{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "run-005-gpt-5-6-sol-high-2026-08-31T11-04-49", "taskChecksum": "33d6d3ffa5167d64d7bb9749954c90796f57f9ecacd22a92248e84ae4988a603", "source": { "format": "codex-rollout", "sha256": "80da5defe5bef9a6bde5a1d9d4a106ef06170de081adf350f5fdba69de542190", "records": 674, "bookkeepingRecords": { "task_started": 1, "user_message": 1, "agent_message": 14, "token_count": 153, "patch_apply_end": 14, "task_complete": 1 } }, "sessionId": "01a05911-560e-72e0-8d10-ff5687a42d43", "agentVersion": "0.147.0", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-08-31T18:25:03.528Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:03.528Z", "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": "2026-08-31T18:25:05.279Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:05.279Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-08-31T18:25:05.279Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-08-31T18:25:05.279Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-08-31T18:25:05.280Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:05.280Z", "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": "2026-08-31T18:25:05.280Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:05.280Z", "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": "2026-08-31T18:25:05.280Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:05.280Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Airtable (airtable@openai-curated-remote)\n- Alpaca (alpaca@openai-curated-remote)\n- Apollo.io (apollo@openai-curated-remote)\n- Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote)\n- AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote)\n- Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote)\n- Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote)\n- LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote)\n- SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote)\n- Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote)\n- Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote)\n- Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote)\n- Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote)\n- True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote)\n- Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote)\n- Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote)\n- Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote)\n- Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote)\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote)\n- Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote)\n- Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote)\n- Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote)\n- vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote)\n- TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote)\n- Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote)\n- Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote)\n- Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote)\n- Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote)\n- COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote)\n- TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote)\n- CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote)\n- Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote)\n- Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote)\n- freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote)\n- Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote)\n- Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote)\n- Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote)\n- CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote)\n- Asana (asana@openai-curated-remote)\n- Atlassian Rovo (atlassian-rovo@openai-curated-remote)\n- Base44 (base44@openai-curated-remote)\n- Binance (binance@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Canva (canva@openai-curated-remote)\n- ClickUp (clickup@openai-curated-remote)\n- Cloudflare (cloudflare@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-08-31T18:25:05.280Z", "text": "\n /app\n sh\n 2026-08-31\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-08-31T18:25:05.280Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "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": "2026-08-31T18:25:05.280Z", "data": { "type": "turn_context", "turn_id": "01a05911-5623-79c0-b6fc-6b425971d257", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-08-31", "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": "2026-08-31T18:25:05.289Z", "blocks": [ { "kind": "message", "timestamp": "2026-08-31T18:25:05.289Z", "text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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": "2026-08-31T18:25:08.034Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:08.034Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:25:11.044Z", "text": "I’ll build and benchmark the full two-tower pipeline in `/app/solution/`, first auditing the data and evaluator, then training a validation model, preserving the best valid ranking, and finally refitting with validation history for the test submission. I’ll keep the scoring path exactly decomposable into independently computed user/item vectors and avoid any global interaction matrix." }, { "kind": "tool", "timestamp": "2026-08-31T18:25:14.462Z", "callId": "call_3ZXIbxyMdjWPoo8ZCc7HQB1w", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:25:14.529Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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# Exact distribution inventory for the Yambda two-tower task family.\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. Python itself is pinned by task.toml and both\n# 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\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\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\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\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\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\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": "2026-08-31T18:25:18.750Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:18.750Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:25:29.637Z", "callId": "call_RvyLFqtqb89kuEea1OvPSByF", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py && python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows', pf.metadata.num_rows, 'rgs', pf.metadata.num_row_groups, 'schema', pf.schema_arrow)\\nprint('npz')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n print(n,{k:(z[k].shape,z[k].dtype) for k in z.files},'avg eligible',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).mean())\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:25:29.748Z", "content": "Script completed\nWall time 0.1 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()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "isError": null } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-08-31T18:25:32.679Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:32.679Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:25:35.239Z", "text": "The interaction log is large enough that I’m checking its physical layout and available parquet reader before choosing the sampling strategy. The first metadata probe found that the declared `pyarrow` package is not importable in this runtime, so I’m adapting the loader rather than risking a memory-heavy full read." }, { "kind": "tool", "timestamp": "2026-08-31T18:25:42.157Z", "callId": "call_5XoI3tRYWbo1qWsT8h7s0pf8", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, numpy as np, torch, sys\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\nfor x in ['pyarrow','fastparquet']:\\n try:\\n m=__import__(x); print(x,m.__version__)\\n except Exception as e: print(x,'ERR',repr(e))\\ntry:\\n print(pd.read_parquet('/task/data/target_users.parquet').head())\\nexcept Exception as e: print('read ERR',type(e),e)\\nPY\\ncommand -v parquet-tools || true\\nfind /usr -iname '*pyarrow*' -maxdepth 6 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:25:42.243Z", "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": 10, "role": "agent", "timestamp": "2026-08-31T18:25:45.185Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:45.185Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:25:49.701Z", "callId": "call_RX36ToPl3URboZpFKlUhGsdH", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 uv 2>/dev/null || true\\npython --version\\npython -m pip list 2>/dev/null | head -40 || true\\nfind /opt /usr/local /root -type f \\\\( -name 'python' -o -name 'python3' \\\\) 2>/dev/null | head -30\\nenv | sort | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:25:50.544Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPackage Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_THREAD_ID=01a05911-560e-72e0-8d10-ff5687a42d43\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=40c965ab66e1\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/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_=/usr/local/bin/codex\n", "isError": null } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-08-31T18:25:53.081Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:53.081Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:25:57.602Z", "callId": "call_1SHMS9K7UgJbVHSCoLL93h7s", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -la /task | sed -n '1,160p'\\nfind / -maxdepth 4 -type f -name 'activate' 2>/dev/null | head -50\\nfind / -maxdepth 5 -type d -name 'site-packages' 2>/dev/null | head -50\\nls -la /opt /usr/local/lib/python3.12/site-packages 2>/dev/null | head -100\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:25:57.744Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\ntotal 36\ndr-xr-xr-x 1 root root 4096 Aug 31 18:22 .\ndrwxr-xr-x 1 root root 4096 Aug 31 18:25 ..\n-r--r--r-- 1 root root 0 Aug 31 18:08 .keep\ndr-xr-xr-x 1 root root 4096 Aug 31 18:09 data\n-r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py\n-r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt\n/opt/task-venv/bin/activate\n/opt/task-venv/lib/python3.12/site-packages\n/usr/local/lib/python3.12/site-packages\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Aug 31 18:21 .\ndrwxr-xr-x 1 root root 4096 Aug 31 18:25 ..\ndrwxr-xr-x 4 root root 4096 Aug 31 18:21 codex-home\ndr-xr-xr-x 1 root root 4096 Aug 31 18:22 task-venv\n\n/usr/local/lib/python3.12/site-packages:\ntotal 20\ndrwxr-xr-x 4 root root 4096 Aug 5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug 5 01:13 ..\n-rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt\ndrwxr-xr-x 5 root root 4096 Aug 5 01:13 pip\ndrwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info\n", "isError": null } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-08-31T18:25:59.636Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:25:59.636Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:26:11.115Z", "callId": "call_TH8PfxxDEZ4rwlUzGhgH31DY", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, torch\\nfrom pathlib import Path\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows', pf.metadata.num_rows, 'rgs', pf.metadata.num_row_groups, 'cols',pf.schema_arrow.names)\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\\n s=bits.sum(1)\\n print(n,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'eligible mean/min/max',s.mean(),s.min(),s.max())\\nfor n in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n x=pd.read_parquet(D/n)\\n print('\\\\n',n,x.shape,x.dtypes.to_dict())\\n print(x.head())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:26:15.931Z", "content": "Script completed\nWall time 4.8 seconds\nOutput:\n\nversions 2.3.3 2.2.6 2.8.0+cpu\ncandidate_catalog.parquet rows 30000 rgs 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rgs 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rgs 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rgs 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rgs 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rgs 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible mean/min/max 29067.306291390727 24657 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible mean/min/max 29041.3178807947 24552 30000\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n", "isError": null } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-08-31T18:26:35.686Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:26:35.686Z", "text": "" } ] }, { "id": 14, "role": "agent", "timestamp": "2026-08-31T18:26:37.988Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:26:37.988Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:26:41.364Z", "text": "The catalog has 30,000 items and 8,456 target users, with roughly 29,000 eligible items per user. That makes exhaustive CPU scoring feasible here, while the learned dot-product representation remains production-compatible with MIPS. I’m now measuring how much target-user and catalog-positive history exists so the training examples emphasize organic 80% completions and explicit likes without scanning into a global matrix." }, { "kind": "tool", "timestamp": "2026-08-31T18:26:54.942Z", "callId": "call_WSgQZipCyl7os0eAU55cF9ld", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nD='/task/data/'\\ntarget=np.sort(pd.read_parquet(D+'target_users.parquet').uid.to_numpy())\\ncat=np.sort(pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy())\\npf=pq.ParquetFile(D+'train_listens.parquet')\\nagg=np.zeros(12,np.int64); t=time.time()\\nfor i in range(pf.num_row_groups):\\n x=pf.read_row_group(i,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\\n ut=np.isin(x.uid.to_numpy(),target)\\n ic=np.isin(x.item_id.to_numpy(),cat)\\n pos=x.played_ratio_pct.to_numpy()>=50\\n strong=x.played_ratio_pct.to_numpy()>=80\\n org=x.is_organic.to_numpy()==1\\n agg += [len(x),ut.sum(),ic.sum(),(ut&ic).sum(),(ut&ic&pos).sum(),(ut&ic&strong&org).sum(),(ic&pos).sum(),(ic&strong&org).sum(), x.timestamp.min(),x.timestamp.max(),(ut&pos).sum(),(ut&strong&org).sum()]\\n if i%10==9: print(i+1,agg[:8].tolist(),'sec',round(time.time()-t,1))\\nprint('FINAL',agg.tolist(),'sec',time.time()-t)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:26:59.518Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\n10 [10485760, 10426041, 7502693, 7459622, 4752124, 2075856, 4773469, 2085564] sec 0.7\n20 [20971520, 20832481, 15010786, 14915893, 9608448, 4148867, 9660026, 4166905] sec 1.5\n30 [31457280, 31246755, 22532804, 22383259, 14387816, 6209932, 14467283, 6241205] sec 2.2\n40 [41943040, 41683273, 30127131, 29941404, 19245295, 8353545, 19340920, 8391078] sec 2.9\nFINAL [43484159, 43222297, 31249613, 31062540, 19959853, 8661020, 20056329, 8699160, 58505, 1041043790, 27367343, 12123010] sec 3.008568048477173\n", "isError": null } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-08-31T18:27:09.201Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:27:09.201Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:27:23.583Z", "callId": "call_NBKlMA1WiRHsnMgK7Nug7OTd", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\\nD='/task/data/'\\npf=pq.ParquetFile(D+'train_listens.parquet')\\ncounts=np.zeros(8,np.int64); vals=[]\\nfor i in range(pf.num_row_groups):\\n x=pf.read_row_group(i,columns=['timestamp']).column(0).to_numpy()\\n counts += [(x<0).sum(),(x<=24786800).sum(),(x>24786800).sum(),(x>1e8).sum(),(x>1e9).sum(),len(x),x.min(),x.max()]\\n vals.append(x[::100])\\na=np.concatenate(vals)\\nprint('counts sums (mins max sums irrelevant)',counts)\\nprint('sample quantiles',np.quantile(a,[0,.001,.01,.1,.25,.5,.75,.9,.99,.999,1]))\\nfor family in ['likes','dislikes','unlikes','undislikes']:\\n x=pd.read_parquet(D+'train_'+family+'.parquet',columns=['timestamp'])\\n print(family,x.timestamp.min(),x.timestamp.max(),x.timestamp.quantile([0,.01,.5,.99,1]).to_dict(),(x.timestamp>24786800).sum())\\nfor family in ['listens','likes']:\\n x=pd.read_parquet(D+'val_'+family+'.parquet',columns=['timestamp'])\\n print('val',family,x.timestamp.min(),x.timestamp.max(),x.timestamp.quantile([0,.01,.5,.99,1]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:27:25.674Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\ncounts sums (mins max sums irrelevant) [ 0 43484159 0 0 0 43484159\n 58505 1041043790]\nsample quantiles [1.40000000e+02 3.84060600e+04 3.63190350e+05 3.32272400e+06\n 7.74013375e+06 1.42409975e+07 2.00100438e+07 2.29713080e+07\n 2.46187542e+07 2.47713380e+07 2.47867950e+07]\nlikes 80 24786790 {0.0: 80.0, 0.01: 304381.5, 0.5: 14330500.0, 0.99: 24647098.5, 1.0: 24786790.0} 0\ndislikes 75 24786580 {0.0: 75.0, 0.01: 416615.0, 0.5: 13586810.0, 0.99: 24641102.099999998, 1.0: 24786580.0} 0\nunlikes 250 24786570 {0.0: 250.0, 0.01: 402560.0, 0.5: 13931030.0, 0.99: 24663950.0, 1.0: 24786570.0} 0\nundislikes 15950 24785395 {0.0: 15950.0, 0.01: 487448.65, 0.5: 13358502.5, 0.99: 24593935.85, 1.0: 24785395.0} 0\nval listens 24788600 25393395 {0.0: 24788600.0, 0.01: 24803755.0, 0.5: 25097890.0, 0.99: 25386015.2, 1.0: 25393395.0}\nval likes 24788815 25393390 {0.0: 24788815.0, 0.01: 24809869.4, 0.5: 25051965.0, 0.99: 25386795.6, 1.0: 25393390.0}\n", "isError": null } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-08-31T18:27:45.419Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:27:45.419Z", "text": "" } ] }, { "id": 17, "role": "agent", "timestamp": "2026-08-31T18:28:04.001Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:28:04.001Z", "text": "" } ] }, { "id": 18, "role": "agent", "timestamp": "2026-08-31T18:28:16.296Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:28:16.296Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:28:30.100Z", "callId": "call_ZiapMq5KkksCqUcNkJ9FcRWh", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+\\\"\\\"\\\"Create an eligibility-safe popularity baseline for quick diagnostics.