yambda-discovery-ranking-two-tower-base / run-009-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:26:07.334Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-08-31T18:26:09.208Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-08-31T18:26:09.208Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-08-31T18:26:09.208Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-08-31T18:26:09.208Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "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 } } } ### Harness context { "type": "turn_context", "turn_id": "01a05912-4f60-7390-b9b7-47c9098e00d4", "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" } ## Step 6 · user · 2026-08-31T18:26:09.218Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-08-31T18:26:11.413Z ### Thinking unavailable — no text recorded I’ll build and validate this end to end, keeping a valid submission in place early and then iterating on ranking quality. I’ll first inspect the data shapes, eligibility density, and interaction statistics, then implement a scalable event-sampled PyTorch two-tower and tune it only against the public window. ### exec · 2026-08-31T18:26:21.619Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data, schemas, environment, and public evaluator\",status:\"in_progress\"},\n {step:\"Implement scalable history-feature two-tower training and ranking pipeline\",status:\"pending\"},\n {step:\"Train validation models, evaluate, and tune\",status:\"pending\"},\n {step:\"Refit on train+validation and write a valid test submission\",status:\"pending\"},\n {step:\"Verify artifacts and document the solution\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:21.692Z Script completed Wall time 0.1 seconds Output: /app /task/requirements.txt /task/evaluate_public.py /task/ranking_evaluation.py /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/validation_preference_state.parquet """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-08-31T18:26:27.438Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:36.783Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n d=pd.read_parquet(f)\\n print(f.name, d.shape, list(d.columns), {c:(int(d[c].min()),int(d[c].max())) for c in ['timestamp'] if c in d})\\n print(d.head(2).to_dict('records'))\\nfor stage in ['validation','test']:\\n with np.load(p/f'{stage}_eligible_candidates.npz') as z:\\n print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n pc=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('eligible min/mean/max percentiles',pc.min(),pc.mean(),pc.max(),np.percentile(pc,[1,25,50,75,99]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:36.919Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-08-31T18:26:38.788Z ### Thinking unavailable — no text recorded The default shell’s `python` is a minimal interpreter despite the task’s pinned environment. I’m locating the task’s actual PyTorch/Pandas interpreter now; this is an environment-path issue, not a data issue. ### exec · 2026-08-31T18:26:44.591Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 uv 2>/dev/null || true\\nls -la /task /app\\nfind / -maxdepth 4 -type f -name 'python*' 2>/dev/null | sed -n '1,100p'\\nenv | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:44.677Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /app: total 12 drwxr-xr-x 3 agent agent 4096 Aug 31 18:23 . drwxr-xr-x 1 root root 4096 Aug 31 18:26 .. drwxr-xr-x 2 agent agent 4096 Aug 31 18:23 solution /task: total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:23 . drwxr-xr-x 1 root root 4096 Aug 31 18:26 .. -r--r--r-- 1 root root 0 Aug 31 18:09 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:10 data -r--r--r-- 1 root root 5774 Aug 31 18:09 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:09 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:09 requirements.txt /usr/local/bin/python3.12-config /usr/local/bin/python3.12 CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05912-4f4c-7c83-8d0f-badb40e6a17c COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=cd3e042a7152 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/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 PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ## Step 10 · agent · 2026-08-31T18:26:46.607Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:51.125Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt /opt/* 2>/dev/null | sed -n '1,160p'\\nfind / -type d \\\\( -name site-packages -o -name dist-packages \\\\) 2>/dev/null | sed -n '1,100p'\\nfind / -type f -path '*/bin/python' 2>/dev/null | sed -n '1,100p'\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:51.575Z Script completed Wall time 0.4 seconds Output: /opt: total 16 drwxr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:26 .. drwxr-xr-x 4 root root 4096 Aug 31 18:22 codex-home dr-xr-xr-x 1 root root 4096 Aug 31 18:23 task-venv /opt/codex-home: total 16 drwxr-xr-x 4 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:22 .. drwxr-xr-x 3 root root 4096 Aug 31 18:22 packages drwxr-xr-x 3 root root 4096 Aug 31 18:22 tmp /opt/task-venv: total 32 dr-xr-xr-x 1 root root 4096 Aug 31 18:23 . drwxr-xr-x 1 root root 4096 Aug 31 18:22 .. -r--r--r-- 1 root root 0 Aug 31 18:23 .lock dr-xr-xr-x 1 root