n for w in [1,3,7]: write_rank(f'grade_{days}_w{w}',base+w*lk)\\n+# Finer search around the winning one-day boundary.\\n+for hours in [6,9,12,15,18,21,24,27,30,36,42,48,60]:\\n+ ml=(li.timestamp >= CUT-hours*3600)&li.uid.isin(tset)&(li.is_organic==1)&(li.played_ratio_pct>=80)\\n+ mk=(la.timestamp >= CUT-hours*3600)&la.uid.isin(tset)&(la.is_organic==1)\\n+ bl=li[ml].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ lk=la[mk].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+ for w in [2,2.5,3,3.5,4,5]: write_rank(f'hours_{hours}_w{w}',bl+w*lk)\\n np.savez_compressed(O/'recent_features.npz',**features)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:52:14.080Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-24T17:52:15.885Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:52:15.885Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:52:18.269Z", "callId": "call_b34UytpzJyCD9EJwAORFIQ5f", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py > /app/solution/recent_scores_hours.txt && grep '^hours_' /app/solution/recent_scores_hours.txt | sort -k2,2nr | sed -n '1,20p' && tail -n 1 /app/solution/recent_scores_hours.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:52:29.284Z", "content": "Script running with cell ID 46\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:52:31.934Z", "callId": "call_lRl3PKfFOkwVVqG5NyBBJXrh", "name": "wait", "input": { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 10000 }, "result": { "timestamp": "2026-09-24T17:52:48.449Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\n\nSESSION_ID=15742", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:52:50.431Z", "callId": "call_oR8sdhRgCfpNUPBwheZ37dC2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:15742,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:52:55.509Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\nhours_21_w3 0.035042\nhours_18_w5 0.035011\nhours_18_w4 0.035007\nhours_21_w3.5 0.034919\nhours_15_w4 0.034879\nhours_18_w3.5 0.03487\nhours_21_w4 0.03485\nhours_15_w5 0.034839\nhours_15_w3 0.034747\nhours_15_w3.5 0.034709\nhours_21_w2.5 0.034636\nhours_24_w3 0.034619\nhours_24_w3.5 0.034602\nhours_18_w3 0.034556\nhours_21_w5 0.034552\nhours_24_w4 0.034538\nhours_18_w2.5 0.034511\nhours_27_w5 0.034502\nhours_24_w2.5 0.034482\nhours_27_w4 0.034451\nBEST 0.035042412582765914 hours_21_w3\n", "isError": null } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-24T17:52:59.355Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:52:59.355Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:53:09.354Z", "callId": "call_klxNe83wUlpPGeE31Zr1zNub", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\\n@@\\n-li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n-la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic'])\\n@@\\n with np.load(O/'recent_features.npz') as z:\\n- base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+ tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\\n+base=li.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n+base+=3*la.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\\n-np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:53:09.444Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-24T17:53:11.551Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:53:11.551Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:53:13.440Z", "callId": "call_EPPcuQJaXWbRzy0I8smoNTuA", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 | tee /app/solution/collab_binary128_h21_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:53:24.455Z", "content": "Script running with cell ID 49\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:53:26.218Z", "callId": "call_yshPP7k7JfWP1FUjMjTgr2gq", "name": "wait", "input": { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-09-24T17:53:43.614Z", "content": "Script completed\nWall time 17.4 seconds\nOutput:\n\n\nSESSION_ID=13544", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:53:47.243Z", "callId": "call_M4hiVkJSmN88IlDp80DR6SZ2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:13544,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:53:49.735Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\n('raw', -0.49) 0.034871\r\n('raw', -0.2) 0.034871\r\n('raw', -0.05) 0.034884\r\n('raw', 0) 0.035079\r\n('raw', 0.02) 0.03514\r\n('raw', 0.05) 0.035111\r\n('raw', 0.1) 0.03511\r\n('raw', 0.2) 0.03511\r\n('raw', 0.35) 0.03511\r\n('raw', 