\\\"\\\"\\\"\\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 write(stage: str) -> Path:\\n+ archive = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"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(\\n+ pd.DataFrame(\\n+ {\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}\\n+ )\\n+ )\\n+ path = OUT / f\\\"{stage}_popularity.parquet\\\"\\n+ pd.concat(rows, ignore_index=True).astype(\\\"int64\\\").to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ print(write(\\\"validation\\\"))\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:28:30.173Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-08-31T18:28:32.246Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:28:32.246Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:28:35.329Z", "callId": "call_euBjmJTN5aNuyokAFy4bR8s5", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:28:41.604Z", "content": "Script completed\nWall time 6.3 seconds\nOutput:\n\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Aug 31 18:28 .\ndrwxr-xr-x 1 agent agent 4096 Aug 31 18:22 ..\n-rw-r--r-- 1 agent agent 986 Aug 31 18:28 baseline.py\n/app/solution/validation_popularity.parquet\n{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-08-31T18:29:01.096Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:29:01.096Z", "text": "" } ] }, { "id": 21, "role": "agent", "timestamp": "2026-08-31T18:29:19.767Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:29:19.767Z", "text": "" } ] }, { "id": 22, "role": "agent", "timestamp": "2026-08-31T18:29:38.448Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:29:38.448Z", "text": "" } ] }, { "id": 23, "role": "agent", "timestamp": "2026-08-31T18:29:57.027Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:29:57.027Z", "text": "" } ] }, { "id": 24, "role": "agent", "timestamp": "2026-08-31T18:30:07.959Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:30:07.959Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:30:11.644Z", "text": "The popularity baseline scores 0.010710 NDCG@10, which gives a concrete floor. The learned model will use rolling, pre-cutoff weekly backtests as training examples: each user vector pools recent completed listens plus active like/dislike state, while the independent item tower combines a trainable catalog embedding with popularity metadata. Training uses sampled pairwise negatives—linear in observed examples, with no user-ID parameters and no user–item matrix." }, { "kind": "tool", "timestamp": "2026-08-31T18:34:25.206Z", "callId": "call_LlWaJYNflcSEBa6rTUIGYlQg", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based PyTorch two-tower ranker.\\n+\\n+The script deliberately represents interactions as event streams and sampled\\n+training triples. It never creates a user-by-item matrix. At inference the\\n+two towers are called independently and the only scoring operation is a dot\\n+product between their equal-dimensional outputs.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import os\\n+import random\\n+import shutil\\n+import time\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+import torch.nn as nn\\n+import torch.nn.functional as F\\n+from torch.utils.data import DataLoader, Dataset\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+GAP = 1_800\\n+WEEK = 604_800\\n+STEP = GAP + WEEK\\n+HISTORY_DAYS = 120\\n+MAX_LISTENS = 120\\n+MAX_LIKES = 60\\n+MAX_DISLIKES = 40\\n+EMBED_DIM = 64\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+class IdMap:\\n+ \\\"\\\"\\\"Vectorized mapping from raw integer IDs to compact indices.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: np.ndarray):\\n+ self.raw = np.asarray(raw, dtype=np.int64)\\n+ self.order = np.argsort(self.raw)\\n+ self.sorted = self.raw[self.order]\\n+\\n+ def map(self, values: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ values = np.asarray(values, dtype=np.int64)\\n+ pos = np.searchsorted(self.sorted, values)\\n+ valid = pos < len(self.sorted)\\n+ safe = np.minimum(pos, len(self.sorted) - 1)\\n+ valid &= self.sorted[safe] == values\\n+ mapped = np.full(len(values), -1, dtype=np.int32)\\n+ mapped[valid] = self.order[safe[valid]].astype(np.int32)\\n+ return mapped, valid\\n+\\n+\\n+@dataclass\\n+class EventData:\\n+ user: np.ndarray\\n+ item: np.ndarray\\n+ timestamp: np.ndarray\\n+ ratio: np.ndarray\\n+ organic: np.ndarray\\n+\\n+\\n+@dataclass\\n+class ActionData:\\n+ user: np.ndarray\\n+ item: np.ndarray\\n+ timestamp: np.ndarray\\n+ channel: np.ndarray # 1 liked, -1 disliked\\n+ active: np.ndarray\\n+ organic: np.ndarray\\n+\\n+\\n+def load_listens(\\n+ names: list[str], user_map: IdMap, item_map: IdMap, earliest: int\\n+) -> EventData:\\n+ chunks: list[tuple[np.ndarray, ...]] = []\\n+ for name in names:\\n+ pf = pq.ParquetFile(DATA / name)\\n+ for rg in range(pf.num_row_groups):\\n+ x = pf.read_row_group(\\n+ rg,\\n+ columns=[\\n+ \\\"uid\\\",\\n+ \\\"timestamp\\\",\\n+ \\\"item_id\\\",\\n+ \\\"is_organic\\\",\\n+ \\\"played_ratio_pct\\\",\\n+ ],\\n+ ).to_pandas()\\n+ ts = x.timestamp.to_numpy(dtype=np.int64)\\n+ ratio = x.played_ratio_pct.to_numpy(dtype=np.float32)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv & (ts >= earliest) & (ratio >= 50.0)\\n+ if keep.any():\\n+ chunks.append(\\n+ (\\n+ u[keep],\\n+ i[keep],\\n+ ts[keep].astype(np.int32),\\n+ ratio[keep].astype(np.float16),\\n+ x.is_organic.to_numpy(dtype=np.uint8)[keep],\\n+ )\\n+ )\\n+ arrays = [np.concatenate([c[j] for c in chunks]) for j in range(5)]\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ arrays = [a[order] for a in arrays]\\n+ return EventData(*arrays)\\n+\\n+\\n+def load_actions(\\n+ include_validation: bool, user_map: IdMap, item_map: IdMap, earliest: int\\n+) -> ActionData:\\n+ specs = [\\n+ (\\\"likes\\\", 1, 1),\\n+ (\\\"unlikes\\\", 1, 0),\\n+ (\\\"dislikes\\\", -1, 1),\\n+ (\\\"undislikes\\\", -1, 0),\\n+ ]\\n+ chunks: list[tuple[np.ndarray, ...]] = []\\n+ prefixes = [\\\"train\\\", \\\"val\\\"] if include_validation else [\\\"train\\\"]\\n+ for prefix in prefixes:\\n+ for family, channel, active in specs:\\n+ x = pd.read_parquet(\\n+ DATA / f\\\"{prefix}_{family}.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"],\\n+ )\\n+ ts = x.timestamp.to_numpy(dtype=np.int64)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv & (ts >= earliest)\\n+ n = int(keep.sum())\\n+ if n:\\n+ chunks.append(\\n+ (\\n+ u[keep],\\n+ i[keep],\\n+ ts[keep].astype(np.int32),\\n+ np.full(n, channel, dtype=np.int8),\\n+ np.full(n, active, dtype=np.uint8),\\n+ x.is_organic.to_numpy(dtype=np.uint8)[keep],\\n+ )\\n+ )\\n+ arrays = [np.concatenate([c[j] for c in chunks]) for j in range(6)]\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ arrays = [a[order] for a in arrays]\\n+ return ActionData(*arrays)\\n+\\n+\\n+def offsets_for_users(user: np.ndarray, n_users: int) -> np.ndarray:\\n+ return np.searchsorted(user, np.arange(n_users + 1)).astype(np.int64)\\n+\\n+\\n+class FeatureBuilder:\\n+ \\\"\\\"\\\"Construct bounded history sequences directly from chronological logs.\\\"\\\"\\\"\\n+\\n+ def __init__(self, events: EventData, actions: ActionData, n_users: int):\\n+ self.e = events\\n+ self.a = actions\\n+ self.eoff = offsets_for_users(events.user, n_users)\\n+ self.aoff = offsets_for_users(actions.user, n_users)\\n+ self.n_users = n_users\\n+\\n+ def one(self, user: int, cutoff: int) -> tuple[np.ndarray, ...]:\\n+ # Most recent occurrence of each listened item, bounded by count and age.\\n+ lo, hi = self.eoff[user], self.eoff[user + 1]\\n+ end = lo + np.searchsorted(self.e.timestamp[lo:hi], cutoff, side=\\\"right\\\")\\n+ min_ts = cutoff - HISTORY_DAYS * 86_400\\n+ listen_i = np.zeros(MAX_LISTENS, dtype=np.int32)\\n+ listen_w = np.zeros(MAX_LISTENS, dtype=np.float32)\\n+ seen: set[int] = set()\\n+ n = 0\\n+ for p in range(end - 1, lo - 1, -1):\\n+ ts = int(self.e.timestamp[p])\\n+ if ts < min_ts:\\n+ break\\n+ item = int(self.e.item[p])\\n+ if item in seen:\\n+ continue\\n+ seen.add(item)\\n+ age_days = (cutoff - ts) / 86_400.0\\n+ completion = min(float(self.e.ratio[p]), 100.0) / 100.0\\n+ organic = 1.35 if self.e.organic[p] else 1.0\\n+ listen_i[n] = item + 1\\n+ listen_w[n] = completion * organic * (0.35 + 0.65 * math.exp(-age_days / 35.0))\\n+ n += 1\\n+ if n == MAX_LISTENS:\\n+ break\\n+\\n+ # Walk explicit actions backward: the first action seen for each\\n+ # (channel,item) is the cutoff-correct active state.\\n+ lo, hi = self.aoff[user], self.aoff[user + 1]\\n+ end = lo + np.searchsorted(self.a.timestamp[lo:hi], cutoff, side=\\\"right\\\")\\n+ likes = np.zeros(MAX_LIKES, dtype=np.int32)\\n+ dislikes = np.zeros(MAX_DISLIKES, dtype=np.int32)\\n+ seen_actions: set[tuple[int, int]] = set()\\n+ nl = nd = 0\\n+ for p in range(end - 1, lo - 1, -1):\\n+ channel, item = int(self.a.channel[p]), int(self.a.item[p])\\n+ key = (channel, item)\\n+ if key in seen_actions:\\n+ continue\\n+ seen_actions.add(key)\\n+ if not self.a.active[p]:\\n+ continue\\n+ if channel == 1 and nl < MAX_LIKES:\\n+ likes[nl] = item + 1\\n+ nl += 1\\n+ elif channel == -1 and nd < MAX_DISLIKES:\\n+ dislikes[nd] = item + 1\\n+ nd += 1\\n+ if nl == MAX_LIKES and nd == MAX_DISLIKES:\\n+ break\\n+\\n+ numeric = np.asarray(\\n+ [\\n+ math.log1p(n) / math.log1p(MAX_LISTENS),\\n+ math.log1p(nl) / math.log1p(MAX_LIKES),\\n+ math.log1p(nd) / math.log1p(MAX_DISLIKES),\\n+ float(self.e.organic[max(lo, end - 500) : end].mean())\\n+ if end > lo\\n+ else 0.0,\\n+ ],\\n+ dtype=np.float32,\\n+ )\\n+ return listen_i, listen_w, likes, dislikes, numeric\\n+\\n+ def many(self, users: np.ndarray, cutoff: int) -> tuple[np.ndarray, ...]:\\n+ rows = [self.one(int(u), cutoff) for u in users]\\n+ return tuple(np.stack([r[j] for r in rows]) for j in range(5))\\n+\\n+\\n+def provided_preference_features(\\n+ path: Path, user_map: IdMap, item_map: IdMap\\n+) -> tuple[np.ndarray, np.ndarray]:\\n+ x = pd.read_parquet(path)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv\\n+ x = pd.DataFrame(\\n+ {\\\"u\\\": u[keep], \\\"i\\\": i[keep] + 1, \\\"state\\\": x.state.to_numpy()[keep]}\\n+ )\\n+ likes = np.zeros((len(user_map.raw), MAX_LIKES), dtype=np.int32)\\n+ dislikes = np.zeros((len(user_map.raw), MAX_DISLIKES), dtype=np.int32)\\n+ for state, target, width in [\\n+ (\\\"liked\\\", likes, MAX_LIKES),\\n+ (\\\"disliked\\\", dislikes, MAX_DISLIKES),\\n+ ]:\\n+ y = x[x.state == state]\\n+ # File order is stable and active state contains no timestamp; retaining\\n+ # the final bounded set keeps request-time work constant.\\n+ y = y.groupby(\\\"u\\\", sort=False).tail(width)\\n+ for uid, vals in y.groupby(\\\"u\\\", sort=False).i:\\n+ arr = vals.to_numpy(dtype=np.int32)[-width:]\\n+ target[int(uid), : len(arr)] = arr\\n+ return likes, dislikes\\n+\\n+\\n+def labels_for_window(\\n+ events: EventData, actions: ActionData, cutoff: int\\n+) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n+ start, end = cutoff + GAP, cutoff + GAP + WEEK\\n+ strong = (\\n+ (events.timestamp >= start)\\n+ & (events.timestamp <= end)\\n+ & (events.organic == 1)\\n+ & (events.ratio.astype(np.float32) >= 80.0)\\n+ )\\n+ likes = (\\n+ (actions.timestamp >= start)\\n+ & (actions.timestamp <= end)\\n+ & (actions.organic == 1)\\n+ & (actions.channel == 1)\\n+ & (actions.active == 1)\\n+ )\\n+ pairs: dict[int, dict[int, int]] = {}\\n+ for u, i in zip(events.user[strong], events.item[strong]):\\n+ pairs.setdefault(int(u), {})[int(i)] = 1\\n+ for u, i in zip(actions.user[likes], actions.item[likes]):\\n+ pairs.setdefault(int(u), {})[int(i)] = 3\\n+ return {\\n+ u: (\\n+ np.fromiter(d.keys(), dtype=np.int32),\\n+ np.fromiter(d.values(), dtype=np.int8),\\n+ )\\n+ for u, d in pairs.items()\\n+ }\\n+\\n+\\n+class SnapshotDataset(Dataset):\\n+ def __init__(self, features: tuple[np.ndarray, ...], labels: list[tuple[np.ndarray, np.ndarray]]):\\n+ self.features = features\\n+ self.labels = labels\\n+\\n+ def __len__(self) -> int:\\n+ return len(self.labels)\\n+\\n+ def __getitem__(self, index: int):\\n+ items, grades = self.labels[index]\\n+ # A new positive is sampled on every epoch. Grade-3 actions are sampled\\n+ # and weighted more heavily to match their larger NDCG gain.\\n+ probs = np.where(grades == 3, 4.0, 1.0)\\n+ p = int(np.random.choice(len(items), p=probs / probs.sum()))\\n+ return tuple(torch.from_numpy(x[index]) for x in self.features) + (\\n+ torch.tensor(int(items[p]), dtype=torch.long),\\n+ torch.tensor(3.0 if grades[p] == 3 else 1.0, dtype=torch.float32),\\n+ )\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user tower; there is intentionally no user-ID table.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int = EMBED_DIM):\\n+ super().__init__()\\n+ self.listen = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.like = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.dislike = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim * 3 + 4, dim * 2),\\n+ nn.LayerNorm(dim * 2),\\n+ nn.GELU(),\\n+ nn.Dropout(0.08),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+ self.log_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+ @staticmethod\\n+ def mean_pool(embedding: nn.Embedding, ids: torch.Tensor, weights=None):\\n+ mask = ids.ne(0)\\n+ if weights is None:\\n+ weights = mask.float()\\n+ else:\\n+ weights = weights * mask\\n+ values = embedding(ids) * weights.unsqueeze(-1)\\n+ return values.sum(1) / weights.sum(1, keepdim=True).clamp_min(1e-5)\\n+\\n+ def forward(self, listens, listen_weights, likes, dislikes, numeric):\\n+ pooled = torch.cat(\\n+ [\\n+ self.mean_pool(self.listen, listens, listen_weights),\\n+ self.mean_pool(self.like, likes),\\n+ self.mean_pool(self.dislike, dislikes),\\n+ numeric,\\n+ ],\\n+ dim=1,\\n+ )\\n+ vector = F.normalize(self.net(pooled), dim=1)\\n+ return vector * self.log_scale.exp().clamp(2.0, 30.0)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently callable candidate tower.\\\"\\\"\\\"\\n+\\n+ def __init__(self, metadata: np.ndarray, dim: int = EMBED_DIM):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(len(metadata), dim)\\n+ self.register_buffer(\\\"metadata\\\", torch.tensor(metadata, dtype=torch.float32))\\n+ self.meta_net = nn.Sequential(nn.Linear(metadata.shape[1], 24), nn.GELU())\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim + 24, dim * 2),\\n+ nn.LayerNorm(dim * 2),\\n+ nn.GELU(),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+\\n+ def forward(self, item_index: torch.Tensor):\\n+ x = torch.cat(\\n+ [self.embedding(item_index), self.meta_net(self.metadata[item_index])], dim=-1\\n+ )\\n+ return F.normalize(self.net(x), dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, metadata: np.ndarray):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items)\\n+ self.item_tower = ItemTower(metadata)\\n+\\n+\\n+def item_metadata(catalog: pd.DataFrame) -> np.ndarray:\\n+ positive = catalog.positive_listens.to_numpy(dtype=np.float32)\\n+ organic = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\\n+ rank = catalog.popularity_rank.to_numpy(dtype=np.float32)\\n+ raw = np.column_stack(\\n+ [\\n+ np.log1p(positive),\\n+ np.log1p(organic),\\n+ organic / np.maximum(positive, 1),\\n+ -np.log1p(rank),\\n+ ]\\n+ ).astype(np.float32)\\n+ return (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\\n+\\n+\\n+def build_training_data(\\n+ builder: FeatureBuilder,\\n+ events: EventData,\\n+ actions: ActionData,\\n+ cutoffs: list[int],\\n+ supplied_train_preferences: tuple[np.ndarray, np.ndarray] | None,\\n+) -> SnapshotDataset:\\n+ feature_parts: list[tuple[np.ndarray, ...]] = []\\n+ all_labels: list[tuple[np.ndarray, np.ndarray]] = []\\n+ for cutoff in cutoffs:\\n+ labels = labels_for_window(events, actions, cutoff)\\n+ users = np.asarray(sorted(labels), dtype=np.int32)\\n+ features = list(builder.many(users, cutoff))\\n+ if supplied_train_preferences is not None and cutoff == TRAIN_END:\\n+ likes, dislikes = supplied_train_preferences\\n+ features[2] = likes[users]\\n+ features[3] = dislikes[users]\\n+ features[4][:, 1] = np.log1p((features[2] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((features[3] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ feature_parts.append(tuple(features))\\n+ all_labels.extend(labels[int(u)] for u in users)\\n+ print(f\\\"snapshot cutoff={cutoff}: {len(users)} labeled users, \\\"\\n+ f\\\"{sum(len(labels[int(u)][0]) for u in users)} positives\\\", flush=True)\\n+ merged = tuple(np.concatenate([p[j] for p in feature_parts]) for j in range(5))\\n+ return SnapshotDataset(merged, all_labels)\\n+\\n+\\n+def train_model(\\n+ model: TwoTower,\\n+ dataset: SnapshotDataset,\\n+ popularity: np.ndarray,\\n+ epochs: int,\\n+ seed: int,\\n+) -> None:\\n+ generator = torch.Generator().manual_seed(seed)\\n+ loader = DataLoader(dataset, batch_size=384, shuffle=True, generator=generator,\\n+ num_workers=0, drop_last=False)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ pop = torch.tensor(popularity, dtype=torch.float32)\\n+ pop = pop / pop.sum()\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ total = 0.0\\n+ seen = 0\\n+ for batch in loader:\\n+ *features, positive, grade_weight = batch\\n+ user_vec = model.user_tower(*features)\\n+ pos_vec = model.item_tower(positive)\\n+ # Half popularity-aware and half uniform negatives. This focuses\\n+ # learning on the candidates likely to compete near rank 10.\\n+ b = len(positive)\\n+ hard = torch.multinomial(pop, b * 8, replacement=True).view(b, 8)\\n+ uniform = torch.randint(0, len(pop), (b, 8))\\n+ negatives = torch.cat([hard, uniform], dim=1)\\n+ neg_vec = model.item_tower(negatives)\\n+ pos_score = (user_vec * pos_vec).sum(1)\\n+ neg_score = (user_vec[:, None, :] * neg_vec).sum(2)\\n+ pair_loss = F.softplus(neg_score - pos_score[:, None]).mean(1)\\n+ loss = (pair_loss * grade_weight).sum() / grade_weight.sum()\\n+ # Small embedding penalty prevents the history lookup from merely\\n+ # memorizing the short collection of rolling snapshots.\\n+ loss = loss + 2e-5 * (\\n+ model.item_tower.embedding.weight.square().mean()\\n+ + model.user_tower.listen.weight.square().mean()\\n+ )\\n+ optimizer.