root 4096 Aug 31 18:23 bin dr-xr-xr-x 1 root root 4096 Aug 31 18:23 etc dr-xr-xr-x 1 root root 4096 Aug 31 18:22 include dr-xr-xr-x 1 root root 4096 Aug 31 18:22 lib lrwxrwxrwx 1 root root 3 Aug 31 18:22 lib64 -> lib -r--r--r-- 1 root root 171 Aug 31 18:22 pyvenv.cfg dr-xr-xr-x 1 root root 4096 Aug 31 18:23 share /opt/task-venv/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages ## Step 11 · agent · 2026-08-31T18:26:53.056Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:03.651Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, torch\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n d=pd.read_parquet(f)\\n mm={c:(int(d[c].min()),int(d[c].max())) for c in ['timestamp'] if c in d and len(d)}\\n print(f.name, d.shape, list(d.columns), mm)\\n print(' ',d.head(2).to_dict('records'))\\nfor stage in ['validation','test']:\\n with np.load(p/f'{stage}_eligible_candidates.npz') as z:\\n print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n pc=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print(' eligible min/mean/max percentiles',pc.min(),pc.mean(),pc.max(),np.percentile(pc,[1,25,50,75,99]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:09.254Z Script completed Wall time 5.6 seconds Output: versions 2.3.3 2.2.6 2.8.0+cpu candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] {} [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}] target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] {} [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}] test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state'] {} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (75, 24786580)} [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}] train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (80, 24786790)} [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}] train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] {'timestamp': (0, 24786795)} [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}] train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (15950, 24785395)} [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}] train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (250, 24786570)} [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}] val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24789920, 25393280)} [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}] val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24788815, 25393390)} [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}] val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] {'timestamp': (24788600, 25393395)} [{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}] val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24810715, 25393035)} [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}] val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24789005, 25393385)} [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}] validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state'] {} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible min/mean/max percentiles 24657 29067.306291390727 30000 [26724.2 28682.75 29242. 29644. 29979. ] test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible min/mean/max percentiles 24552 29041.3178807947 30000 [26672.7 28657. 29220. 29619.25 29975. ] ## Step 12 · agent · 2026-08-31T18:27:25.248Z ### Thinking unavailable — no text recorded The data is substantial enough to reflect the intended scaling regime (43.5M listens), while retrieval is tractable (8,456 targets × 30,000 catalog items, with ~29k eligible per user). I’m establishing popularity and repeat-history baselines before training so every learned change has a measured reference and a structurally valid file remains available. ### exec · 2026-08-31T18:28:06.870Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nD=Path('/task/data'); O=Path('/app/solution')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\\nwith np.load(D/'validation_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\n pos={int(x):i for i,x in enumerate(items)}\\n order=np.array([pos[int(x)] for x in cat.item_id],dtype=np.int32)\\n rows=[]\\n for r,u in enumerate(uids):\\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n top=items[order[m[order]][:100]]\\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nD=Path('/task/data')\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); cset=set(cat.item_id.astype(int))\\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nwith np.load(D/'validation_eligible_candidates.npz') as z:\\n uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(items)}\\n def elig(u,i):\\n r=ur.get(int(u)); c=ic.get(int(i)); return r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1)\\nstrong=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\\ng={}\\nfor u,i in strong.itertuples(index=False,name=None):\\n if elig(u,i):g.setdefault(int(u),{})[int(i)]=1\\nfor u,i in likes.itertuples(index=False,name=None):\\n if elig(u,i):g.setdefault(int(u),{})[int(i)]=3\\nprint('gold users/items',len(g),sum(map(len,g.values())),'per user',np.percentile([len(x) for x in g.values()],[0,25,50,75,90,99,100]))\\n# load training seen sets only for gold users/items to measure repeat\\nneedu=set(g); needpairs={(u,i) for