0.49) 0.03511\r\n('raw', 0.75) 0.03511\r\n('raw', 1.0) 0.03511\r\n('log', 0.02) 0.03514\r\n('log', 0.05) 0.035496\r\n('log', 0.1) 0.036223\r\n('log', 0.2) 0.038207\r\n('log', 0.3) 0.039512\r\n('log', 0.4) 0.040944\r\n('log', 0.45) 0.041581\r\n('log', 0.5) 0.04231\r\n('log', 0.55) 0.042683\r\n('log', 0.6) 0.04302\r\n('log', 0.65) 0.043466\r\n('log', 0.7) 0.044103\r\n('log', 0.8) 0.044612\r\n('log', 0.9) 0.044429\r\n('log', 1.0) 0.044704\r\n('log', 1.1) 0.044046\r\n('log', 1.2) 0.043482\r\n('log', 1.35) 0.042559\r\n('log', 1.5) 0.041448\r\n('log', 1.75) 0.039684\r\n('log', 2.0) 0.037831\r\nBEST ('log', 1.0) 0.044704257604108524\r\n", "isError": null } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-24T17:53:51.350Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:53:51.350Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:53:52.649Z", "callId": "call_J5xkgMWif6O37Ob4mUMUqiSv", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:53:55.283Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_collab_binary_r128_h21.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.044685,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-24T17:54:00.086Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:54:00.086Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:54:01.892Z", "callId": "call_6DpwyWnqR6FBjm1HVl6LwML4", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n- # The selected momentum window is the final observed 24 hours. Organic\\n+ # The selected momentum window is the final observed 21 hours. Organic\\n@@\\n- l1,k1 = recent_pairs(24)\\n+ l1,k1 = recent_pairs(21)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:54:02.007Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-24T17:54:03.694Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:54:03.694Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:54:04.912Z", "callId": "call_eXLtEgY4x2fM93936vVQQW2g", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:54:13.609Z", "content": "Script completed\nWall time 8.7 seconds\nOutput:\n\nlatent fit 5669201 3.77 sec\nwrote 845600 rows in 7.26 sec\n", "isError": null } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-24T17:54:20.287Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:54:20.287Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:54:21.717Z", "text": "The finer search settled on a 21-hour momentum/adopter window, with a small public improvement to `0.044685`. The final submission has now been refit on 5,669,201 distinct train+validation user–track positives and rebuilt against test eligibility. I’m running contract-level validation and documenting the exact rebuild path." }, { "kind": "tool", "timestamp": "2026-09-24T17:54:29.533Z", "callId": "call_RDqqlNH1z8E1RJ4Saz3qGAMT", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Music discovery ranker\\n+\\n+The final model is an implicit collaborative momentum ranker. It uses binary\\n+target-user/catalog interactions with at least 50% completion, refits a\\n+128-dimensional randomized SVD on `train + validation`, and represents each\\n+currently trending track by the normalized latent centroid of target users who\\n+organically discovered it in the last 21 observed hours. The final score is an\\n+equal standardized blend of:\\n+\\n+- 21-hour organic discovery momentum (organic >=80% listens plus 3x organic\\n+ like counts), and\\n+- listener similarity to the track's recent organic adopters.\\n+\\n+A seven-day momentum value is retained only as a deterministic tie-breaker.\\n+All ranking happens after applying the supplied per-user cutoff eligibility.\\n+\\n+Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\\n+plain catalog-popularity fallback scored 0.010710.\\n+\\n+## Rebuild\\n+\\n+Use the task's pinned Python environment:\\n+\\n+```sh\\n+cd /app\\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py\\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py\\n+```\\n+\\n+The first command streams the large training listen log and writes the compact\\n+`target_history.parquet` intermediate. The second adds validation history,\\n+refits the selected model, applies `test_eligible_candidates.npz`, and writes\\n+`submission.parquet`. Random seeds and cutoff constants are fixed in code.\\n+\\n+`submission.parquet` contains exactly the required columns `uid`, `item_id`,\\n+and `rank`, with 100 cutoff-eligible distinct tracks per target user.