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ total += float(loss) * b\\n+ seen += b\\n+ print(f\\\"epoch {epoch + 1:02d}/{epochs}: loss={total / seen:.5f}\\\", flush=True)\\n+\\n+\\n+@torch.inference_mode()\\n+def write_ranking(\\n+ model: TwoTower,\\n+ features: tuple[np.ndarray, ...],\\n+ eligibility_path: Path,\\n+ destination: Path,\\n+) -> None:\\n+ model.eval()\\n+ archive = np.load(eligibility_path)\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ raw_items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+ item_vectors = model.item_tower(torch.arange(len(raw_items))).cpu()\\n+ rows: list[pd.DataFrame] = []\\n+ batch_size = 128\\n+ for start in range(0, len(uids), batch_size):\\n+ stop = min(start + batch_size, len(uids))\\n+ tensors = [torch.from_numpy(x[start:stop]) for x in features]\\n+ user_vectors = model.user_tower(*tensors).cpu()\\n+ scores = user_vectors @ item_vectors.T\\n+ eligible = np.unpackbits(\\n+ packed[start:stop], axis=1, count=len(raw_items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores[~torch.from_numpy(eligible)] = -torch.inf\\n+ top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\\n+ for r in range(stop - start):\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": uids[start + r],\\n+ \\\"item_id\\\": raw_items[top[r]],\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ result = pd.concat(rows, ignore_index=True).astype(\\\"int64\\\")\\n+ temp = destination.with_suffix(\\\".tmp.parquet\\\")\\n+ result.to_parquet(temp, index=False)\\n+ temp.replace(destination)\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=12)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ parser.add_argument(\\\"--output\\\", type=Path)\\n+ args = parser.parse_args()\\n+ seed_everything(args.seed)\\n+ torch.set_num_threads(min(12, os.cpu_count() or 1))\\n+ started = time.time()\\n+\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ archive_name = f\\\"{args.stage}_eligible_candidates.npz\\\"\\n+ archive = np.load(DATA / archive_name)\\n+ raw_users = archive[\\\"uids\\\"].astype(np.int64)\\n+ raw_items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ assert np.array_equal(raw_items, catalog.item_id.to_numpy(dtype=np.int64))\\n+ user_map, item_map = IdMap(raw_users), IdMap(raw_items)\\n+\\n+ include_val = args.stage == \\\"test\\\"\\n+ # Eight clean rolling windows ending before public validation. In final\\n+ # mode, the public validation window is appended as one more supervised\\n+ # snapshot, exactly matching the requested refit protocol.\\n+ cutoffs = [TRAIN_END - n * STEP for n in range(8, 0, -1)]\\n+ if include_val:\\n+ cutoffs.append(TRAIN_END)\\n+ earliest = min(cutoffs) - HISTORY_DAYS * 86_400\\n+ listen_files = [\\\"train_listens.parquet\\\"]\\n+ if include_val:\\n+ listen_files.append(\\\"val_listens.parquet\\\")\\n+ print(f\\\"loading event streams since {earliest}\\\", flush=True)\\n+ events = load_listens(listen_files, user_map, item_map, earliest)\\n+ actions = load_actions(include_val, user_map, item_map, earliest)\\n+ print(f\\\"loaded {len(events.user):,} positive listens and {len(actions.user):,} actions\\\", flush=True)\\n+ builder = FeatureBuilder(events, actions, len(raw_users))\\n+\\n+ validation_preferences = provided_preference_features(\\n+ DATA / \\\"validation_preference_state.parquet\\\", user_map, item_map\\n+ )\\n+ dataset = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ )\\n+ model = TwoTower(len(raw_items), item_metadata(catalog))\\n+ popularity = np.sqrt(catalog.organic_positive_listens.to_numpy(dtype=np.float64) + 5.0)\\n+ train_model(model, dataset, popularity, args.epochs, args.seed)\\n+\\n+ if args.stage == \\\"validation\\\":\\n+ scoring_cutoff = TRAIN_END\\n+ preferences = validation_preferences\\n+ default_output = OUT / \\\"validation.parquet\\\"\\n+ else:\\n+ scoring_cutoff = TRAIN_END + STEP\\n+ preferences = provided_preference_features(\\n+ DATA / \\\"test_preference_state.parquet\\\", user_map, item_map\\n+ )\\n+ default_output = OUT / \\\"submission.parquet\\\"\\n+ features = list(builder.many(np.arange(len(raw_users), dtype=np.int32), scoring_cutoff))\\n+ features[2], features[3] = preferences\\n+ features[4][:, 1] = np.log1p((preferences[0] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((preferences[1] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ destination = args.output or default_output\\n+ write_ranking(model, tuple(features), DATA / archive_name, destination)\\n+ checkpoint = OUT / f\\\"model_{args.stage}.pt\\\"\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"stage\\\": args.stage,\\n+ \\\"seed\\\": args.seed,\\n+ \\\"epochs\\\": args.epochs,\\n+ \\\"architecture\\\": \\\"history pooling user tower / ID+metadata item tower / 64D dot product\\\",\\n+ },\\n+ checkpoint,\\n+ )\\n+ print(json.dumps({\\\"ranking\\\": str(destination), \\\"checkpoint\\\": str(checkpoint),\\n+ \\\"training_examples\\\": len(dataset),\\n+ \\\"elapsed_seconds\\\": round(time.time() - started, 1)}), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:34:25.252Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-08-31T18:34:27.697Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:34:27.697Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:34:30.896Z", "callId": "call_d3hVveuQTenC9iGtKEIjJxJk", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:34:32.075Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"fda1b0\",\"wall_time_seconds\":1.001470468,\"session_id\":96139,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-08-31T18:34:33.899Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:34:33.899Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:34:35.226Z", "callId": "call_bDuSEEVfi4AYLS5Hkv7uTF5f", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:34:46.244Z", "content": "Script running with cell ID 13\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-08-31T18:34:49.900Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:34:49.900Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:34:50.667Z", "callId": "call_Nrf9MoX2Tozbn6GdirY3n8Mv", "name": "wait", "input": { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:35:05.248Z", "content": "Script completed\nWall time 14.6 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 5318 labeled users, 164063 positives\nsnapshot cutoff=20540600: 5392 labeled users, 165953 positives\nsnapshot cutoff=21147200: 5411 labeled users, 158923 positives\nsnapshot cutoff=21753800: 5563 labeled users, 165819 positives\nsnapshot cutoff=22360400: 5650 labeled users, 168735 positives\nsnapshot cutoff=22967000: 5754 labeled users, 170431 positives\nsnapshot cutoff=23573600: 5830 labeled users, 171275 positives\nsnapshot cutoff=24180200: 5989 labeled users, 176193 positives\n/app/solution/build_ranker.py:478: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\nepoch 01/10: loss=0.59321\n", "isError": null } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-08-31T18:35:09.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:35:09.022Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:35:11.891Z", "text": "The rolling dataset is healthy: each weekly snapshot has about 5,300–6,000 labeled users and roughly 160,000–176,000 relevant candidate events, for over 1.3 million positive labels across eight leakage-free backtests. Training has started; the first epoch’s sampled ranking loss is 0.593." }, { "kind": "tool", "timestamp": "2026-08-31T18:35:13.247Z", "callId": "call_Da1771gyyfoyGJTuWT5utU9a", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:35:24.299Z", "content": "Script running with cell ID 14\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-08-31T18:35:26.245Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:35:26.245Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:35:27.184Z", "callId": "call_S2NZunZlsPSXDPT2Rpalb1Nn", "name": "wait", "input": { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:35:43.310Z", "content": "Script completed\nWall time 16.1 seconds\nOutput:\n\nepoch 02/10: loss=0.54179\nepoch 03/10: loss=0.51136\nepoch 04/10: loss=0.48345\nepoch 05/10: loss=0.46498\nepoch 06/10: loss=0.44869\n", "isError": null } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-08-31T18:35:45.142Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:35:45.142Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:35:46.506Z", "callId": "call_RL3FRFy4FRLj32exPYRZ5ABk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:35:57.557Z", "content": "Script running with cell ID 15\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-08-31T18:35:59.131Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:35:59.131Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:35:59.950Z", "callId": "call_xJCGkUUgoVjYOKHtRvi8AcwK", "name": "wait", "input": { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:36:16.551Z", "content": "Script completed\nWall time 16.6 seconds\nOutput:\n\nepoch 07/10: loss=0.43527\nepoch 08/10: loss=0.41981\nepoch 09/10: loss=0.40792\nepoch 10/10: loss=0.39613\n", "isError": null } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-08-31T18:36:18.529Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:36:18.529Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:36:19.904Z", "callId": "call_2aGUHDWOcjNmegTTC3WJBhWX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:36:20.000Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"ranking\": \"/app/solution/validation.parquet\", \"checkpoint\": \"/app/solution/model_validation.pt\", \"training_examples\": 44907, \"elapsed_seconds\": 102.9}\n", "isError": null } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-08-31T18:36:21.619Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:36:21.619Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:36:23.762Z", "callId": "call_vcB0U2WMj13iaziFE0yzyfXm", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:36:27.111Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016758,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-08-31T18:36:41.253Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:36:41.253Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:36:45.134Z", "text": "The first learned model reaches 0.016758 NDCG@10, a 56% lift over the 0.010710 popularity floor. I’m preserving it now and testing a small set of validation-only rank fusions with the catalog prior; this often stabilizes a sampled two-tower’s top ten without changing the model’s production decomposition. Any fusion retained for the final model will be representable as fixed dot-product dimensions, not a request-time cross feature." }, { "kind": "tool", "timestamp": "2026-08-31T18:37:09.925Z", "callId": "call_FLYYCQAf5rpGWYaF4R1pbE2w", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_fusion.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fuse two-tower order with a catalog prior for validation diagnostics.\\n+\\n+Reciprocal-rank fusion is only a tuning helper. The selected catalog prior is\\n+already available to the item tower and can be absorbed into fixed extra dot-\\n+product dimensions in a deployed model.\\n+\\\"\\\"\\\"\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+\\n+\\n+def main():\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--input\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--model-weight\\\", type=float, required=True)\\n+ p.add_argument(\\\"--constant\\\", type=float, default=30.0)\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], default=\\\"validation\\\")\\n+ args = p.parse_args()\\n+ frame = pd.read_parquet(args.input).sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ by_user = {int(u): g.item_id.to_numpy(dtype=np.int64) for u, g in frame.groupby(\\\"uid\\\")}\\n+ z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ out = []\\n+ for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n+ eligible = np.unpackbits(z[\\\"packed_eligible\\\"][row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ pop = items[eligible][:400]\\n+ model = by_user[int(uid)]\\n+ candidates = np.unique(np.concatenate([model, pop]))\\n+ mr = {int(x): r for r, x in enumerate(model, 1)}\\n+ pr = {int(x): r for r, x in enumerate(pop, 1)}\\n+ c = args.constant\\n+ score = np.asarray([\\n+ args.model_weight / (c + mr.get(int(x), 1000))\\n+ + (1.0 - args.model_weight) / (c + pr.get(int(x), 1000))\\n+ for x in candidates\\n+ ])\\n+ chosen = candidates[np.argsort(-score, kind=\\\"stable\\\")[:100]]\\n+ out.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(out, ignore_index=True).astype(\\\"int64\\\").to_parquet(args.output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:37:09.989Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-08-31T18:37:15.339Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:37:15.339Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:37:21.621Z", "callId": "call_sJHuuDb4oPWcqvzy3QbBFvWM", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:37:32.642Z", "content": "Script running with cell ID 19\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-08-31T18:37:34.498Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:37:34.498Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:37:35.307Z", "callId": "call_kKiGpLecYF9WHHayEbvKCjkj", "name": "wait", "input": { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:37:51.805Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-08-31T18:37:55.471Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:37:55.471Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:37:58.172Z", "callId": "call_v5V3OmpuZhBR9ea0MqAQK8vh", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_fusion_*.parquet 2>/dev/null || true\\nps -ef | rg 'rank_fusion|evaluate_public' || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:37:58.259Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 2: ps: not found\n", "isError": null } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-08-31T18:38:01.166Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:38:01.166Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:38:04.950Z", "callId": "call_SHmlbPojRK1J8mcW6LiiK1B6", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lah /app/solution | tail -30\\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:38:15.971Z", "content": "Script running with cell ID 21\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-08-31T18:38:18.837Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:38:18.837Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:38:19.617Z", "callId": "call_PRLk47ygY0NAnRPgioTYZ0EW", "name": "wait", "input": { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-08-31T18:38:35.134Z", "content": "Script completed\nWall time 15.5 seconds\nOutput:\n\ntotal 33M\ndrwxr-xr-x 1 agent agent 4.0K Aug 31 18:37 .\ndrwxr-xr-x 1 agent agent 4.0K Aug 31 18:22 ..\ndrwxr-xr-x 2 agent agent 4.0K Aug 31 18:34 __pycache__\n-rw-r--r-- 1 agent agent 986 Aug 31 18:28 baseline.py\n-rw-r--r-- 1 agent agent 23K Aug 31 18:34 build_ranker.py\n-rw-r--r-- 1 agent agent 30M Aug 31 18:36 model_validation.pt\n-rw-r--r-- 1 agent agent 2.0K Aug 31 18:37 rank_fusion.py\n-rw-r--r-- 1 agent agent 1.5M Aug 31 18:36 validation.parquet\n-rw-r--r-- 1 agent agent 712K Aug 31 18:28 validation_popularity.parquet\n", "isError": null } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-08-31T18:38:37.762Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:38:37.762Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:38:40.783Z", "callId": "call_FtGiKWfUcLuHvWLm5G77M8qp", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_fusion_0.75.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_0.75.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:38:40.847Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nls: cannot access '/app/solution/validation_fusion_0.75.parquet': No such file or directory\n", "isError": null } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-08-31T18:38:44.831Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:38:44.831Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:38:48.150Z", "callId": "call_jtPsmgw36TMONzqNzKMhKo3B", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:38:49.337Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"682eb1\",\"wall_time_seconds\":1.001457047,\"session_id\":99485,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-08-31T18:38:51.377Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:38:51.377Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:38:52.724Z", "callId": "call_PZW08y96sJzqrJE2FbYXv8vB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:39:03.745Z", "content": "Script running with cell ID 24\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-08-31T18:39:05.286Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:39:05.286Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:39:06.038Z", "callId": "call_MZmJRgMal6wlRbvdbZOQaIB4", "name": "wait", "input": { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:39:22.750Z", "content": "Script completed\nWall time 16.7 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-08-31T18:39:24.626Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:39:24.626Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:39:25.964Z", "callId": "call_zztQY2sjpO4p9MoCbD1eZCpt", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:39:36.988Z", "content": "Script running with cell ID 25\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-08-31T18:39:39.857Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:39:39.857Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:39:40.634Z", "callId": "call_7erquiT1X5lDlPYpT7ZWI6Gw", "name": "wait", "input": { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:39:55.993Z", "content": "Script completed\nWall time 15.4 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-08-31T18:39:58.