u,x in g.items() for i in x}\\ntrli=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','played_ratio_pct'])\\ntrli=trli[trli.uid.isin(needu)&(trli.played_ratio_pct>=50)][['uid','item_id']].drop_duplicates()\\nseen=set(map(tuple,trli.itertuples(index=False,name=None)))\\ntrla=pd.read_parquet(D/'train_likes.parquet',columns=['uid','item_id']); trla=trla[trla.uid.isin(needu)].drop_duplicates(); liked=set(map(tuple,trla.itertuples(index=False,name=None)))\\nprint('gold repeated positive listen',sum(p in seen for p in needpairs)/len(needpairs),'prior like event',sum(p in liked for p in needpairs)/len(needpairs))\\nprint('train unique users/items listens',trli.uid.nunique(),trli.item_id.nunique())\\n# top gold popularity ranks\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nrr=np.array([rank[i] for u,x in g.items() for i in x]);print('gold catalog rank pct',np.percentile(rr,[0,10,25,50,75,90,99,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:28:17.890Z Script running with cell ID 6 Wall time 11.0 seconds Output: ## Step 13 · agent · 2026-08-31T18:28:19.348Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:20.154Z { "cell_id": "6", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:28:21.156Z Script running with cell ID 6 Wall time 1.0 seconds Output: ### wait · 2026-08-31T18:28:23.110Z { "cell_id": "6", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:28:24.111Z Script running with cell ID 6 Wall time 1.0 seconds Output: ## Step 14 · agent · 2026-08-31T18:28:25.379Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:26.144Z { "cell_id": "6", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:28:26.178Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } gold users/items 4263 37769 per user [ 1. 2. 4. 10. 21. 63. 442.] gold repeated positive listen 0.0 prior like event 0.0 train unique users/items listens 4263 448434 gold catalog rank pct [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04 2.91010e+04 3.00000e+04] ## Step 15 · agent · 2026-08-31T18:28:46.028Z ### Thinking unavailable — no text recorded ## Step 16 · agent · 2026-08-31T18:28:51.347Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:08.921Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,time\\nfrom pathlib import Path\\nD=Path('/task/data'); cat=set(pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.astype(int)); tar=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nt=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),'sec',time.time()-t,'unique',x.uid.nunique(),x.item_id.nunique())\\nfor name,m in [('positive',x.played_ratio_pct>=50),('strong',x.played_ratio_pct>=80),('organic strong',(x.is_organic==1)&(x.played_ratio_pct>=80)),('target organic strong',x.uid.isin(tar)&(x.is_organic==1)&(x.played_ratio_pct>=80))]:\\n y=x[m]; print(name,len(y),'users',y.uid.nunique(),'items',y.item_id.nunique(),'catalog events',y.item_id.isin(cat).sum(),'catalog unique',y.loc[y.item_id.isin(cat),'item_id'].nunique())\\nprint('catalog events by organic/recommended and ratio')\\ny=x[x.item_id.isin(cat)];print(pd.crosstab(y.is_organic,pd.cut(y.played_ratio_pct,[-1,49,79,100])).to_string())\\n# target history size recent (last 90d if timestamps seconds? cutoff 24m sec ~287d; 90 days seconds)\\nz=x[x.uid.isin(tar)&(x.played_ratio_pct>=50)]\\nfor days in [7,30,90,999]:\\n a=z[z.timestamp>=24786800-days*86400]\\n print('target pos days',days,len(a),np.percentile(a.groupby('uid').size().reindex(list(tar),fill_value=0),[0,10,50,90,99,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:29:19.941Z Script running with cell ID 7 Wall time 11.0 seconds Output: ## Step 17 · agent · 2026-08-31T18:29:21.569Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:22.475Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:23.477Z Script running with cell ID 7 Wall time 1.0 seconds Output: ## Step 18 · agent · 2026-08-31T18:29:24.837Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:25.644Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:26.646Z Script running with cell ID 7 Wall time 1.0 seconds Output: ## Step 19 · agent · 2026-08-31T18:29:28.209Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:28.997Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:29.999Z Script running with cell ID 7 Wall time 1.0 seconds Output: ## Step 20 · agent · 2026-08-31T18:29:31.503Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:32.256Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:33.258Z Script running with cell ID 7 Wall time 1.0 seconds Output: ## Step 21 · agent · 2026-08-31T18:29:34.624Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:35.378Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:36.380Z Script running with cell ID 7 Wall time 1.0 seconds Output: ## Step 22 · agent · 2026-08-31T18:29:37.720Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:38.491Z { "cell_id": "7", "yield_time_ms": 1000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:29:39.102Z Script completed Wall time 0.6 seconds Output: ## Step 23 · agent · 2026-08-31T18:29:52.907Z ### Thinking unavailable — no text recorded The first diagnostic tried to materialize too much of the 43.5M-row table at once and was terminated before emitting results. I’m switching the implementation