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:54:29.621Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:54:37.161Z", "callId": "call_yxVRrqztAxceYa72QWbkc4Qn", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nfrom ranking_evaluation import validate_ranking, CandidateSet\\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\\nf=pd.read_parquet(S)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nn=validate_ranking(f,targets)\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\neligible=all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(n),'users':n.uid.nunique(),'columns':list(f.columns),'dtypes':{k:str(v) for k,v in f.dtypes.items()},'all_test_eligible':eligible,'bytes':S.stat().st_size})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build_final.py /app/solution/target_history.parquet\\ndu -sh /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:54:39.049Z", "content": "Script completed\nWall time 1.9 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'all_test_eligible': True, 'bytes': 1640967}\n-rw-r--r-- 1 agent agent 1.6K Sep 24 17:54 /app/solution/README.md\n-rw-r--r-- 1 agent agent 5.1K Sep 24 17:54 /app/solution/build_final.py\n-rw-r--r-- 1 agent agent 1.6M Sep 24 17:54 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 54M Sep 24 17:39 /app/solution/target_history.parquet\n232M\t/app/solution\n", "isError": null } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-24T17:54:51.929Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:54:51.929Z", "text": "" } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-24T17:54:58.527Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:54:58.527Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:55:01.718Z", "callId": "call_j7x0BBdLyf40vnmMmlYBO9pt", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\nt=pd.read_parquet(D+'target_users.parquet').uid\\nc=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\\nfor stage in ['validation','test']:\\n p=pd.read_parquet(D+stage+'_preference_state.parquet')\\n q=p[p.uid.isin(t)&p.item_id.isin(c)]\\n print(stage,len(p),len(q),q.state.value_counts().to_dict(),q.groupby('uid').size().describe().to_dict())\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:55:02.671Z", "content": "Script completed\nWall time 0.9 seconds\nOutput:\n\nvalidation 469124 469124 {'liked': 418435, 'disliked': 50689} {'count': 7038.0, 'mean': 66.65586814435919, 'std': 108.6863506794777, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 81.0, 'max': 1847.0}\ntest 484776 484776 {'liked': 432659, 'disliked': 52117} {'count': 7092.0, 'mean': 68.35532994923858, 'std': 112.3020426159094, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 83.0, 'max': 1861.0}\n", "isError": null } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-24T17:55:06.064Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:55:06.064Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:55:11.248Z", "callId": "call_Mqdrfb2fWeiWx6IdOVCXn6SC", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nfrom scipy import sparse\\nD='/task/data/'; O='/app/solution/'\\nuids=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); items=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy(); um=pd.Index(uids); im=pd.Index(items)\\nwith np.load(O+'svd_binary_r128.npz') as z: U=z['user']; C=z['components']\\np=pd.read_parquet(D+'validation_preference_state.parquet'); r=um.get_indexer(p.uid); c=im.get_indexer(p.item_id)\\nfor st in ['liked','disliked']:\\n m=p.state.eq(st).to_numpy(); A=sparse.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(uids),len(items)))\\n V=A@C.T\\n print(st,'base norm median',np.median(np.linalg.norm(U,axis=1)),'adj norm',np.quantile(np.linalg.norm(V,axis=1),[.25,.5,.75,.9]))\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:55:12.363Z", "content": "Script completed\nWall time 1.1 seconds\nOutput:\n\nliked base norm median 11.444354 adj norm [0.11191944 0.61916333 1.82857478 3.62718737]\ndisliked base norm median 11.444354 adj norm [0. 