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:39:58.022Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:39:59.359Z", "callId": "call_3MelE3aqzenswTU0al6biJMH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:40:10.380Z", "content": "Script running with cell ID 26\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-08-31T18:40:11.929Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:40:11.929Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:40:12.690Z", "callId": "call_1Tdpd8Lj8oV7E8xXFnLLCgTV", "name": "wait", "input": { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:40:29.386Z", "content": "Script completed\nWall time 16.7 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-08-31T18:40:31.769Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:40:31.769Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:40:33.386Z", "callId": "call_IEqJZbhsXkYsWxBUhzcZ0w3b", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:40:44.404Z", "content": "Script running with cell ID 27\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-08-31T18:40:46.273Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:40:46.273Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:40:47.095Z", "callId": "call_HBWWvuEdFpZ5M9D1fvxkAnUZ", "name": "wait", "input": { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:41:03.409Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-08-31T18:41:04.890Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:41:04.890Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:41:06.257Z", "callId": "call_r99N8SGGZYkIp6ols5Z3CJoM", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:41:17.279Z", "content": "Script running with cell ID 28\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-08-31T18:41:21.911Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:41:21.911Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:41:23.145Z", "callId": "call_QeNonEW5mXjxLkMIFYSzksKK", "name": "wait", "input": { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:41:36.284Z", "content": "Script completed\nWall time 13.1 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-08-31T18:41:37.972Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:41:37.972Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:41:41.670Z", "callId": "call_O1o7NM7yiMi1cUKgCiZlaQ8u", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:41:41.752Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/bin/sh -c for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done \n PID=487\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.55.parquet --model-weight 0.55 --constant 30 \n PID=488\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30 \n PID=522\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30 \n PID=525\n/bin/sh -c /opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 \n PID=555\n/opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 \n PID=556\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=60\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done \n PID=619\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=66\n", "isError": null } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-08-31T18:41:45.111Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:41:45.111Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:41:49.495Z", "callId": "call_eyeVESp2RFE38qvhHjRQNMoV", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 487 488 522 525 555 556 2>/dev/null || true\\nfor d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:41:49.574Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nfor d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'rank_fusion' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done \n PID=649\n", "isError": null } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-08-31T18:41:57.915Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:41:57.915Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:42:12.752Z", "callId": "call_TjePelrPXV7FYpUy5Jx1XJIK", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n- by_user = {int(u): g.item_id.to_numpy(dtype=np.int64) for u, g in frame.groupby(\\\"uid\\\")}\\n z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ frame = frame.set_index([\\\"uid\\\", \\\"rank\\\"])\\n+ model_matrix = np.stack([\\n+ frame.loc[(int(uid), slice(None)), \\\"item_id\\\"].to_numpy(dtype=np.int64)\\n+ for uid in z[\\\"uids\\\"]\\n+ ])\\n items = z[\\\"item_ids\\\"].astype(np.int64)\\n- out = []\\n+ out_items = np.empty((len(z[\\\"uids\\\"]), 100), dtype=np.int64)\\n for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n@@\\n- model = by_user[int(uid)]\\n+ model = model_matrix[row]\\n@@\\n- out.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}))\\n- pd.concat(out, ignore_index=True).astype(\\\"int64\\\").to_parquet(args.output, index=False)\\n+ out_items[row] = chosen\\n+ result = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(z[\\\"uids\\\"].astype(np.int64), 100),\\n+ \\\"item_id\\\": out_items.ravel(),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(out_items)),\\n+ })\\n+ result.to_parquet(args.output, index=False)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:42:12.812Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-08-31T18:42:16.255Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:42:16.255Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:42:20.465Z", "callId": "call_kmih5DnUviZRGSW9owhHxZQ9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"time /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:42:22.538Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\n/bin/sh: 1: time: not found\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_fusion_075.parquet'\n", "isError": null } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-08-31T18:42:25.021Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:42:25.021Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:42:29.293Z", "callId": "call_m1BOs9zgF5od67C9kjWXtOCA", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:42:40.314Z", "content": "Script running with cell ID 33\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-08-31T18:42:41.801Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:42:41.801Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:42:42.577Z", "callId": "call_8eSvxXnriqqt5Y0FS22g6elw", "name": "wait", "input": { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-08-31T18:42:59.474Z", "content": "Script completed\nWall time 16.9 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-08-31T18:43:01.759Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:01.759Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:06.310Z", "callId": "call_8sT3czUj97H8oRZyfB0ny8pv", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:43:06.388Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=60\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=66\n/bin/sh -c /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet \n PID=700\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 \n PID=701\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done\n PID=728\n", "isError": null } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-08-31T18:43:08.141Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:08.141Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:11.871Z", "callId": "call_QDGDYv8Hs7SoluSkrCaJR7v0", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for p in 700 701; do if [ -e /proc/$p/status ]; then printf 'PID %s ' $p; rg '^(State|VmRSS|VmSize|Threads):' /proc/$p/status; fi; done\\ncat /proc/701/io 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:43:11.932Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nPID 700 State:\tS (sleeping)\nVmSize:\t 2592 kB\nVmRSS:\t 1644 kB\nThreads:\t1\nPID 701 State:\tR (running)\nVmSize:\t 1557936 kB\nVmRSS:\t 269092 kB\nThreads:\t16\nrchar: 3132077045\nwchar: 0\nsyscr: 101389\nsyscw: 0\nread_bytes: 0\nwrite_bytes: 0\ncancelled_write_bytes: 0\n", "isError": null } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-08-31T18:43:16.866Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:16.866Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:18.813Z", "callId": "call_x1tPYUSmX18NvEvSWnBvC1XF", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 700 701 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:43:18.882Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-08-31T18:43:20.729Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:20.729Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:28.530Z", "callId": "call_jnzfSQD6zI1rIqrCa6uo7ACI", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n- frame = frame.set_index([\\\"uid\\\", \\\"rank\\\"])\\n- model_matrix = np.stack([\\n- frame.loc[(int(uid), slice(None)), \\\"item_id\\\"].to_numpy(dtype=np.int64)\\n- for uid in z[\\\"uids\\\"]\\n- ])\\n+ ordered = frame.sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ ordered_uids = ordered.uid.to_numpy(dtype=np.int64).reshape(-1, 100)[:, 0]\\n+ if not np.array_equal(ordered_uids, z[\\\"uids\\\"].astype(np.int64)):\\n+ raise ValueError(\\\"ranking users do not align with eligibility archive\\\")\\n+ model_matrix = ordered.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:43:28.602Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-08-31T18:43:30.395Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:30.395Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:34.739Z", "callId": "call_deLzmpXSSt07gse6TBU79FYL", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:43:45.760Z", "content": "Script running with cell ID 38\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-08-31T18:43:47.743Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:43:47.743Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:43:48.571Z", "callId": "call_o5AYNlg1cs6jk3EInTKGga7o", "name": "wait", "input": { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-08-31T18:44:04.921Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-08-31T18:44:06.887Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:44:06.887Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:44:11.419Z", "callId": "call_OwIkHp0hJ3RvwEc9h77f7mUk", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:44:11.492Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=60\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=66\n/bin/sh -c /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet \n PID=766\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 \n PID=767\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done\n PID=793\n", "isError": null } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-08-31T18:44:14.340Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:44:14.340Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:44:17.539Z", "callId": "call_bCASpqAiZ2pJd1yNXhKUcf2s", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for p in 766 767; do if [ -e /proc/$p/status ]; then printf 'PID %s ' $p; rg '^(State|VmRSS|VmSize|Threads):' /proc/$p/status; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:44:17.606Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nPID 766 State:\tS (sleeping)\nVmSize:\t 2592 kB\nVmRSS:\t 1704 kB\nThreads:\t1\nPID 767 State:\tR (running)\nVmSize:\t 1614860 kB\nVmRSS:\t 252036 kB\nThreads:\t17\n", "isError": null } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-08-31T18:44:19.783Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:44:19.783Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:44:21.115Z", "callId": "call_w6HaR8GTFL6Ca0o5hBBPZP0w", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:766,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:44:21.198Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\nwrite_stdin failed: Unknown process id 766", "isError": null } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-08-31T18:44:24.797Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:44:24.797Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:44:28.221Z", "callId": "call_yd4hKNqWnf7kptJfR23lZ8tK", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"i=0; while [ -e /proc/767 ] && [ $i -lt 30 ]; do sleep 1; i=$((i+1)); done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:44:39.245Z", "content": "Script running with cell ID 42\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-08-31T18:44:41.336Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:44:41.336Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:44:42.115Z", "callId": "call_k8s00NVsCehBR8V3vKpsGram", "name": "wait", "input": { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 1000 }, "result": { "timestamp": "2026-08-31T18:44:58.298Z", "content": "Script completed\nWall time 16.2 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-08-31T18:45:01.106Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:45:01.106Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:45:05.837Z", "callId": "call_36afP8uWcs6Yuj4TBP6djPdB", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"if [ -e /proc/767/status ]; then rg '^(State|VmRSS|voluntary_ctxt_switches|nonvoluntary_ctxt_switches):' /proc/767/status; cat /proc/767/io; fi\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:45:05.966Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nState:\tR (running)\nVmRSS:\t 252036 kB\nvoluntary_ctxt_switches:\t4\nnonvoluntary_ctxt_switches:\t641\nrchar: 7270611151\nwchar: 0\nsyscr: 234454\nsyscw: 0\nread_bytes: 0\nwrite_bytes: 0\ncancelled_write_bytes: 0\n", "isError": null } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-08-31T18:45:13.844Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:45:13.844Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:45:15.675Z", "callId": "call_AcIDdas8Wn7sSRTg9Wfh1emk", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 766 767 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:45:15.754Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-08-31T18:45:17.401Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:45:17.401Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:45:28.830Z", "callId": "call_BBOGcAxrqy8kMAtzEvkZLOkr", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n@@\\n- if not np.array_equal(ordered_uids, z[\\\"uids\\\"].astype(np.int64)):\\n+ if not np.array_equal(ordered_uids, uids):\\n@@\\n- out_items = np.empty((len(z[\\\"uids\\\"]), 100), dtype=np.int64)\\n- for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n- eligible = np.unpackbits(z[\\\"packed_eligible\\\"][row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ out_items = np.empty((len(uids), 100), dtype=np.int64)\\n+ for row, uid in enumerate(uids):\\n+ eligible = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n@@\\n- \\\"uid\\\": np.repeat(z[\\\"uids\\\"].astype(np.int64), 100),\\n+ \\\"uid\\\": np.repeat(uids, 100),\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:45:28.922Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-08-31T18:45:30.699Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:45:30.699Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:45:36.806Z", "callId": "call_9iXnoIKiYIO1yn0WyDA6wrwq", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:45:47.826Z", "content": "Script running with cell ID 46\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-08-31T18:45:49.361Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:45:49.361Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:45:50.141Z", "callId": "call_XQxVQ4JA9cnDM08kbv5vHoD6", "name": "wait", "input": { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:46:06.987Z", "content": "Script completed\nWall time 16.8 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_fusion_0.55.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016251,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-08-31T18:46:10.484Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:46:10.484Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:46:14.226Z", "callId": "call_cxuhll1g6G7u9aevFfT6dno2", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'validation_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:46:14.306Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_0.85.parquet \n PID=1029\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'validation_fusion|evaluate_public' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done \n PID=1048\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=60\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\n PID=66\n/bin/sh -c for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done \n PID=885\n", "isError": null } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-08-31T18:46:16.008Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:46:16.008Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:46:18.001Z", "callId": "call_xxkG9CmksJNLQqE7FC3Mh7QY", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"while [ -e /proc/885 ]; do sleep 1; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:46:18.071Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-08-31T18:46:20.391Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:46:20.391Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:46:23.756Z", "callId": "call_2d9YIx891O6D6OotBznoB0Au", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:46:34.778Z", "content": "Script running with cell ID 49\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-08-31T18:46:36.992Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:46:36.992Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:46:37.788Z", "callId": "call_ZnV9tDB3Zz9J0Wn77tleMxJK", "name": "wait", "input": { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:46:40.441Z", "content": "Script completed\nWall time 2.7 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_fusion_0.55.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016251,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_fusion_0.65.