to Parquet batch scans and bounded arrays—the same streaming discipline the final training pipeline will use—so memory stays proportional to selected interactions rather than the full log. ### exec · 2026-08-31T18:30:09.879Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nfrom pathlib import Path\\nD=Path('/task/data'); cats=np.sort(pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.to_numpy(np.int64)); tar=np.sort(pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64))\\npf=pq.ParquetFile(D/'train_listens.parquet'); counts=np.zeros(6,dtype=np.int64); us=set(); start=time.time()\\nfor bi,b in enumerate(pf.iter_batches(batch_size=1_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\\n u=b.column(0).to_numpy(); i=b.column(1).to_numpy(); o=b.column(2).to_numpy(); p=b.column(3).to_numpy()\\n ic=np.isin(i,cats); strong=p>=80; org=o==1; ut=np.isin(u,tar)\\n counts += [len(u),ic.sum(),(ic&strong).sum(),(ic&strong&org).sum(),(ic&strong&org&ut).sum(),(strong&org).sum()]\\n us.update(map(int,u[ic&strong&org]))\\n if bi%10==0: print('batch',bi,'counts',counts.tolist(),'sec',round(time.time()-start,1),flush=True)\\nprint('FINAL total,cat,cat strong,cat orgstrong,target cat orgstrong,all orgstrong',counts.tolist(),'users cat orgstrong',len(us),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:15.269Z Script completed Wall time 5.4 seconds Output: batch 0 counts [1000000, 705306, 419072, 205405, 204376, 296188] sec 0.1 batch 10 counts [11000000, 7856999, 4688162, 2178007, 2168245, 3055481] sec 1.0 batch 20 counts [21000000, 15032871, 9070049, 4175767, 4157729, 5871778] sec 1.8 batch 30 counts [31000000, 22196837, 13387087, 6161736, 6130586, 8665545] sec 2.7 batch 40 counts [41000000, 29422799, 17725212, 8180552, 8143551, 11476291] sec 3.5 FINAL total,cat,cat strong,cat orgstrong,target cat orgstrong,all orgstrong [43484159, 31249613, 18801138, 8699160, 8661020, 12177583] users cat orgstrong 8588 ## Step 24 · agent · 2026-08-31T18:30:34.701Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T18:30:53.356Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-08-31T18:31:12.003Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-08-31T18:31:30.661Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:31:49.279Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:32:07.933Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:32:26.574Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:32:45.234Z ### Thinking unavailable — no text recorded ## Step 32 · agent · 2026-08-31T18:33:03.893Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-08-31T18:33:13.800Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:39.771Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-encoded PyTorch two-tower ranker.\\n+\\n+The script never creates a user/item matrix. It streams the event parquet,\\n+forms rolling temporal next-discovery examples, learns with in-batch sampled\\n+softmax, precomputes the catalog tower once, and scores eligible candidates by\\n+inner product.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\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+from torch import nn\\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+VAL_END = 25_393_400\\n+DAY = 86_400\\n+GAP = 1_800\\n+N_CHANNELS = 4\\n+HISTORY_LEN = 96\\n+WINDOWS = 18\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+ torch.set_num_threads(max(1, min(12, torch.get_num_threads())))\\n+\\n+\\n+@dataclass\\n+class Example:\\n+ ids: np.ndarray # [channel, history]\\n+ weights: np.ndarray # [channel, history]\\n+ stats: np.ndarray\\n+ targets: np.ndarray\\n+ target_weights: np.ndarray\\n+\\n+\\n+class RollingDataset(Dataset):\\n+ def __init__(self, examples: list[Example], seed: int):\\n+ self.examples = examples\\n+ self.seed = seed\\n+ self.epoch = 0\\n+\\n+ def __len__(self) -> int:\\n+ return len(self.examples)\\n+\\n+ def __getitem__(self, index: int):\\n+ ex = self.examples[index]\\n+ # Epoch-dependent deterministic weighted sampling gives likes (grade 3)\\n+ # more training mass without materializing duplicate contexts.\\n+ rng = np.random.default_rng(self.seed + 1_000_003 * self.epoch + index)\\n+ p = ex.target_weights.astype(np.float64)\\n+ p /= p.sum()\\n+ j = int(rng.choice(len(ex.targets), p=p))\\n+ return (\\n+ torch.from_numpy(ex.ids.astype(np.int64, copy=False)),\\n+ torch.from_numpy(ex.weights.astype(np.float32, copy=False)),\\n+ torch.from_numpy(ex.stats.astype(np.float32, copy=False)),\\n+ torch.tensor(int(ex.targets[j]), dtype=torch.long),\\n+ )\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user encoder: four weighted bags followed by an MLP.