0.03020553 0.2039081 0.43307543]\n", "isError": null } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-24T17:55:14.775Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:55:14.775Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:55:24.165Z", "callId": "call_eObBY5SqmvJZVQQUbhgaRn8l", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_collab.py\\n@@\\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); ap.add_argument('--like-profile',type=float,default=0); ap.add_argument('--dislike-profile',type=float,default=0); args=ap.parse_args()\\n@@\\n-with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32); C=z['components'].astype(np.float32)\\n+if args.like_profile or args.dislike_profile:\\n+ pref=pd.read_parquet(D/'validation_preference_state.parquet')\\n+ pr=umap.get_indexer(pref.uid); pc=imap.get_indexer(pref.item_id)\\n+ for state,weight in [('liked',args.like_profile),('disliked',-args.dislike_profile)]:\\n+ m=pref.state.eq(state).to_numpy()\\n+ M=sparse.csr_matrix((np.ones(m.sum(),np.float32),(pr[m],pc[m])),shape=(len(uids),len(items)))\\n+ U += weight*(M@C.T)\\n U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n@@\\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\\n-np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\\n+tag=f'{args.latent}_h{args.hours}_lp{args.like_profile}_dp{args.dislike_profile}'\\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{tag}.parquet',index=False)\\n+np.savez_compressed(O/f'collab_model_{tag}.npz',U=U,P=P)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:55:24.279Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-24T17:55:25.993Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:55:25.993Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:55:27.991Z", "callId": "call_XyKKGTZE4W8wvMUxCmy0Bje8", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 3 --dislike-profile 10 | tee /app/solution/collab_pref_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:55:39.006Z", "content": "Script running with cell ID 59\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:55:40.846Z", "callId": "call_PnoErDkCxmoPZGrMo73uTX9g", "name": "wait", "input": { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-09-24T17:55:58.165Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n\nSESSION_ID=89873", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:56:00.201Z", "callId": "call_GAd738ToMmWpTRLzhnBobClz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:89873,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:56:04.804Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\n('raw', -0.49) 0.034843\r\n('raw', -0.2) 0.034843\r\n('raw', -0.05) 0.034845\r\n('raw', 0) 0.035079\r\n('raw', 0.02) 0.035133\r\n('raw', 0.05) 0.035129\r\n('raw', 0.1) 0.035129\r\n('raw', 0.2) 0.035136\r\n('raw', 0.35) 0.035137\r\n('raw', 0.49) 0.035137\r\n('raw', 0.75) 0.035137\r\n('raw', 1.0) 0.035137\r\n('log', 0.02) 0.035184\r\n('log', 0.05) 0.035463\r\n('log', 0.1) 0.036307\r\n('log', 0.2) 0.038141\r\n('log', 0.3) 0.039579\r\n('log', 0.4) 0.041144\r\n('log', 0.45) 0.041627\r\n('log', 0.5) 0.042281\r\n('log', 0.55) 0.042781\r\n('log', 0.6) 0.043116\r\n('log', 0.65) 0.043422\r\n('log', 0.7) 0.043989\r\n('log', 0.8) 0.044089\r\n('log', 0.9) 0.044274\r\n('log', 1.0) 0.044146\r\n('log', 1.1) 0.043748\r\n('log', 1.2) 0.043323\r\n('log', 1.35) 0.042081\r\n('log', 1.5) 0.041016\r\n('log', 1.75) 0.038737\r\n('log', 2.0) 0.036627\r\nBEST ('log', 0.9) 0.04427390575522779\r\n", "isError": null } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-24T17:56:06.616Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:56:06.616Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:56:08.641Z", "callId": "call_ngo3qw11dq8bkOu9n2PVsHUD", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 0 | tee /app/solution/collab_like1_scores.txt\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:56:19.655Z", "content": "Script running with cell ID 61\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:56:21.502Z", "callId": "call_hxg3x0i1nEsD8diKvxyuudBP", "name": "wait", "input": { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-09-24T17:56:38.816Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n\nSESSION_ID=55452", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:56:41.038Z", "callId": "call_zVhc1lmTW1CF6dq1xsKy8AdY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:55452,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:56:44.544Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n('raw', -0.49) 0.034867\r\n('raw', -0.2) 0.034867\r\n('raw', -0.05) 0.034881\r\n('raw', 0) 0.035079\r\n('raw', 0.02) 0.035164\r\n('raw', 0.05) 0.035135\r\n('raw', 0.1) 0.035115\r\n('raw', 0.2) 0.035115\r\n('raw', 0.35) 0.035115\r\n('raw', 0.49) 0.035115\r\n('raw', 0.75) 0.035115\r\n('raw', 1.0) 0.035099\r\n('log', 0.02) 0.035129\r\n('log', 0.05) 0.035489\r\n('log', 0.1) 0.036285\r\n('log', 0.2) 0.038261\r\n('log', 0.3) 0.039663\r\n('log', 0.4) 0.040966\r\n('log', 0.45) 0.041715\r\n('log', 0.5) 0.042273\r\n('log', 0.55) 0.042729\r\n('log', 0.6) 0.043287\r\n('log', 0.65) 0.043553\r\n('log', 0.7) 0.044097\r\n('log', 0.8) 0.044629\r\n('log', 0.9) 0.044919\r\n('log', 1.0) 0.044986\r\n('log', 1.1) 0.044737\r\n('log', 1.2) 0.044121\r\n('log', 1.35) 0.042842\r\n('log', 1.5) 0.042229\r\n('log', 1.75) 0.039953\r\n('log', 2.0) 0.037889\r\nBEST ('log', 1.0) 0.04498647700478426\r\n", "isError": null } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-24T17:56:46.