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016701,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_fusion_0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017016,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_fusion_0.85.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016989,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_fusion_0.92.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017035,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-08-31T18:46:58.713Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:46:58.713Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:47:01.536Z", "text": "Fusion testing shows only a modest gain, peaking at 0.017035 with a 92% model weight, so personalization is carrying nearly all of the lift. I’m keeping that artifact and inspecting hit rates by relevance grade before the next training run; this will tell me whether to spend capacity on explicit likes or strong organic listens." }, { "kind": "tool", "timestamp": "2026-08-31T18:47:21.307Z", "callId": "call_BqeLNCBtpVQh6zKvS7urEcfI", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom ranking_evaluation import CandidateSet,ranked_items,validate_ranking,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); gold=validation_truth(e,targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nfor name in ['validation_popularity.parquet','validation.parquet','validation_fusion_0.92.parquet']:\\n x=validate_ranking(pd.read_parquet('/app/solution/'+name),targets); rec=ranked_items(x)\\n hit1=hit3=total1=total3=users_hit=0; vals=[]\\n for u,g in gold.items():\\n top=set(rec[u][:10]); h=[g.get(i,0) for i in top]\\n hit1+=sum(v==1 for v in h); hit3+=sum(v==3 for v in h); total1+=sum(v==1 for v in g.values()); total3+=sum(v==3 for v in g.values()); users_hit+=any(v for v in h); vals.append(ndcg_at_k(rec[u],g,10))\\n print(name,'ndcg',np.mean(vals),'users_hit',users_hit,'hits grade1/3',hit1,hit3,'recall',hit1/total1,hit3/max(total3,1))\\nranks1=[];ranks3=[]\\nfor g in gold.values():\\n for i,v in g.items(): (ranks3 if v==3 else ranks1).append(rank[i])\\nfor n,a in [('g1',ranks1),('g3',ranks3)]: print(n,'n',len(a),'pop rank quantiles',np.quantile(a,[0,.1,.25,.5,.75,.9,.99,1]))\\nprint('gold users avg grades',np.mean([len(g) for g in gold.values()]),'grade3 total',len(ranks3),'grade1',len(ranks1))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:47:25.401Z", "content": "Script completed\nWall time 4.1 seconds\nOutput:\n\nvalidation_popularity.parquet ndcg 0.010709951701903768 users_hit 229 hits grade1/3 334 34 recall 0.010014391940513312 0.007697532261716097\nvalidation.parquet ndcg 0.016757940681045823 users_hit 348 hits grade1/3 408 71 recall 0.012233149436315663 0.01607425854652479\nvalidation_fusion_0.92.parquet ndcg 0.01703496952046796 users_hit 355 hits grade1/3 413 69 recall 0.012383065483329335 0.015621462531129726\ng1 n 33352 pop rank quantiles [1.00000e+00 4.08000e+02 1.69375e+03 5.54500e+03 1.32740e+04 2.16560e+04\n 2.91020e+04 3.00000e+04]\ng3 n 4417 pop rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04\n 2.88126e+04 2.99890e+04]\ngold users avg grades 8.859723199624677 grade3 total 4417 grade1 33352\n", "isError": null } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-08-31T18:47:35.640Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:47:35.640Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:47:38.782Z", "callId": "call_5kkQTkVJ1ioW4T9VmCuvoA5Y", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 18 --seed 20260901 --output /app/solution/validation_seed2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:47:39.958Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"1846ae\",\"wall_time_seconds\":1.000883111,\"session_id\":47446,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-08-31T18:47:59.785Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:47:59.785Z", "text": "" } ] }, { "id": 81, "role": "agent", "timestamp": "2026-08-31T18:48:02.119Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:48:02.119Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:48:03.340Z", "callId": "call_zLWbhpKoBnonGySVbOQFIlGT", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:48:14.361Z", "content": "Script running with cell ID 52\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-08-31T18:48:16.281Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:48:16.281Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:48:17.073Z", "callId": "call_kiwz2lWphFYqpQXCnDPh6Cp8", "name": "wait", "input": { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:48:33.366Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 5318 labeled users, 164063 positives\nsnapshot cutoff=20540600: 5392 labeled users, 165953 positives\nsnapshot cutoff=21147200: 5411 labeled users, 158923 positives\nsnapshot cutoff=21753800: 5563 labeled users, 165819 positives\nsnapshot cutoff=22360400: 5650 labeled users, 168735 positives\nsnapshot cutoff=22967000: 5754 labeled users, 170431 positives\nsnapshot cutoff=23573600: 5830 labeled users, 171275 positives\nsnapshot cutoff=24180200: 5989 labeled users, 176193 positives\n/app/solution/build_ranker.py:478: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\nepoch 01/18: loss=0.58654\nepoch 02/18: loss=0.53702\nepoch 03/18: loss=0.50527\n", "isError": null } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-08-31T18:48:35.586Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:48:35.586Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:48:36.997Z", "callId": "call_9tEnm5sxinYo8SOgnXb3MJwW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:48:48.047Z", "content": "Script running with cell ID 53\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-08-31T18:48:58.322Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:48:58.322Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:48:59.126Z", "callId": "call_ANbK7RNe6vuKGrMA0yqcWRDx", "name": "wait", "input": { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:49:07.048Z", "content": "Script completed\nWall time 7.9 seconds\nOutput:\n\nepoch 04/18: loss=0.48211\nepoch 05/18: loss=0.46221\nepoch 06/18: loss=0.44632\nepoch 07/18: loss=0.43340\nepoch 08/18: loss=0.41662\n", "isError": null } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-08-31T18:49:08.632Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:49:08.632Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:49:10.015Z", "callId": "call_nMoDqNLQXp4loY0cQsaJ6KOs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:49:21.069Z", "content": "Script running with cell ID 54\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-08-31T18:49:24.122Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:49:24.122Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:49:24.826Z", "callId": "call_03H0TqUCB16YedHWecgqL6eq", "name": "wait", "input": { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:49:40.080Z", "content": "Script completed\nWall time 15.3 seconds\nOutput:\n\nepoch 09/18: loss=0.40759\nepoch 10/18: loss=0.39683\nepoch 11/18: loss=0.38677\nepoch 12/18: loss=0.37735\n", "isError": null } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-08-31T18:49:41.487Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:49:41.487Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:49:42.880Z", "callId": "call_mKx47Hi29mvcwZcfCJISiNfd", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:49:53.922Z", "content": "Script running with cell ID 55\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-08-31T18:49:55.547Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:49:55.547Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:49:56.337Z", "callId": "call_smHdtAQ1cFlJJ3cjyfDyGObm", "name": "wait", "input": { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:50:12.919Z", "content": "Script completed\nWall time 16.6 seconds\nOutput:\n\nepoch 13/18: loss=0.37004\nepoch 14/18: loss=0.35896\nepoch 15/18: loss=0.34928\nepoch 16/18: loss=0.34116\n", "isError": null } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-08-31T18:50:14.975Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:50:14.975Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:50:16.321Z", "callId": "call_y6vgAzdBzcA3ZzKCmqogNPuO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:50:27.364Z", "content": "Script running with cell ID 56\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-08-31T18:50:29.183Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:50:29.183Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:50:30.114Z", "callId": "call_CmyVKL7DI3SNMRRawAc74c8d", "name": "wait", "input": { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:50:30.211Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nepoch 17/18: loss=0.33154\nepoch 18/18: loss=0.32212\n{\"ranking\": \"/app/solution/validation_seed2.parquet\", \"checkpoint\": \"/app/solution/model_validation.pt\", \"training_examples\": 44907, \"elapsed_seconds\": 165.5}\n", "isError": null } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-08-31T18:50:33.176Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:50:33.176Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:50:36.984Z", "callId": "call_LuwCJHbRKmFJy0oFzKz0C11C", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_seed2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:50:40.333Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_seed2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011489,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-08-31T18:50:49.545Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:50:49.545Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:50:53.200Z", "text": "The longer run overfit: despite continuing loss reduction, its public score fell to 0.011489 at epoch 18. I’m rejecting it and retaining the 10-epoch ranking. This is useful evidence that the rolling objective needs early stopping near 8–10 epochs; next I’m checking how much validation relevance consists of repeat listening, which determines whether a recency-aware history pool or broader latent discovery signal deserves the next iteration." }, { "kind": "tool", "timestamp": "2026-08-31T18:51:26.784Z", "callId": "call_elJk0Cn4e31p8YHr8CCSOAfd", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/repeat_diagnostic.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build a history-repeat diagnostic ranking (not the submitted model).\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+\\n+\\n+def main():\\n+ users = pd.read_parquet(D / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\").item_id.to_numpy(dtype=np.int64)\\n+ us = np.sort(users)\\n+ it = np.sort(catalog)\\n+ chunks = []\\n+ pf = pq.ParquetFile(D / \\\"train_listens.parquet\\\")\\n+ for rg in range(pf.num_row_groups):\\n+ x = pf.read_row_group(rg, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]).to_pandas()\\n+ uv = np.isin(x.uid.to_numpy(), us)\\n+ iv = np.isin(x.item_id.to_numpy(), it)\\n+ keep = uv & iv & (x.played_ratio_pct.to_numpy() >= 50) & (x.timestamp.to_numpy() >= CUTOFF - 90 * 86400)\\n+ y = x.loc[keep, [\\\"uid\\\", \\\"item_id\\\", \\\"timestamp\\\", \\\"is_organic\\\"]]\\n+ y[\\\"organic_count\\\"] = y.is_organic.astype(np.int16)\\n+ y = y.groupby([\\\"uid\\\", \\\"item_id\\\"], as_index=False).agg(count=(\\\"timestamp\\\", \\\"size\\\"), last=(\\\"timestamp\\\", \\\"max\\\"), organic_count=(\\\"organic_count\\\", \\\"sum\\\"))\\n+ chunks.append(y)\\n+ hist = pd.concat(chunks, ignore_index=True).groupby([\\\"uid\\\", \\\"item_id\\\"], as_index=False).agg(count=(\\\"count\\\", \\\"sum\\\"), last=(\\\"last\\\", \\\"max\\\"), organic_count=(\\\"organic_count\\\", \\\"sum\\\"))\\n+ # Several simple rankings can be selected by changing this expression.\\n+ age = (CUTOFF - hist[\\\"last\\\"]) / 86400.0\\n+ hist[\\\"score\\\"] = np.log1p(hist[\\\"organic_count\\\"] * 2 + hist[\\\"count\\\"]) - 0.015 * age\\n+ by_user = {int(u): g.sort_values(\\\"score\\\", ascending=False).item_id.to_numpy(dtype=np.int64) for u, g in hist.groupby(\\\"uid\\\")}\\n+ z = np.load(D / \\\"validation_eligible_candidates.npz\\\")\\n+ z_uids = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64); packed = z[\\\"packed_eligible\\\"]\\n+ result = np.empty((len(z_uids), 100), dtype=np.int64)\\n+ for row, uid in enumerate(z_uids):\\n+ eligible = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ allowed = set(items[eligible].tolist())\\n+ personal = [i for i in by_user.get(int(uid), []) if i in allowed]\\n+ chosen = list(dict.fromkeys(personal + items[eligible].tolist()))[:100]\\n+ result[row] = chosen\\n+ pd.DataFrame({\\\"uid\\\": np.repeat(z_uids,100), \\\"item_id\\\":result.ravel(), \\\"rank\\\":np.tile(np.arange(1,101),len(z_uids))}).astype(\\\"int64\\\").to_parquet(O/\\\"validation_repeat.parquet\\\",index=False)\\n+ hist.to_parquet(O/\\\"repeat_history.parquet\\\",index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:51:26.837Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-08-31T18:51:28.776Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:51:28.776Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:51:31.615Z", "callId": "call_bavzpFjLvUbnbjcH4Yf8ly0O", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/repeat_diagnostic.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:51:42.633Z", "content": "Script running with cell ID 59\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-08-31T18:51:44.183Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:51:44.183Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:51:45.010Z", "callId": "call_4HkrPMwqU22sWDVFeErB1PQR", "name": "wait", "input": { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 5000 }, "result": { "timestamp": "2026-08-31T18:52:01.795Z", "content": "Script completed\nWall time 16.8 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-08-31T18:52:03.741Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:52:03.741Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:52:08.583Z", "callId": "call_9gVK0TejZ2FD1wXLsHyEwZjl", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'repeat_diagnostic|evaluate_public.*validation_repeat' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_repeat.parquet /app/solution/repeat_history.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:52:08.671Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/bin/sh -c /opt/task-venv/bin/python /app/solution/repeat_diagnostic.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet \n PID=1330\n/opt/task-venv/bin/python /app/solution/repeat_diagnostic.py \n PID=1331\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" | rg 'repeat_diagnostic|evaluate_public.*validation_repeat' && printf ' PID=%s\\n' \"${d##*/}\"; fi; done\n PID=1357\n", "isError": null } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-08-31T18:52:12.595Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:52:12.595Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:52:14.675Z", "callId": "call_nhTE54qNG0iyl0PAkSfkAZGt", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"while [ -e /proc/1330 ]; do sleep 1; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:52:17.707Z", "content": "Script completed\nWall time 3.0 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-08-31T18:52:20.251Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:52:20.251Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:52:23.085Z", "callId": "call_agANAYkPaNKGkM8gZVdmXFkC", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_repeat.parquet /app/solution/repeat_history.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:52:26.378Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 46M Aug 31 18:52 /app/solution/repeat_history.parquet\n-rw-r--r-- 1 agent agent 712K Aug 31 18:52 /app/solution/validation_repeat.parquet\n{\n \"model\": \"/app/solution/validation_repeat.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-08-31T18:52:46.823Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:52:46.823Z", "text": "" } ] }, { "id": 99, "role": "agent", "timestamp": "2026-08-31T18:52:57.968Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:52:57.968Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:53:01.428Z", "text": "The repeat-listen diagnostic collapses exactly to the popularity score, confirming that the eligibility service removes previously consumed tracks. That exposes the main modeling mismatch: my rolling training labels still included repeat listens even though they can never enter the shelf. I’m correcting the sampled objective to keep only cutoff-novel positives before retraining; the user history remains the signal, but it will now predict unseen items rather than reconstruct consumed ones." }, { "kind": "tool", "timestamp": "2026-08-31T18:53:14.414Z", "callId": "call_mC9JeQyClTKWqLmqV8M7FcUg", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n def labels_for_window(\\n events: EventData, actions: ActionData, cutoff: int\\n ) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n@@\\n for u, i in zip(actions.user[likes], actions.item[likes]):\\n pairs.setdefault(int(u), {})[int(i)] = 3\\n+ # The platform's candidate boundary removes consumed tracks. Reproduce\\n+ # that crucial aspect at every pseudo-cutoff: labels already present in\\n+ # the pre-cutoff positive history are not valid discovery targets. This\\n+ # is a sparse per-user set lookup over observed events, not a matrix.\\n+ if pairs:\\n+ n_users = int(events.user.max()) + 1\\n+ event_offsets = offsets_for_users(events.user, n_users)\\n+ novel: dict[int, dict[int, int]] = {}\\n+ for u, grades in pairs.items():\\n+ lo, hi = event_offsets[u], event_offsets[u + 1]\\n+ before = lo + np.searchsorted(\\n+ events.timestamp[lo:hi], cutoff, side=\\\"right\\\"\\n+ )\\n+ consumed = set(map(int, events.item[lo:before]))\\n+ kept = {item: grade for item, grade in grades.items() if item not in consumed}\\n+ if kept:\\n+ novel[u] = kept\\n+ pairs = novel\\n return {\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:53:14.463Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-08-31T18:53:16.555Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:53:16.555Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:53:19.450Z", "callId": "call_EpBuXO9DWoy0rxJ8fqzRw5Ss", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --output /app/solution/validation_novel.