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.n_items = n_items\\n+ self.dim = dim\\n+ self.history_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.channel_scale = nn.Parameter(torch.ones(N_CHANNELS, dim))\\n+ self.mlp = nn.Sequential(\\n+ nn.LayerNorm(N_CHANNELS * dim + 6),\\n+ nn.Linear(N_CHANNELS * dim + 6, 2 * dim),\\n+ nn.GELU(),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+ nn.init.normal_(self.history_embedding.weight, std=0.04)\\n+ with torch.no_grad():\\n+ self.history_embedding.weight[n_items].zero_()\\n+\\n+ def latent(self, ids: torch.Tensor, weights: torch.Tensor, stats: torch.Tensor):\\n+ safe_ids = ids.clamp(min=0)\\n+ emb = self.history_embedding(safe_ids) # B,C,L,D\\n+ mask = (ids >= 0).to(weights.dtype)\\n+ w = weights * mask\\n+ pooled = (emb * w[..., None]).sum(2) / w.sum(2, keepdim=True).clamp_min(1e-6)\\n+ pooled = pooled * self.channel_scale[None]\\n+ z = self.mlp(torch.cat([pooled.flatten(1), stats], dim=1))\\n+ return nn.functional.normalize(z, dim=1)\\n+\\n+ def forward(self, ids: torch.Tensor, weights: torch.Tensor, stats: torch.Tensor,\\n+ popularity_dims: int = 0):\\n+ z = self.latent(ids, weights, stats)\\n+ if popularity_dims:\\n+ return torch.cat([z, torch.ones((len(z), popularity_dims), device=z.device)], 1)\\n+ return z\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently computable catalog-item encoder.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int, fixed_features: torch.Tensor):\\n+ super().__init__()\\n+ self.item_embedding = nn.Embedding(n_items, dim)\\n+ self.register_buffer(\\\"fixed_features\\\", fixed_features)\\n+ self.mlp = nn.Sequential(\\n+ nn.LayerNorm(dim + fixed_features.shape[1]),\\n+ nn.Linear(dim + fixed_features.shape[1], 2 * dim),\\n+ nn.GELU(),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+ nn.init.normal_(self.item_embedding.weight, std=0.04)\\n+\\n+ def latent(self, item_index: torch.Tensor):\\n+ x = torch.cat([self.item_embedding(item_index), self.fixed_features[item_index]], 1)\\n+ return nn.functional.normalize(self.mlp(x), dim=1)\\n+\\n+ def forward(self, item_index: torch.Tensor, popularity_coeffs=None):\\n+ z = self.latent(item_index)\\n+ if popularity_coeffs is not None:\\n+ coeff = torch.as_tensor(popularity_coeffs, dtype=z.dtype, device=z.device)\\n+ return torch.cat([z, self.fixed_features[item_index, :len(coeff)] * coeff], 1)\\n+ return z\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, fixed_features: torch.Tensor):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, dim, fixed_features)\\n+ self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+ def loss(self, ids, weights, stats, target):\\n+ u = self.user_tower.latent(ids, weights, stats)\\n+ v = self.item_tower.latent(target)\\n+ scale = self.logit_scale.exp().clamp(3.0, 40.0)\\n+ logits = scale * (u @ v.T)\\n+ labels = torch.arange(len(target), device=target.device)\\n+ # Symmetric contrastive loss makes both independently used towers train.\\n+ return 0.5 * (nn.functional.cross_entropy(logits, labels) +\\n+ nn.functional.cross_entropy(logits.T, labels))\\n+\\n+\\n+def catalog_info():\\n+ cat = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").sort_values(\\\"popularity_rank\\\")\\n+ raw_ids = cat.item_id.to_numpy(np.int64)\\n+ item_to_index = {int(x): i for i, x in enumerate(raw_ids)}\\n+ pop = np.log1p(cat.positive_listens.to_numpy(np.float32))\\n+ org = np.log1p(cat.organic_positive_listens.to_numpy(np.float32))\\n+ ratio = np.log1p(cat.organic_positive_listens.to_numpy(np.float32)) - pop\\n+ feats = np.stack([(pop - pop.mean()) / pop.std(),\\n+ (org - org.mean()) / org.std(),\\n+ (ratio - ratio.mean()) / ratio.std()], 1).astype(np.float32)\\n+ sorted_raw = np.sort(raw_ids)\\n+ sorted_to_index = np.array([item_to_index[int(x)] for x in sorted_raw], np.int32)\\n+ return cat, raw_ids, item_to_index, sorted_raw, sorted_to_index, feats\\n+\\n+\\n+def map_catalog(raw: np.ndarray, sorted_raw: np.ndarray, sorted_to_index: np.ndarray):\\n+ pos = np.searchsorted(sorted_raw, raw)\\n+ ok = pos < len(sorted_raw)\\n+ ok[ok] &= sorted_raw[pos[ok]] == raw[ok]\\n+ mapped = np.full(len(raw), -1, np.int32)\\n+ mapped[ok] = sorted_to_index[pos[ok]]\\n+ return mapped, ok\\n+\\n+\\n+def load_small_events(stage: str, item_to_index: dict[int, int]):\\n+ \\\"\\\"\\\"Load explicit events and the small validation listen tail by user.\\\"\\\"\\\"\\n+ families = [\\\"likes\\\", \\\"unlikes\\\", \\\"dislikes\\\", \\\"undislikes\\\"]\\n+ action_code = {\\\"likes\\\": 1, \\\"unlikes\\\": -1, \\\"dislikes\\\": 2, \\\"undislikes\\\": -2}\\n+ actions: dict[int, list[np.ndarray]] = {}\\n+ for family in families:\\n+ parts = [pd.read_parquet(DATA / f\\\"train_{family}.parquet\\\")]\\n+ if stage == \\\"final\\\":\\n+ parts.append(pd.read_parquet(DATA / f\\\"val_{family}.parquet\\\"))\\n+ frame = pd.concat(parts, ignore_index=True)\\n+ idx = frame.item_id.map(item_to_index)\\n+ frame = frame[idx.notna()].copy()\\n+ frame[\\\"idx\\\"] = idx[idx.notna()].astype(np.int32)\\n+ frame[\\\"code\\\"] = action_code[family]\\n+ for uid, g in frame.groupby(\\\"uid\\\", sort=False):\\n+ a = np.stack([g.timestamp.to_numpy(np.int64), g.idx.to_numpy(np.int64),\\n+ np.full(len(g), action_code[family], np.int64),\\n+ g.is_organic.to_numpy(np.int64)], 1)\\n+ actions.setdefault(int(uid), []).append(a)\\n+ actions2 = {u: np.concatenate(v)[np.argsort(np.concatenate(v)[:, 0])]\\n+ for u, v in actions.items()}\\n+\\n+ tails: dict[int, np.ndarray] = {}\\n+ if stage == \\\"final\\\":\\n+ f = pd.read_parquet(DATA / \\\"val_listens.