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:56:46.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:56:47.867Z", "callId": "call_I2Q9PyJOxkG3S9godifPvsJL", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:56:50.481Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.04496,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-24T17:56:53.270Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:56:53.270Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:56:59.532Z", "callId": "call_ssoXHMnqT96uRTc5XCBsj0H2", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_final.py\\n@@\\n svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\\n U = svd.fit_transform(X).astype(np.float32)\\n+ # Active likes sharpen the listener profile in the fitted item basis. A\\n+ # unit positive adjustment was selected on the temporal validation cut.\\n+ pref = pd.read_parquet(DATA/'test_preference_state.parquet')\\n+ pref = pref[pref.state.eq('liked')]\\n+ pr, pc = umap.get_indexer(pref.uid), imap.get_indexer(pref.item_id)\\n+ L = sparse.csr_matrix((np.ones(len(pref),np.float32),(pr,pc)),shape=X.shape)\\n+ U += L @ svd.components_.T\\n U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n*** Update File: /app/solution/README.md\\n@@\\n-128-dimensional randomized SVD on `train + validation`, and represents each\\n+128-dimensional randomized SVD on `train + validation`, sharpens each listener\\n+profile with the cutoff-specific active-like state, and represents each\\n@@\\n-Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\\n+Public temporal validation NDCG@10: **0.044960** over 4,263 scored users. The\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T17:56:59.640Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-24T17:57:02.460Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:57:02.460Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:57:06.754Z", "callId": "call_IR66oA5SipsUF3xT55d4cwM9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py && PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import validate_ranking, CandidateSet\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint('VALID',len(f),f.uid.nunique(),p.stat().st_size)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" }, "result": { "timestamp": "2026-09-24T17:57:17.438Z", "content": "Script completed\nWall time 10.7 seconds\nOutput:\n\nlatent fit 5669201 3.82 sec\nwrote 845600 rows in 7.34 sec\nVALID 845600 8456 1640803\n", "isError": null } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-24T17:57:20.388Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:57:20.388Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:57:21.841Z", "text": "Active likes provided a modest but repeatable gain; the selected validation score is now `0.044960`. I’ve incorporated the cutoff-correct test preference state into the refit and revalidated all 845,600 rows. I’m checking whether a conservative negative-state adjustment helps before freezing the artifact; the current valid submission remains saved throughout." }, { "kind": "tool", "timestamp": "2026-09-24T17:57:23.796Z", "callId": "call_9wrifj4k5uZfZEbC6E3oawxy", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 3 | tail -n 23\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);" }, "result": { "timestamp": "2026-09-24T17:57:34.824Z", "content": "Script running with cell ID 66\nWall time 11.0 seconds\nOutput:\n", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:57:36.649Z", "callId": "call_7ZXgrfj39aWup7GJIljKVkAo", "name": "wait", "input": { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-09-24T17:57:53.983Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n\nSESSION_ID=50595", "isError": null } }, { "kind": "tool", "timestamp": "2026-09-24T17:57:58.069Z", "callId": "call_LSyLIoozLGjigssq21zc7gjn", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:50595,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T17:58:00.282Z", "content": "Script completed\nWall time 2.2 seconds\nOutput:\n\n('raw', 1.0) 0.035115\r\n('log', 0.02) 0.035146\r\n('log', 0.05) 0.035544\r\n('log', 0.1) 0.036319\r\n('log', 0.2) 0.038336\r\n('log', 0.3) 0.039712\r\n('log', 0.4) 0.04107\r\n('log', 0.45) 0.042032\r\n('log', 0.5) 0.042556\r\n('log', 0.55) 0.043119\r\n('log', 0.6) 0.043483\r\n('log', 0.65) 0.043615\r\n('log', 0.7) 0.044135\r\n('log', 0.8) 0.044454\r\n('log', 0.9) 0.044667\r\n('log', 1.0) 0.044876\r\n('log', 1.1) 0.044271\r\n('log', 1.2) 0.043594\r\n('log', 1.35) 0.042784\r\n('log', 1.5) 0.041997\r\n('log', 1.75) 0.03955\r\n('log', 2.0) 0.037688\r\nBEST ('log', 1.0) 0.044875728761976125\r\n", "isError": null } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-24T17:58:06.038Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:58:06.038Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T17:58:10.000Z", "callId": "call_JQ9a5ee9LsdNnyZwQq1cQKT0", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/analyze.py /app/solution/build_final.py /app/solution/build_baseline.py && sha256sum /app/solution/submission.parquet && sed -n '1,220p' /app/solution/README.md\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);\ntext(await tools.update_plan({explanation:\"Selected and refit the strongest public-validated collaborative momentum model; final artifact passes schema and cutoff eligibility checks.\",\"plan\":[{\"step\":\"Inspect data schemas, scale, time ranges, and candidate/eligibility structure\",\"status\":\"completed\"},{\"step\":\"Build a valid baseline and validation feature pipeline\",\"status\":\"completed\"},{\"step\":\"Tune ranking ensemble on the public temporal window\",\"status\":\"completed\"},{\"step\":\"Refit on train+validation and generate final test ranking\",\"status\":\"completed\"},{\"step\":\"Validate deliverables and document reproduction\",\"status\":\"completed\"}]}));" }, "result": { "timestamp": "2026-09-24T17:58:10.178Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n1e7d088b56b8c273776eb9177a65bb9aaab1a96c639d2340a510c94d5174fc57 /app/solution/submission.parquet\n# Music discovery ranker\n\nThe final model is an implicit collaborative momentum ranker. It uses binary\ntarget-user/catalog interactions with at least 50% completion, refits a\n128-dimensional randomized SVD on `train + validation`, sharpens each listener\nprofile with the cutoff-specific active-like state, and represents each\ncurrently trending track by the normalized latent centroid of target users who\norganically discovered it in the last 21 observed hours. The final score is an\nequal standardized blend of:\n\n- 21-hour organic discovery momentum (organic >=80% listens plus 3x organic\n like counts), and\n- listener similarity to the track's recent organic adopters.\n\nA seven-day momentum value is retained only as a deterministic tie-breaker.\nAll ranking happens after applying the supplied per-user cutoff eligibility.\n\nPublic temporal validation NDCG@10: **0.044960** over 4,263 scored users. The\nplain catalog-popularity fallback scored 0.010710.\n\n## Rebuild\n\nUse the task's pinned Python environment:\n\n```sh\ncd /app\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py\n```\n\nThe first command streams the large training listen log and writes the compact\n`target_history.parquet` intermediate. The second adds validation history,\nrefits the selected model, applies `test_eligible_candidates.npz`, and writes\n`submission.parquet`. Random seeds and cutoff constants are fixed in code.\n\n`submission.parquet` contains exactly the required columns `uid`, `item_id`,\nand `rank`, with 100 cutoff-eligible distinct tracks per target user.\n\n{}", "isError": null } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-24T17:58:12.383Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T17:58:12.383Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T17:58:14.366Z", "text": "Completed the music-discovery ranker.\n\n- Final artifact: [submission.parquet](/app/solution/submission.parquet)\n- Rebuild code: [build_final.py](/app/solution/build_final.py)\n- Documentation: [README.md](/app/solution/README.md)\n- Public validation NDCG@10: **0.044960**\n- Verified: 845,600 rows, 8,456 users, ranks 1–100, unique items, and all candidates test-eligible." } ] } ] }