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:53:20.627Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"e38935\",\"wall_time_seconds\":1.000418798,\"session_id\":72485,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-08-31T18:53:22.185Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:53:22.185Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:53:25.454Z", "callId": "call_WxRammJh1fQKnnEHTTtVNfnp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:53:36.474Z", "content": "Script running with cell ID 65\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-08-31T18:53:38.329Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:53:38.329Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:53:39.108Z", "callId": "call_Gs8QSuGC1OSLvH5Q1B3nsFz0", "name": "wait", "input": { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:53:55.486Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 4225 labeled users, 45005 positives\nsnapshot cutoff=20540600: 4315 labeled users, 47973 positives\nsnapshot cutoff=21147200: 4338 labeled users, 45917 positives\nsnapshot cutoff=21753800: 4459 labeled users, 49300 positives\nsnapshot cutoff=22360400: 4540 labeled users, 48291 positives\nsnapshot cutoff=22967000: 4562 labeled users, 50197 positives\nsnapshot cutoff=23573600: 4694 labeled users, 50667 positives\nsnapshot cutoff=24180200: 4778 labeled users, 51636 positives\n/app/solution/build_ranker.py:496: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\n", "isError": null } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-08-31T18:53:58.235Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:53:58.235Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:53:58.613Z", "callId": "call_vM81kkLtLmy1a8txooVzLBRM", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:54:09.659Z", "content": "Script running with cell ID 66\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-08-31T18:54:11.283Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:54:11.283Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:54:12.087Z", "callId": "call_pOSkh27eT0NKkZGt3BV8mmAl", "name": "wait", "input": { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:54:28.666Z", "content": "Script completed\nWall time 16.6 seconds\nOutput:\n\nepoch 01/10: loss=0.62228\nepoch 02/10: loss=0.55714\nepoch 03/10: loss=0.51068\nepoch 04/10: loss=0.46700\nepoch 05/10: loss=0.42923\nepoch 06/10: loss=0.40015\n", "isError": null } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-08-31T18:54:30.664Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:54:30.664Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:54:32.594Z", "callId": "call_1jgUdQKRoLSBzP1shWLvjgUA", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:54:43.645Z", "content": "Script running with cell ID 67\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-08-31T18:54:46.778Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:54:46.778Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:54:47.522Z", "callId": "call_47udQLC0AvSthjBscpi3PUEo", "name": "wait", "input": { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:54:57.380Z", "content": "Script completed\nWall time 9.9 seconds\nOutput:\n\nepoch 07/10: loss=0.37907\nepoch 08/10: loss=0.36506\nepoch 09/10: loss=0.34721\nepoch 10/10: loss=0.33319\n{\"ranking\": \"/app/solution/validation_novel.parquet\", \"checkpoint\": \"/app/solution/model_validation.pt\", \"training_examples\": 35911, \"elapsed_seconds\": 93.8}\n", "isError": null } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-08-31T18:54:58.995Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:54:58.995Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:55:01.255Z", "callId": "call_eiT9qoFSypvDb8sasWiGUv47", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_novel.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:55:04.678Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_novel.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01277,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-08-31T18:55:09.411Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:55:09.411Z", "text": "" } ] }, { "id": 109, "role": "agent", "timestamp": "2026-08-31T18:55:23.012Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:55:23.012Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:55:26.669Z", "text": "Novel-only training reaches 0.012770—better than popularity but worse than the mixed next-item objective. The repeats are not rankable, yet they still teach strong item–item affinity, so deleting them discards useful representation learning. I’m retaining the original 0.017035 best and testing the more principled combination: broad next-item pretraining followed by a short novel-item fine-tune, rather than training either signal in isolation." }, { "kind": "tool", "timestamp": "2026-08-31T18:55:51.073Z", "callId": "call_Z0Iyk6qwMBk1UrfOXEl6oH5N", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n def labels_for_window(\\n- events: EventData, actions: ActionData, cutoff: int\\n+ events: EventData, actions: ActionData, cutoff: int, novel_only: bool = False\\n ) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n@@\\n- if pairs:\\n+ if novel_only and pairs:\\n@@\\n def build_training_data(\\n@@\\n supplied_train_preferences: tuple[np.ndarray, np.ndarray] | None,\\n+ novel_only: bool = False,\\n ) -> SnapshotDataset:\\n@@\\n- labels = labels_for_window(events, actions, cutoff)\\n+ labels = labels_for_window(events, actions, cutoff, novel_only=novel_only)\\n@@\\n def train_model(\\n@@\\n epochs: int,\\n seed: int,\\n+ learning_rate: float = 2.5e-3,\\n ) -> None:\\n@@\\n- optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n@@\\n parser.add_argument(\\\"--output\\\", type=Path)\\n+ parser.add_argument(\\n+ \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"pretrain\\\",\\n+ help=\\\"pretrain uses broad positives then a short novel-candidate fine-tune\\\",\\n+ )\\n@@\\n dataset = build_training_data(\\n builder, events, actions, cutoffs,\\n validation_preferences if include_val else None,\\n+ novel_only=args.label_mode == \\\"novel\\\",\\n )\\n@@\\n- train_model(model, dataset, popularity, args.epochs, args.seed)\\n+ if args.label_mode == \\\"pretrain\\\":\\n+ broad = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ novel_only=False,\\n+ )\\n+ broad_epochs = max(1, args.epochs - 2)\\n+ train_model(model, broad, popularity, broad_epochs, args.seed)\\n+ # Two gentle epochs align the pretrained affinity space to the actual\\n+ # unseen-candidate boundary without forgetting co-consumption structure.\\n+ novel = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ novel_only=True,\\n+ )\\n+ train_model(model, novel, popularity, args.epochs - broad_epochs,\\n+ args.seed + 1, learning_rate=6e-4)\\n+ dataset = novel\\n+ else:\\n+ train_model(model, dataset, popularity, args.epochs, args.seed)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:55:51.142Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-08-31T18:55:56.275Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:55:56.275Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:00.132Z", "callId": "call_kdHRMBiHUL4wgcGqnny1v5i3", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n if args.label_mode == \\\"pretrain\\\":\\n- broad = build_training_data(\\n- builder, events, actions, cutoffs,\\n- validation_preferences if include_val else None,\\n- novel_only=False,\\n- )\\n+ broad = dataset\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:56:00.205Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 111, "role": "agent", "timestamp": "2026-08-31T18:56:02.187Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:56:02.187Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:05.972Z", "callId": "call_corKca5aLcvDgIjeRVwpoK6j", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode pretrain --output /app/solution/validation_pretrain.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:56:07.150Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d82d57\",\"wall_time_seconds\":1.001007921,\"session_id\":19121,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-08-31T18:56:08.888Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:56:08.888Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:10.233Z", "callId": "call_oxijfu7GbUplaxG7cOBjiOWp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:56:21.250Z", "content": "Script running with cell ID 72\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-08-31T18:56:22.712Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:56:22.712Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:23.505Z", "callId": "call_KFl2FsjJjPMDTJ2cFKwdgk8m", "name": "wait", "input": { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:56:40.256Z", "content": "Script completed\nWall time 16.8 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 5318 labeled users, 164063 positives\nsnapshot cutoff=20540600: 5392 labeled users, 165953 positives\nsnapshot cutoff=21147200: 5411 labeled users, 158923 positives\nsnapshot cutoff=21753800: 5563 labeled users, 165819 positives\nsnapshot cutoff=22360400: 5650 labeled users, 168735 positives\nsnapshot cutoff=22967000: 5754 labeled users, 170431 positives\nsnapshot cutoff=23573600: 5830 labeled users, 171275 positives\nsnapshot cutoff=24180200: 5989 labeled users, 176193 positives\n/app/solution/build_ranker.py:498: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\nepoch 01/8: loss=0.59321\n", "isError": null } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-08-31T18:56:42.225Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:56:42.225Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:43.557Z", "callId": "call_26gxVDwV0OCuAjIpP5iFpPhd", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:56:54.582Z", "content": "Script running with cell ID 73\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-08-31T18:56:56.247Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:56:56.247Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:56:57.152Z", "callId": "call_SNWNjkPsDWjL3T2IJ3yDwqEB", "name": "wait", "input": { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:57:13.592Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\nepoch 02/8: loss=0.54179\nepoch 03/8: loss=0.51136\nepoch 04/8: loss=0.48345\nepoch 05/8: loss=0.46498\n", "isError": null } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-08-31T18:57:15.355Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:57:15.355Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:57:16.713Z", "callId": "call_QuMipgxBw7jPRUeOOGjTdf9l", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:57:27.756Z", "content": "Script running with cell ID 74\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-08-31T18:57:30.149Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:57:30.149Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:57:31.010Z", "callId": "call_px0RQT3ZT7Ppu4P2iaFJH0JJ", "name": "wait", "input": { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:57:46.757Z", "content": "Script completed\nWall time 15.7 seconds\nOutput:\n\nepoch 06/8: loss=0.44869\nepoch 07/8: loss=0.43527\nepoch 08/8: loss=0.41981\nsnapshot cutoff=19934000: 4225 labeled users, 45005 positives\nsnapshot cutoff=20540600: 4315 labeled users, 47973 positives\nsnapshot cutoff=21147200: 4338 labeled users, 45917 positives\nsnapshot cutoff=21753800: 4459 labeled users, 49300 positives\nsnapshot cutoff=22360400: 4540 labeled users, 48291 positives\nsnapshot cutoff=22967000: 4562 labeled users, 50197 positives\n", "isError": null } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-08-31T18:57:48.446Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:57:48.446Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:57:49.813Z", "callId": "call_Mjnw80pbpTYNUpt8BkBr2FCU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:58:00.835Z", "content": "Script running with cell ID 75\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 119, "role": "agent", "timestamp": "2026-08-31T18:58:02.508Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:58:02.508Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:58:03.255Z", "callId": "call_SReqkWWSDdMsNv8DXCK49cfE", "name": "wait", "input": { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:58:07.340Z", "content": "Script completed\nWall time 4.1 seconds\nOutput:\n\nsnapshot cutoff=23573600: 4694 labeled users, 50667 positives\nsnapshot cutoff=24180200: 4778 labeled users, 51636 positives\nepoch 01/2: loss=0.42226\nepoch 02/2: loss=0.41066\n{\"ranking\": \"/app/solution/validation_pretrain.parquet\", \"checkpoint\": \"/app/solution/model_validation.pt\", \"training_examples\": 35911, \"elapsed_seconds\": 117.0}\n", "isError": null } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-08-31T18:58:08.764Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:58:08.764Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:58:10.981Z", "callId": "call_m4gT6twEuIdIRnecdFYhrsrP", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pretrain.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:58:14.330Z", "content": "Script completed\nWall time 3.3 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_pretrain.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015141,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-08-31T18:58:20.159Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:58:20.159Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:58:28.239Z", "callId": "call_51sY6lcl0FDpiS2hXLaNfRDo", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n p.add_argument(\\\"--constant\\\", type=float, default=30.0)\\n+ p.add_argument(\\\"--prior-ranking\\\", type=Path, help=\\\"optional second ranking instead of popularity\\\")\\n@@\\n model_matrix = ordered.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n+ prior_matrix = None\\n+ if args.prior_ranking:\\n+ prior = pd.read_parquet(args.prior_ranking).sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ prior_matrix = prior.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n@@\\n- pop = items[eligible][:400]\\n+ pop = prior_matrix[row] if prior_matrix is not None else items[eligible][:400]\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:58:28.345Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 122, "role": "agent", "timestamp": "2026-08-31T18:58:32.148Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:58:32.148Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:58:38.501Z", "callId": "call_FlaeCHhUyChmvORLs2LmIlCE", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for w in 0.5 0.65 0.8 0.9; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --prior-ranking /app/solution/validation_novel.parquet --output /app/solution/validation_ensemble_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.5 0.65 0.8 0.9; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T18:58:49.522Z", "content": "Script running with cell ID 78\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-08-31T18:58:51.182Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:58:51.182Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:58:52.158Z", "callId": "call_Fk8O9fyHV4l7mWR5ROQ8EB43", "name": "wait", "input": { "cell_id": "78", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T18:59:04.008Z", "content": "Script completed\nWall time 11.8 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_ensemble_0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019391,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_ensemble_0.65.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019662,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_ensemble_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018767,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_ensemble_0.9.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017606,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-08-31T18:59:23.699Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:59:23.699Z", "text": "" } ] }, { "id": 125, "role": "agent", "timestamp": "2026-08-31T18:59:29.892Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:59:29.892Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T18:59:33.625Z", "text": "The two learned signals are strongly complementary: validation-only reciprocal-rank diagnostics reach 0.019662 when broad affinity gets 65% weight and novel-item training 35%. I won’t use reciprocal ranks in the final system because that would violate the required pure-dot-product scorer. Instead, I’m preserving separate checkpoints and combining them by concatenating scaled tower vectors; the resulting weighted score is exactly one dot product and candidate vectors remain independently precomputable." }, { "kind": "tool", "timestamp": "2026-08-31T18:59:38.450Z", "callId": "call_UosnV4dCiX4kvDTHOFkbhOs5", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"pretrain\\\",\\n+ \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"all\\\",\\n@@\\n- checkpoint = OUT / f\\\"model_{args.stage}.pt\\\"\\n+ checkpoint = OUT / f\\\"model_{args.stage}_{destination.stem}.pt\\\"\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T18:59:38.590Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 126, "role": "agent", "timestamp": "2026-08-31T18:59:44.703Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:59:44.703Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:59:48.149Z", "callId": "call_i8xXq1Q5sKZ5WaiD4sNKSeRm", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode all --output /app/solution/validation_all_checkpoint.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T18:59:49.326Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"31ab1d\",\"wall_time_seconds\":1.000960007,\"session_id\":47955,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-08-31T18:59:52.292Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T18:59:52.292Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T18:59:53.135Z", "callId": "call_gKmMSC5PWprPa5ag3kK8utbq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:00:04.153Z", "content": "Script running with cell ID 81\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-08-31T19:00:07.154Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:00:07.154Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:00:08.147Z", "callId": "call_JVm5HN6gi5lkEQPa0saDHuwS", "name": "wait", "input": { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:00:23.171Z", "content": "Script completed\nWall time 15.0 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 5318 labeled users, 164063 positives\nsnapshot cutoff=20540600: 5392 labeled users, 165953 positives\nsnapshot cutoff=21147200: 5411 labeled users, 158923 positives\nsnapshot cutoff=21753800: 5563 labeled users, 165819 positives\nsnapshot cutoff=22360400: 5650 labeled users, 168735 positives\nsnapshot cutoff=22967000: 5754 labeled users, 170431 positives\nsnapshot cutoff=23573600: 5830 labeled users, 171275 positives\nsnapshot cutoff=24180200: 5989 labeled users, 176193 positives\n/app/solution/build_ranker.py:498: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\nepoch 01/10: loss=0.59321\n", "isError": null } } ] }, { "id": 129, "role": "agent", "timestamp": "2026-08-31T19:00:24.869Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:00:24.869Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:00:26.401Z", "callId": "call_Quni3LjUhSmEEWr2t9vaag1F", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:00:37.442Z", "content": "Script running with cell ID 82\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 130, "role": "agent", "timestamp": "2026-08-31T19:00:39.166Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:00:39.166Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:00:40.021Z", "callId": "call_tdwzCyZQhdiGfqanF0p9ClG3", "name": "wait", "input": { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:00:56.444Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\nepoch 02/10: loss=0.54179\nepoch 03/10: loss=0.51136\nepoch 04/10: loss=0.48345\nepoch 05/10: loss=0.46498\n", "isError": null } } ] }, { "id": 131, "role": "agent", "timestamp": "2026-08-31T19:00:58.064Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:00:58.064Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:00:59.427Z", "callId": "call_HnwterhPtlR8ixP0JAy5vBPH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:01:10.463Z", "content": "Script running with cell ID 83\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-08-31T19:01:12.565Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:01:12.565Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:01:13.390Z", "callId": "call_LnxxMopwShdiJ3BEhpHU38S3", "name": "wait", "input": { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:01:29.472Z", "content": "Script completed\nWall time 16.1 seconds\nOutput:\n\nepoch 06/10: loss=0.44869\nepoch 07/10: loss=0.43527\nepoch 08/10: loss=0.41981\nepoch 09/10: loss=0.40792\n", "isError": null } } ] }, { "id": 133, "role": "agent", "timestamp": "2026-08-31T19:01:30.940Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:01:30.940Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:01:32.341Z", "callId": "call_1kPlRNLQYfYgSTtixMKta85D", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:01:35.518Z", "content": "Script completed\nWall time 3.2 seconds\nOutput:\n\nepoch 10/10: loss=0.39613\n{\"ranking\": \"/app/solution/validation_all_checkpoint.parquet\", \"checkpoint\": \"/app/solution/model_validation_validation_all_checkpoint.pt\", \"training_examples\": 44907, \"elapsed_seconds\": 103.3}\n", "isError": null } } ] }, { "id": 134, "role": "agent", "timestamp": "2026-08-31T19:01:38.046Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:01:38.046Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:01:41.049Z", "callId": "call_K4ODv25PpGl2dmbzZ0QuxyCu", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode novel --output /app/solution/validation_novel_checkpoint.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T19:01:42.227Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"909f17\",\"wall_time_seconds\":1.000198739,\"session_id\":14276,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 135, "role": "agent", "timestamp": "2026-08-31T19:01:44.374Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:01:44.374Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:01:46.908Z", "callId": "call_EIQ3gZ17qW5z0NbuY7Birbfw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:01:57.931Z", "content": "Script running with cell ID 86\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 136, "role": "agent", "timestamp": "2026-08-31T19:01:59.643Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:01:59.643Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:02:00.406Z", "callId": "call_XeiZYPJLpmUzIKYyqqzRQBVS", "name": "wait", "input": { "cell_id": "86", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:02:16.930Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 13,748,805 positive listens and 418,226 actions\nsnapshot cutoff=19934000: 4225 labeled users, 45005 positives\nsnapshot cutoff=20540600: 4315 labeled users, 47973 positives\nsnapshot cutoff=21147200: 4338 labeled users, 45917 positives\nsnapshot cutoff=21753800: 4459 labeled users, 49300 positives\nsnapshot cutoff=22360400: 4540 labeled users, 48291 positives\nsnapshot cutoff=22967000: 4562 labeled users, 50197 positives\nsnapshot cutoff=23573600: 4694 labeled users, 50667 positives\nsnapshot cutoff=24180200: 4778 labeled users, 51636 positives\n/app/solution/build_ranker.py:498: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\n", "isError": null } } ] }, { "id": 137, "role": "agent", "timestamp": "2026-08-31T19:02:18.622Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:02:18.622Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:02:19.990Z", "callId": "call_xJiGbgS89Agv7ilI1HtZr9hI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:02:31.019Z", "content": "Script running with cell ID 87\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 138, "role": "agent", "timestamp": "2026-08-31T19:02:32.942Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:02:32.942Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:02:33.752Z", "callId": "call_CTOsNVVCqUMtIByV82CaE9wU", "name": "wait", "input": { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:02:50.030Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\nepoch 01/10: loss=0.62228\nepoch 02/10: loss=0.55714\nepoch 03/10: loss=0.51068\nepoch 04/10: loss=0.46700\nepoch 05/10: loss=0.42923\n", "isError": null } } ] }, { "id": 139, "role": "agent", "timestamp": "2026-08-31T19:02:51.906Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:02:51.906Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:02:53.407Z", "callId": "call_zT7V1i1jGCCIOpcHYRYSxHeL", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:03:04.449Z", "content": "Script running with cell ID 88\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 140, "role": "agent", "timestamp": "2026-08-31T19:03:14.571Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:03:14.571Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:03:15.359Z", "callId": "call_czCxvC8BXZ6QInUKdwDM7uv9", "name": "wait", "input": { "cell_id": "88", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:03:19.772Z", "content": "Script completed\nWall time 4.4 seconds\nOutput:\n\nepoch 06/10: loss=0.40015\nepoch 07/10: loss=0.37907\nepoch 08/10: loss=0.36506\nepoch 09/10: loss=0.34721\nepoch 10/10: loss=0.33319\n{\"ranking\": \"/app/solution/validation_novel_checkpoint.parquet\", \"checkpoint\": \"/app/solution/model_validation_validation_novel_checkpoint.pt\", \"training_examples\": 35911, \"elapsed_seconds\": 94.6}\n", "isError": null } } ] }, { "id": 141, "role": "agent", "timestamp": "2026-08-31T19:03:29.338Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:03:29.338Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:04:25.231Z", "callId": "call_TduuuGKkdD5OClm80So7X9Zx", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/score_ensemble.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Apply two trained tower pairs as one exact concatenated-dot-product model.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import math\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+import torch.nn as nn\\n+\\n+from build_ranker import (\\n+ DATA, GAP, HISTORY_DAYS, MAX_DISLIKES, MAX_LIKES, STEP, TRAIN_END,\\n+ ActionData, FeatureBuilder, IdMap, TwoTower, item_metadata, load_actions,\\n+ load_listens, provided_preference_features,\\n+)\\n+\\n+\\n+class ConcatenatedUserTower(nn.Module):\\n+ \\\"\\\"\\\"One user tower whose output concatenates scaled component vectors.\\\"\\\"\\\"\\n+\\n+ def __init__(self, first: nn.Module, second: nn.Module, weight: float):\\n+ super().__init__()\\n+ self.first, self.second = first, second\\n+ self.a, self.b = math.sqrt(weight), math.sqrt(1.0 - weight)\\n+\\n+ def forward(self, *features):\\n+ return torch.cat([self.a * self.first(*features), self.b * self.second(*features)], dim=-1)\\n+\\n+\\n+class ConcatenatedItemTower(nn.Module):\\n+ \\\"\\\"\\\"Offline item counterpart; its dot product equals the weighted score.\\\"\\\"\\\"\\n+\\n+ def __init__(self, first: nn.Module, second: nn.Module, weight: float):\\n+ super().__init__()\\n+ self.first, self.second = first, second\\n+ self.a, self.b = math.sqrt(weight), math.sqrt(1.0 - weight)\\n+\\n+ def forward(self, item_indices):\\n+ return torch.cat([self.a * self.first(item_indices), self.b * self.second(item_indices)], dim=-1)\\n+\\n+\\n+@torch.inference_mode()\\n+def write(user_tower, item_tower, features, archive_path, output):\\n+ user_tower.eval(); item_tower.eval()\\n+ z = np.load(archive_path)\\n+ uids = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n+ iv = item_tower(torch.arange(len(items))).cpu()\\n+ chosen = np.empty((len(uids), 100), dtype=np.int32)\\n+ for lo in range(0, len(uids), 128):\\n+ hi = min(lo + 128, len(uids))\\n+ uv = user_tower(*[torch.from_numpy(x[lo:hi]) for x in features]).cpu()\\n+ scores = uv @ iv.T\\n+ eligible = np.unpackbits(packed[lo:hi], axis=1, count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ scores[~torch.from_numpy(eligible)] = -torch.inf\\n+ chosen[lo:hi] = torch.topk(scores, 100, dim=1).indices.numpy()\\n+ frame = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(uids, 100),\\n+ \\\"item_id\\\": items[chosen.ravel()],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(uids)),\\n+ }).astype(\\\"int64\\\")\\n+ temp = output.with_suffix(\\\".tmp.parquet\\\")\\n+ frame.to_parquet(temp, index=False); temp.replace(output)\\n+\\n+\\n+def main():\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ p.add_argument(\\\"--all-model\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--novel-model\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--weight\\\", type=float, required=True)\\n+ p.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = p.parse_args()\\n+ torch.set_num_threads(12)\\n+ stage = args.stage\\n+ archive_path = DATA / f\\\"{stage}_eligible_candidates.npz\\\"\\n+ z = np.load(archive_path)\\n+ users = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ user_map, item_map = IdMap(users), IdMap(items)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ meta = item_metadata(catalog)\\n+ first, second = TwoTower(len(items), meta), TwoTower(len(items), meta)\\n+ first.load_state_dict(torch.load(args.all_model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+ second.load_state_dict(torch.load(args.novel_model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+\\n+ include_val = stage == \\\"test\\\"\\n+ cutoff = TRAIN_END + STEP if include_val else TRAIN_END\\n+ earliest = cutoff - HISTORY_DAYS * 86_400\\n+ listens = [\\\"train_listens.parquet\\\"] + ([\\\"val_listens.parquet\\\"] if include_val else [])\\n+ events = load_listens(listens, user_map, item_map, earliest)\\n+ actions = load_actions(include_val, user_map, item_map, earliest)\\n+ builder = FeatureBuilder(events, actions, len(users))\\n+ features = list(builder.many(np.arange(len(users), dtype=np.int32), cutoff))\\n+ pref_name = \\\"test_preference_state.parquet\\\" if include_val else \\\"validation_preference_state.parquet\\\"\\n+ prefs = provided_preference_features(DATA / pref_name, user_map, item_map)\\n+ features[2], features[3] = prefs\\n+ features[4][:, 1] = np.log1p((prefs[0] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((prefs[1] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ user_tower = ConcatenatedUserTower(first.user_tower, second.user_tower, args.weight)\\n+ item_tower = ConcatenatedItemTower(first.item_tower, second.item_tower, args.weight)\\n+ write(user_tower, item_tower, tuple(features), archive_path, args.output)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T19:04:25.282Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 142, "role": "agent", "timestamp": "2026-08-31T19:04:29.809Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:04:29.809Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:04:36.320Z", "callId": "call_sXFaIQpnowIdoltpsD6M3TNK", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for w in 0.4 0.55 0.65 0.75; do /opt/task-venv/bin/python /app/solution/score_ensemble.py --stage validation --all-model /app/solution/model_validation_validation_all_checkpoint.pt --novel-model /app/solution/model_validation_validation_novel_checkpoint.pt --weight $w --output /app/solution/validation_dot_${w}.parquet; done\\nfor w in 0.4 0.55 0.65 0.75; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_dot_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T19:04:37.497Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"2e974d\",\"wall_time_seconds\":1.001195381,\"session_id\":77163,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 143, "role": "agent", "timestamp": "2026-08-31T19:04:39.340Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:04:39.340Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:04:40.678Z", "callId": "call_46tX15PzJ6bt39V2q1qXqkgS", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:04:51.700Z", "content": "Script running with cell ID 91\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 144, "role": "agent", "timestamp": "2026-08-31T19:04:53.904Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:04:53.904Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:04:54.715Z", "callId": "call_43fsVpH8u0K5rsB0VOsnjs0d", "name": "wait", "input": { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:05:10.704Z", "content": "Script completed\nWall time 16.0 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 145, "role": "agent", "timestamp": "2026-08-31T19:05:12.426Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:05:12.426Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:05:13.805Z", "callId": "call_DTXgdnpgBLaH1EU9HFqA69HS", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:05:24.823Z", "content": "Script running with cell ID 92\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 146, "role": "agent", "timestamp": "2026-08-31T19:05:27.011Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:05:27.011Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:05:27.737Z", "callId": "call_lqdbVm4LnqFijc26leH33NUF", "name": "wait", "input": { "cell_id": "92", "yield_time_ms": 30000, "max_tokens": 12000 }, "result": { "timestamp": "2026-08-31T19:05:43.827Z", "content": "Script completed\nWall time 16.1 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_dot_0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018559,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_dot_0.55.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020363,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 147, "role": "agent", "timestamp": "2026-08-31T19:05:49.623Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:05:49.623Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:05:49.627Z", "callId": "call_POrngGKLMX4JAwYpifOFSeTw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:05:49.679Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_dot_0.65.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020298,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_dot_0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020288,\n \"quality_users\": 4263\n }\n}\n", "isError": null } } ] }, { "id": 148, "role": "agent", "timestamp": "2026-08-31T19:05:59.635Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:05:59.635Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T19:06:03.129Z", "text": "The exact concatenated-dot-product ensemble validates at 0.020363 NDCG@10 with a 55% broad-affinity / 45% novel-item weighting, outperforming both individual towers and the rank-fusion diagnostic. I’m locking that choice, creating a structurally valid fallback submission immediately, then refitting both components on `train + val` and replacing the fallback only after the final eligibility checks pass." }, { "kind": "tool", "timestamp": "2026-08-31T19:06:11.718Z", "callId": "call_2q5OiA56Bd0OtZlyygAjTnEf", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom baseline import write\\nimport shutil\\np=write('test')\\nshutil.copyfile(p,'/app/solution/submission.parquet')\\nprint(p)\\nPY\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); x=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); x=validate_ranking(x,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)); print('fallback valid',x.shape)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:06:17.371Z", "content": "Script completed\nWall time 5.6 seconds\nOutput:\n\n/app/solution/test_popularity.parquet\nfallback valid (845600, 3)\n", "isError": null } } ] }, { "id": 149, "role": "agent", "timestamp": "2026-08-31T19:06:19.405Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:06:19.405Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:06:22.369Z", "callId": "call_Rr9hrwMigmejXJs1rDi6PoSG", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --epochs 10 --seed 20260831 --label-mode all --output /app/solution/test_all_component.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":14000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T19:06:23.545Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d39135\",\"wall_time_seconds\":1.000772135,\"session_id\":67089,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 150, "role": "agent", "timestamp": "2026-08-31T19:06:25.294Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:06:25.294Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:06:26.668Z", "callId": "call_ewIRJxqYMCHbkIYMN6PPd2Ke", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:06:37.689Z", "content": "Script running with cell ID 96\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 151, "role": "agent", "timestamp": "2026-08-31T19:06:39.413Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:06:39.413Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:06:40.215Z", "callId": "call_Gc8cUMW5iEAYfGeS03PaXDDr", "name": "wait", "input": { "cell_id": "96", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:06:56.703Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 14,399,100 positive listens and 440,272 actions\nsnapshot cutoff=19934000: 5318 labeled users, 164063 positives\nsnapshot cutoff=20540600: 5392 labeled users, 165953 positives\nsnapshot cutoff=21147200: 5411 labeled users, 158923 positives\nsnapshot cutoff=21753800: 5563 labeled users, 165819 positives\nsnapshot cutoff=22360400: 5650 labeled users, 168735 positives\nsnapshot cutoff=22967000: 5754 labeled users, 170431 positives\nsnapshot cutoff=23573600: 5830 labeled users, 171275 positives\nsnapshot cutoff=24180200: 5989 labeled users, 176193 positives\nsnapshot cutoff=24786800: 6186 labeled users, 175729 positives\n/app/solution/build_ranker.py:498: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\n", "isError": null } } ] }, { "id": 152, "role": "agent", "timestamp": "2026-08-31T19:06:58.376Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:06:58.376Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:06:59.723Z", "callId": "call_Br7iWK0wYdT9JGQoDWb9kDaU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:07:10.753Z", "content": "Script running with cell ID 97\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 153, "role": "agent", "timestamp": "2026-08-31T19:07:13.696Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:07:13.696Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:07:14.652Z", "callId": "call_xPVWeG1kRskfQrnXWWhfdfIK", "name": "wait", "input": { "cell_id": "97", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:07:29.764Z", "content": "Script completed\nWall time 15.1 seconds\nOutput:\n\nepoch 01/10: loss=0.59121\nepoch 02/10: loss=0.54127\nepoch 03/10: loss=0.50785\nepoch 04/10: loss=0.48479\n", "isError": null } } ] }, { "id": 154, "role": "agent", "timestamp": "2026-08-31T19:07:36.910Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:07:36.910Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:07:36.923Z", "callId": "call_3691iPPRQS5RXgN1ZlwJ3qYX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:07:47.965Z", "content": "Script running with cell ID 98\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 155, "role": "agent", "timestamp": "2026-08-31T19:07:49.858Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:07:49.858Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:07:50.663Z", "callId": "call_hpmK8ZzrVNOLbdJjwFU2e0Ld", "name": "wait", "input": { "cell_id": "98", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:08:06.973Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\nepoch 05/10: loss=0.46258\nepoch 06/10: loss=0.44288\nepoch 07/10: loss=0.43119\nepoch 08/10: loss=0.41586\n", "isError": null } } ] }, { "id": 156, "role": "agent", "timestamp": "2026-08-31T19:08:08.656Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:08:08.656Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:08:10.059Z", "callId": "call_DcScohJRUh3rlGmMIS1is1qe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:08:21.097Z", "content": "Script running with cell ID 99\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 157, "role": "agent", "timestamp": "2026-08-31T19:08:22.815Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:08:22.815Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:08:23.609Z", "callId": "call_f8plG0jTuLWGBtvlVtAirBtu", "name": "wait", "input": { "cell_id": "99", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:08:23.728Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nepoch 09/10: loss=0.40578\nepoch 10/10: loss=0.39861\n{\"ranking\": \"/app/solution/test_all_component.parquet\", \"checkpoint\": \"/app/solution/model_test_test_all_component.pt\", \"training_examples\": 51093, \"elapsed_seconds\": 114.9}\n", "isError": null } } ] }, { "id": 158, "role": "agent", "timestamp": "2026-08-31T19:08:27.318Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:08:27.318Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:08:30.358Z", "callId": "call_F4Zdoq1p9jNUC5vFao03nr9j", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --epochs 10 --seed 20260831 --label-mode novel --output /app/solution/test_novel_component.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":14000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T19:08:31.535Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"76b75e\",\"wall_time_seconds\":1.000543118,\"session_id\":8264,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 159, "role": "agent", "timestamp": "2026-08-31T19:08:34.462Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:08:34.462Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:08:35.846Z", "callId": "call_jDknj04ZbyGvDkFLZlvuwgHV", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:08:46.864Z", "content": "Script running with cell ID 101\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 160, "role": "agent", "timestamp": "2026-08-31T19:08:48.703Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:08:48.703Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:08:49.526Z", "callId": "call_Lwgx2Gopcuav3w5Syri8VLeB", "name": "wait", "input": { "cell_id": "101", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:09:05.870Z", "content": "Script completed\nWall time 16.3 seconds\nOutput:\n\nloading event streams since 9566000\nloaded 14,399,100 positive listens and 440,272 actions\nsnapshot cutoff=19934000: 4225 labeled users, 45005 positives\nsnapshot cutoff=20540600: 4315 labeled users, 47973 positives\nsnapshot cutoff=21147200: 4338 labeled users, 45917 positives\nsnapshot cutoff=21753800: 4459 labeled users, 49300 positives\nsnapshot cutoff=22360400: 4540 labeled users, 48291 positives\nsnapshot cutoff=22967000: 4562 labeled users, 50197 positives\nsnapshot cutoff=23573600: 4694 labeled users, 50667 positives\nsnapshot cutoff=24180200: 4778 labeled users, 51636 positives\nsnapshot cutoff=24786800: 4799 labeled users, 48252 positives\n/app/solution/build_ranker.py:498: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b\n", "isError": null } } ] }, { "id": 161, "role": "agent", "timestamp": "2026-08-31T19:09:08.151Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:09:08.151Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:09:09.551Z", "callId": "call_uoejAVijgY0jMtmpKBKmoDoP", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:09:20.581Z", "content": "Script running with cell ID 102\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 162, "role": "agent", "timestamp": "2026-08-31T19:09:22.365Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:09:22.365Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:09:23.215Z", "callId": "call_j6mwTFPAfOgT6JWwrlGi0v0d", "name": "wait", "input": { "cell_id": "102", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:09:39.590Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\nepoch 01/10: loss=0.61669\nepoch 02/10: loss=0.55944\nepoch 03/10: loss=0.51130\nepoch 04/10: loss=0.46934\n", "isError": null } } ] }, { "id": 163, "role": "agent", "timestamp": "2026-08-31T19:09:41.635Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:09:41.635Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:09:42.984Z", "callId": "call_kI5MaQDnYCHEJlQFDdOh8oib", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:09:54.009Z", "content": "Script running with cell ID 103\nWall time 11.0 seconds\nOutput:\n", "isError": null } } ] }, { "id": 164, "role": "agent", "timestamp": "2026-08-31T19:09:56.597Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:09:56.597Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:09:57.424Z", "callId": "call_rKZQbRtFTwP7lkoDolsuB29q", "name": "wait", "input": { "cell_id": "103", "yield_time_ms": 30000, "max_tokens": 14000 }, "result": { "timestamp": "2026-08-31T19:10:13.017Z", "content": "Script completed\nWall time 15.6 seconds\nOutput:\n\nepoch 05/10: loss=0.43242\nepoch 06/10: loss=0.40577\nepoch 07/10: loss=0.38599\nepoch 08/10: loss=0.36507\nepoch 09/10: loss=0.35812\n", "isError": null } } ] }, { "id": 165, "role": "agent", "timestamp": "2026-08-31T19:10:14.522Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:10:14.522Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:10:15.898Z", "callId": "call_dEtrAApkIAqiTnG2BAwKawX9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:10:19.761Z", "content": "Script completed\nWall time 3.9 seconds\nOutput:\n\nepoch 10/10: loss=0.35291\n{\"ranking\": \"/app/solution/test_novel_component.parquet\", \"checkpoint\": \"/app/solution/model_test_test_novel_component.pt\", \"training_examples\": 40710, \"elapsed_seconds\": 105.3}\n", "isError": null } } ] }, { "id": 166, "role": "agent", "timestamp": "2026-08-31T19:10:22.899Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:10:22.899Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:10:26.632Z", "callId": "call_P9fO0uQONXS77lX5PhCw757v", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/score_ensemble.py --stage test --all-model /app/solution/model_test_test_all_component.pt --novel-model /app/solution/model_test_test_novel_component.pt --weight 0.55 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-08-31T19:10:27.808Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"e3416a\",\"wall_time_seconds\":1.001108228,\"session_id\":73874,\"original_token_count\":0,\"output\":\"\"}", "isError": null } } ] }, { "id": 167, "role": "agent", "timestamp": "2026-08-31T19:10:30.375Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:10:30.375Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:10:31.776Z", "callId": "call_8836PmdjcsD6tMewCzAqQiae", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73874,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:10:41.547Z", "content": "Script completed\nWall time 9.8 seconds\nOutput:\n\n", "isError": null } } ] }, { "id": 168, "role": "agent", "timestamp": "2026-08-31T19:10:52.129Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:10:52.129Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T19:10:55.171Z", "text": "The final refit completed on nine rolling snapshots, including the public validation window as observed training history/outcomes. The 128-dimensional concatenated tower has now replaced the fallback submission. I’m performing the independent schema, rank, duplicate, user-set, and test-eligibility audit, then I’ll record the exact reproducible command and validation score in the README." }, { "kind": "tool", "timestamp": "2026-08-31T19:11:22.150Z", "callId": "call_94zVbTF3HfDP8ozINB4bWYbw", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/run_final.sh\\n+#!/bin/sh\\n+set -eu\\n+\\n+PYTHON=/opt/task-venv/bin/python\\n+SOLUTION=/app/solution\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/build_ranker.py\\\" \\\\\\n+ --stage test --epochs 10 --seed 20260831 --label-mode all \\\\\\n+ --output \\\"$SOLUTION/test_all_component.parquet\\\"\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/build_ranker.py\\\" \\\\\\n+ --stage test --epochs 10 --seed 20260831 --label-mode novel \\\\\\n+ --output \\\"$SOLUTION/test_novel_component.parquet\\\"\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/score_ensemble.py\\\" \\\\\\n+ --stage test \\\\\\n+ --all-model \\\"$SOLUTION/model_test_test_all_component.pt\\\" \\\\\\n+ --novel-model \\\"$SOLUTION/model_test_test_novel_component.pt\\\" \\\\\\n+ --weight 0.55 \\\\\\n+ --output \\\"$SOLUTION/submission.parquet\\\"\\n*** Add File: /app/solution/README.md\\n+# History-based CPU two-tower ranker\\n+\\n+## Model\\n+\\n+The final scorer is one exact 128-dimensional inner product. It concatenates\\n+two independently trained 64-dimensional tower pairs with square-root scaling,\\n+so its score is `0.55 * broad_score + 0.45 * novel_score`. Candidate vectors\\n+are produced offline by the item tower and the request path computes one user\\n+vector before MIPS retrieval.\\n+\\n+The user tower has **no user-ID embedding**. It pools bounded sequences of the\\n+user's recent positive listens (completion, organic source, and recency\\n+weighted), active likes, and active dislikes, then applies a LayerNorm/GELU\\n+MLP. The independent item tower applies an MLP to a trainable catalog-item\\n+embedding plus standardized popularity/organic metadata. Each 64D component\\n+is normalized; `score_ensemble.py` defines the concatenated user and item\\n+towers explicitly.\\n+\\n+## Training\\n+\\n+Training uses eight leakage-free rolling seven-day windows from `train_*`.\\n+The hidden-test refit adds the public validation window as a ninth supervised\\n+snapshot and uses `train_* + val_*` as request-time history. The broad model\\n+learns co-consumption from all organic 80%-complete listens and organic likes;\\n+the novel model removes items consumed before each pseudo-cutoff to match the\\n+eligibility boundary. Both optimize grade-weighted sampled BPR with a mixture\\n+of popularity-aware and uniform negatives. Training is event-stream based and\\n+never constructs or decomposes a user-item matrix.\\n+\\n+Public validation NDCG@10: **0.020363** (4,263 scored users). The selected\\n+weight and 10-epoch early stop were chosen only on the public window.\\n+\\n+## Reproduce the final submission\\n+\\n+```sh\\n+sh /app/solution/run_final.sh\\n+```\\n+\\n+The result is `/app/solution/submission.parquet` with columns `uid`, `item_id`,\\n+and `rank`.\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" }, "result": { "timestamp": "2026-08-31T19:11:22.266Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } } ] }, { "id": 169, "role": "agent", "timestamp": "2026-08-31T19:11:26.800Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:11:26.800Z", "text": "" }, { "kind": "tool", "timestamp": "2026-08-31T19:11:39.857Z", "callId": "call_qxfQClco2W3TawNCMFo2mssn", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"chmod +x /app/solution/run_final.sh\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\\nx=pd.read_parquet(P)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ny=validate_ranking(x,targets)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(u,i) for u,i in y[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\\nassert list(x.columns)==['uid','item_id','rank']\\nassert not bad\\nprint({'path':str(P),'rows':len(y),'users':y.uid.nunique(),'items':y.item_id.nunique(),'columns':list(y.columns),'ineligible':len(bad),'bytes':P.stat().st_size})\\nprint(y.sort_values(['uid','rank']).head(12).to_string(index=False))\\nPY\\n/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py /app/solution/score_ensemble.py\\nls -lh /app/solution/submission.parquet /app/solution/model_test_test_all_component.pt /app/solution/model_test_test_novel_component.pt /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" }, "result": { "timestamp": "2026-08-31T19:11:42.665Z", "content": "Script completed\nWall time 2.8 seconds\nOutput:\n\n{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'items': 4401, 'columns': ['uid', 'item_id', 'rank'], 'ineligible': 0, 'bytes': 1511937}\n uid item_id rank\n 100 8250570 1\n 100 8931972 2\n 100 8335640 3\n 100 5488609 4\n 100 2722711 5\n 100 5234367 6\n 100 6414726 7\n 100 672002 8\n 100 2416157 9\n 100 4948220 10\n 100 1758776 11\n 100 1336851 12\n-rw-r--r-- 1 agent agent 1.8K Aug 31 19:11 /app/solution/README.md\n-rw-r--r-- 1 agent agent 30M Aug 31 19:08 /app/solution/model_test_test_all_component.pt\n-rw-r--r-- 1 agent agent 30M Aug 31 19:10 /app/solution/model_test_test_novel_component.pt\n-rw-r--r-- 1 agent agent 1.5M Aug 31 19:10 /app/solution/submission.parquet\n", "isError": null } } ] }, { "id": 170, "role": "agent", "timestamp": "2026-08-31T19:11:47.091Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-08-31T19:11:47.091Z", "text": "" }, { "kind": "message", "timestamp": "2026-08-31T19:11:52.561Z", "text": "Built and validated the CPU PyTorch two-tower ranker.\n\n- Final submission: [submission.parquet](/app/solution/submission.parquet)\n- Training/application code: [build_ranker.py](/app/solution/build_ranker.py), [score_ensemble.py](/app/solution/score_ensemble.py)\n- Reproduction command: [run_final.sh](/app/solution/run_final.sh)\n- Documentation: [README.md](/app/solution/README.md)\n- Public validation NDCG@10: **0.020363**\n\nFinal audit passed: 845,600 rows, 8,456 users, ranks 1–100, no duplicates, and zero test-ineligible items." } ] } ] }