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ idx = f.item_id.map(item_to_index)\\n+ f = f[idx.notna()].copy(); f[\\\"idx\\\"] = idx[idx.notna()].astype(np.int32)\\n+ for uid, g in f.groupby(\\\"uid\\\", sort=False):\\n+ tails[int(uid)] = np.stack([g.timestamp.to_numpy(np.int64),\\n+ g.idx.to_numpy(np.int64), g.is_organic.to_numpy(np.int64),\\n+ g.played_ratio_pct.to_numpy(np.int64)], 1)\\n+ return actions2, tails\\n+\\n+\\n+def active_explicit(actions: np.ndarray | None, cutoff: int):\\n+ if actions is None or not len(actions):\\n+ return np.empty(0, np.int32), np.empty(0, np.int32)\\n+ a = actions[actions[:, 0] < cutoff]\\n+ like, dislike = {}, {}\\n+ for ts, item, code, _org in a:\\n+ if code == 1: like[int(item)] = True\\n+ elif code == -1: like[int(item)] = False\\n+ elif code == 2: dislike[int(item)] = True\\n+ elif code == -2: dislike[int(item)] = False\\n+ return (np.fromiter((x for x, on in like.items() if on), np.int32),\\n+ np.fromiter((x for x, on in dislike.items() if on), np.int32))\\n+\\n+\\n+def pad_channel(ids: np.ndarray, weights: np.ndarray):\\n+ out_i = np.full(HISTORY_LEN, -1, np.int32)\\n+ out_w = np.zeros(HISTORY_LEN, np.float16)\\n+ n = min(HISTORY_LEN, len(ids))\\n+ if n:\\n+ out_i[:n] = ids[:n]\\n+ out_w[:n] = weights[:n].astype(np.float16)\\n+ return out_i, out_w\\n+\\n+\\n+def make_context(events: np.ndarray, actions: np.ndarray | None, cutoff: int,\\n+ supplied_like=None, supplied_dislike=None):\\n+ pre = events[events[:, 0] < cutoff]\\n+ pos = pre[pre[:, 3] >= 50]\\n+ channels_i, channels_w = [], []\\n+ if len(pos):\\n+ # Most-recent distinct positives with a 45-day recency decay.\\n+ rev_items = pos[::-1, 1].astype(np.int32)\\n+ _, first = np.unique(rev_items, return_index=True)\\n+ take = np.sort(first)[:HISTORY_LEN]\\n+ rows = pos[::-1][take]\\n+ age = np.maximum(0, cutoff - rows[:, 0]) / DAY\\n+ w = np.exp(-age / 45.0) * (0.7 + 0.3 * (rows[:, 3] / 100.0)) * (1 + 0.25 * rows[:, 2])\\n+ channels_i.append(rows[:, 1].astype(np.int32)); channels_w.append(w)\\n+\\n+ # Long-term taste: highest frequency/organic-quality distinct tracks.\\n+ uniq, inv, cnt = np.unique(pos[:, 1].astype(np.int32), return_inverse=True, return_counts=True)\\n+ org_count = np.bincount(inv, weights=pos[:, 2], minlength=len(uniq))\\n+ qual = np.bincount(inv, weights=pos[:, 3] / 100.0, minlength=len(uniq))\\n+ taste_w = np.log1p(cnt) * (1.0 + org_count / cnt) * (0.5 + qual / cnt)\\n+ order = np.argsort(-taste_w)[:HISTORY_LEN]\\n+ channels_i.append(uniq[order]); channels_w.append(taste_w[order])\\n+ else:\\n+ channels_i.extend([np.empty(0, np.int32)] * 2)\\n+ channels_w.extend([np.empty(0, np.float32)] * 2)\\n+\\n+ if supplied_like is None:\\n+ liked, disliked = active_explicit(actions, cutoff)\\n+ else:\\n+ liked, disliked = supplied_like, supplied_dislike\\n+ channels_i.extend([liked[-HISTORY_LEN:][::-1], disliked[-HISTORY_LEN:][::-1]])\\n+ channels_w.extend([np.ones(min(HISTORY_LEN, len(liked)), np.float32) * 1.5,\\n+ np.ones(min(HISTORY_LEN, len(disliked)), np.float32) * -1.0])\\n+ padded = [pad_channel(i, w) for i, w in zip(channels_i, channels_w)]\\n+ ids = np.stack([x[0] for x in padded])\\n+ weights = np.stack([x[1] for x in padded])\\n+ stats = np.array([math.log1p(len(pos)), math.log1p(len(np.unique(pos[:,1])) if len(pos) else 0),\\n+ math.log1p(len(liked)), math.log1p(len(disliked)),\\n+ float(pos[:,2].mean()) if len(pos) else 0.0,\\n+ float(pos[:,3].mean()/100) if len(pos) else 0.0], np.float32)\\n+ return ids, weights, stats\\n+\\n+\\n+def targets_for_window(events: np.ndarray, actions: np.ndarray | None, cutoff: int):\\n+ before = events[events[:, 0] < cutoff]\\n+ seen = set(map(int, before[:, 1]))\\n+ future = events[(events[:, 0] >= cutoff + GAP) & (events[:, 0] < cutoff + GAP + 7 * DAY)]\\n+ strong = future[(future[:, 2] == 1) & (future[:, 3] >= 80), 1].astype(np.int32)\\n+ grade = {int(x): 1.0 for x in strong if int(x) not in seen}\\n+ if actions is not None:\\n+ la = actions[(actions[:, 0] >= cutoff + GAP) &\\n+ (actions[:, 0] < cutoff + GAP + 7 * DAY) &\\n+ (actions[:, 2] == 1) & (actions[:, 3] == 1)]\\n+ for x in la[:, 1]:\\n+ if int(x) not in seen:\\n+ grade[int(x)] = 4.0\\n+ if not grade:\\n+ return np.empty(0, np.int32), np.empty(0, np.float32)\\n+ return np.fromiter(grade.keys(), np.int32), np.fromiter(grade.values(), np.float32)\\n+\\n+\\n+def build_data(stage: str, sorted_raw, sorted_to_index, item_to_index):\\n+ end = TRAIN_END if stage == \\\"validation\\\" else VAL_END\\n+ actions, tails = load_small_events(stage, item_to_index)\\n+ targets = set(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.astype(int))\\n+ examples: list[Example] = []\\n+ inference: dict[int, tuple[np.ndarray, np.ndarray, np.ndarray]] = {}\\n+\\n+ pref_name = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pref[\\\"idx\\\"] = pref.item_id.map(item_to_index)\\n+ pref = pref[pref.idx.notna()]\\n+ pref_by_uid = {}\\n+ for uid, g in pref.groupby(\\\"uid\\\", sort=False):\\n+ pref_by_uid[int(uid)] = (g.loc[g.state == \\\"liked\\\", \\\"idx\\\"].to_numpy(np.int32),\\n+ g.loc[g.state == \\\"disliked\\\", \\\"idx\\\"].to_numpy(np.int32))\\n+\\n+ cutoffs = [end - GAP - 7 * DAY * k for k in range(WINDOWS, 0, -1)]\\n+ pf = pq.ParquetFile(DATA / \\\"train_listens.parquet\\\")\\n+ current_uid = None\\n+ chunks: list[np.ndarray] = []\\n+ processed = 0\\n+\\n+ def finish(uid, pieces):\\n+ nonlocal processed\\n+ if uid is None:\\n+ return\\n+ ev = np.concatenate(pieces) if len(pieces) > 1 else pieces[0]\\n+ if stage == \\\"final\\\" and uid in tails:\\n+ ev = np.concatenate([ev, tails[uid]])\\n+ ev = ev[np.argsort(ev[:, 0], kind=\\\"stable\\\")]\\n+ act = actions.get(uid)\\n+ for cutoff in cutoffs:\\n+ ids, weights, stats = make_context(ev, act, cutoff)\\n+ tg, tw = targets_for_window(ev, act, cutoff)\\n+ if len(tg) and (ids[:2] >= 0).sum() >= 3:\\n+ examples.append(Example(ids, weights, stats, tg, tw))\\n+ if uid in targets:\\n+ supplied = pref_by_uid.get(uid, (np.empty(0,np.int32), np.empty(0,np.int32)))\\n+ inference[uid] = make_context(ev, act, end, *supplied)\\n+ processed += 1\\n+ if processed % 1000 == 0:\\n+ print(f\\\" histories {processed}, examples {len(examples)}\\\", flush=True)\\n+\\n+ columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ for batch in pf.iter_batches(batch_size=750_000, columns=columns):\\n+ uid = batch.column(0).to_numpy().astype(np.int64, copy=False)\\n+ ts = batch.column(1).to_numpy().astype(np.int64, copy=False)\\n+ raw = batch.column(2).to_numpy().astype(np.int64, copy=False)\\n+ org = batch.column(3).to_numpy().astype(np.int64, copy=False)\\n+ ratio = batch.column(4).to_numpy().astype(np.int64, copy=False)\\n+ idx, ok = map_catalog(raw, sorted_raw, sorted_to_index)\\n+ uid, ts, idx, org, ratio = uid[ok], ts[ok], idx[ok], org[ok], ratio[ok]\\n+ if not len(uid):\\n+ continue\\n+ boundaries = np.r_[0, np.flatnonzero(uid[1:] != uid[:-1]) + 1, len(uid)]\\n+ for a, b in zip(boundaries[:-1], boundaries[1:]):\\n+ u = int(uid[a])\\n+ seg = np.stack([ts[a:b], idx[a:b], org[a:b], ratio[a:b]], 1)\\n+ if current_uid is None:\\n+ current_uid = u\\n+ if u != current_uid:\\n+ finish(current_uid, chunks)\\n+ current_uid, chunks = u, []\\n+ chunks.append(seg)\\n+ finish(current_uid, chunks)\\n+ missing = targets - set(inference)\\n+ if missing:\\n+ raise RuntimeError(f\\\"missing inference histories for {len(missing)} targets\\\")\\n+ print(f\\\"built {len(examples)} rolling examples and {len(inference)} inference contexts\\\")\\n+ return examples, inference\\n+\\n+\\n+def train_model(model: TwoTower, examples: list[Example], epochs: int, seed: int):\\n+ ds = RollingDataset(examples, seed)\\n+ loader = DataLoader(ds, batch_size=256, shuffle=True, num_workers=0, drop_last=True)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ for epoch in range(epochs):\\n+ ds.epoch = epoch\\n+ model.train(); losses = []; start = time.time()\\n+ for ids, weights, stats, target in loader:\\n+ opt.zero_grad(set_to_none=True)\\n+ loss = model.loss(ids, weights, stats, target)\\n+ loss.backward()\\n+ nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ opt.step(); losses.append(float(loss))\\n+ print(f\\\"epoch {epoch+1}/{epochs}: loss={np.mean(losses):.4f} \\\"\\n+ f\\\"scale={model.logit_scale.exp().item():.2f} sec={time.time()-start:.1f}\\\", flush=True)\\n+\\n+\\n+@torch.inference_mode()\\n+def encode(model: TwoTower, inference, uid_order):\\n+ model.eval()\\n+ ids = torch.from_numpy(np.stack([inference[int(u)][0] for u in uid_order]).astype(np.int64))\\n+ w = torch.from_numpy(np.stack([inference[int(u)][1] for u in uid_order]).astype(np.float32))\\n+ st = torch.from_numpy(np.stack([inference[int(u)][2] for u in uid_order]).astype(np.float32))\\n+ users = []\\n+ for a in range(0, len(uid_order), 256):\\n+ users.append(model.user_tower.latent(ids[a:a+256], w[a:a+256], st[a:a+256]).numpy())\\n+ item_idx = torch.arange(model.item_tower.item_embedding.num_embeddings)\\n+ items = []\\n+ for a in range(0, len(item_idx), 2048):\\n+ items.append(model.item_tower.latent(item_idx[a:a+2048]).numpy())\\n+ return np.concatenate(users), np.concatenate(items)\\n+\\n+\\n+def truth_for_validation(uid_order, raw_ids):\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ packed = z[\\\"packed_eligible\\\"].copy(); euids = z[\\\"uids\\\"].astype(np.int64)\\n+ uidrow = {int(u): r for r, u in enumerate(euids)}\\n+ itemcol = {int(x): c for c, x in enumerate(raw_ids)}\\n+ gold = {}\\n+ li = pd.read_parquet(DATA / \\\"val_listens.parquet\\\")\\n+ li = li[(li.is_organic == 1) & (li.played_ratio_pct >= 80)]\\n+ la = pd.read_parquet(DATA / \\\"val_likes.parquet\\\"); la = la[la.is_organic == 1]\\n+ for frame, grade in [(li, 1), (la, 3)]:\\n+ for u, x in frame[[\\\"uid\\\",\\\"item_id\\\"]].drop_duplicates().itertuples(index=False, name=None):\\n+ r, c = uidrow.get(int(u)), itemcol.get(int(x))\\n+ if r is not None and c is not None and ((int(packed[r,c//8]) >> (c%8)) & 1):\\n+ gold.setdefault(int(u), {})[int(x)] = grade\\n+ return gold\\n+\\n+\\n+def rank_and_write(stage: str, users: np.ndarray, items: np.ndarray, feats: np.ndarray,\\n+ uid_order: np.ndarray, raw_ids: np.ndarray, output: Path,\\n+ coefficients=(0.0, 0.0), tune=False):\\n+ elig_path = DATA / (\\\"validation_eligible_candidates.npz\\\" if stage == \\\"validation\\\" else \\\"test_eligible_candidates.npz\\\")\\n+ with np.load(elig_path) as z:\\n+ euids = z[\\\"uids\\\"].astype(np.int64); packed = z[\\\"packed_eligible\\\"].copy()\\n+ if not np.array_equal(euids, uid_order):\\n+ order = {int(u): i for i, u in enumerate(uid_order)}\\n+ users = users[[order[int(u)] for u in euids]]; uid_order = euids\\n+\\n+ gold = truth_for_validation(uid_order, raw_ids) if tune else None\\n+ discounts = 1.0 / np.log2(np.arange(2, 12))\\n+\\n+ def one(coeff, collect=False):\\n+ ndcgs=[]; rows=[]\\n+ bias = coeff[0] * feats[:, 0] + coeff[1] * feats[:, 1]\\n+ for a in range(0, len(uid_order), 64):\\n+ score = users[a:a+64] @ items.T\\n+ score += bias[None]\\n+ mask = np.unpackbits(packed[a:a+len(score)], axis=1, count=len(raw_ids), bitorder=\\\"little\\\").astype(bool)\\n+ score[~mask] = -np.inf\\n+ ix = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ val = np.take_along_axis(score, ix, axis=1)\\n+ ix = np.take_along_axis(ix, np.argsort(-val, axis=1), axis=1)\\n+ for j, chosen in enumerate(ix):\\n+ uid = int(uid_order[a+j])\\n+ if collect:\\n+ rows.extend((uid, int(raw_ids[k]), rank+1) for rank,k in enumerate(chosen))\\n+ if gold is not None and uid in gold:\\n+ grades=np.array([gold[uid].get(int(raw_ids[k]),0) for k in chosen[:10]])\\n+ dcg=((2.0**grades-1)*discounts[:10]).sum()\\n+ ideal=np.array(sorted(gold[uid].values(),reverse=True)[:10])\\n+ idcg=((2.0**ideal-1)*discounts[:len(ideal)]).sum()\\n+ ndcgs.append(dcg/idcg)\\n+ return float(np.mean(ndcgs)) if ndcgs else 0.0, rows\\n+\\n+ best = tuple(coefficients); best_score = -1.0\\n+ if tune:\\n+ grid = [(a,b) for a in [0.0,0.01,0.02,0.04,0.07,0.10,0.15]\\n+ for b in [0.0,0.01,0.03,0.06]]\\n+ for coeff in grid:\\n+ score,_=one(coeff)\\n+ print(f\\\"bias {coeff}: ndcg={score:.6f}\\\",flush=True)\\n+ if score>best_score: best_score,best=score,coeff\\n+ else:\\n+ best_score = 0.0\\n+ score, rows = one(best, collect=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\",\\\"item_id\\\",\\\"rank\\\"]).to_parquet(output,index=False)\\n+ print(f\\\"wrote {output}; coefficients={best}; validation_ndcg={score:.6f}\\\")\\n+ return best, score\\n+\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\",choices=[\\\"validation\\\",\\\"final\\\"],required=True)\\n+ ap.add_argument(\\\"--output\\\",type=Path,required=True)\\n+ ap.add_argument(\\\"--epochs\\\",type=int,default=6)\\n+ ap.add_argument(\\\"--dim\\\",type=int,default=64)\\n+ ap.add_argument(\\\"--seed\\\",type=int,default=20260831)\\n+ ap.add_argument(\\\"--coefficients\\\",default=\\\"0,0\\\")\\n+ ap.add_argument(\\\"--tune\\\",action=\\\"store_true\\\")\\n+ args=ap.parse_args(); OUT.mkdir(parents=True,exist_ok=True); seed_everything(args.seed)\\n+ cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info()\\n+ examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\\n+ model=TwoTower(len(raw),args.dim,torch.from_numpy(feats))\\n+ train_model(model,examples,args.epochs,args.seed)\\n+ uid_order=np.load(DATA/(\\\"validation_eligible_candidates.npz\\\" if args.stage==\\\"validation\\\" else \\\"test_eligible_candidates.npz\\\"))[\\\"uids\\\"].astype(np.int64)\\n+ users,items=encode(model,inference,uid_order)\\n+ coeff=tuple(map(float,args.coefficients.split(\\\",\\\")))\\n+ best,score=rank_and_write(args.stage,users,items,feats,uid_order,raw,args.output,coeff,args.tune)\\n+ ckpt=OUT/f\\\"model_{args.stage}.pt\\\"\\n+ torch.save({\\\"state_dict\\\":model.state_dict(),\\\"dim\\\":args.dim,\\\"coefficients\\\":best,\\n+ \\\"stage\\\":args.stage,\\\"validation_ndcg\\\":score},ckpt)\\n+ (OUT/f\\\"metrics_{args.stage}.json\\\").write_text(json.dumps({\\\"coefficients\\\":best,\\\"validation_ndcg\\\":score,\\n+ \\\"examples\\\":len(examples),\\\"epochs\\\":args.epochs,\\\"dim\\\":args.dim},indent=2)+\\\"\\\\n\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:37:39.808Z Script completed Wall time 0.0 seconds Output: {} ## Step 34 · agent · 2026-08-31T18:37:41.737Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:46.129Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /app/solution && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation.parquet --epochs 5 --dim 64 --tune\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:37:47.308Z Script completed Wall time 1.2 seconds Output: total 736 -rw-r--r-- 1 agent agent 23166 Aug 31 18:37 train_ranker.py -rw-r--r-- 1 agent agent 728599 Aug 31 18:28 validation_popularity.parquet SESSION_ID=30602 ## Step 35 · agent · 2026-08-31T18:37:48.586Z ### Thinking unavailable — no text recorded The first learned system is now running. Its user tower pools four history channels—recent positives, long-term taste, active likes, and active dislikes—while its item tower embeds catalog IDs plus offline popularity features. Training examples are rolling seven-day “new organic discovery” windows, with in-batch sampled-softmax negatives; neither preprocessing nor training constructs a global interaction matrix. ### exec · 2026-08-31T18:37:53.033Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:37:58.057Z Script completed Wall time 5.0 seconds Output: ## Step 36 · agent · 2026-08-31T18:37:59.392Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:00.754Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:05.776Z Script completed Wall time 5.0 seconds Output: ## Step 37 · agent · 2026-08-31T18:38:07.076Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:08.419Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:13.438Z Script completed Wall time 5.0 seconds Output: histories 1000, examples 6766 ## Step 38 · agent · 2026-08-31T18:38:14.823Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:16.146Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:21.166Z Script completed Wall time 5.0 seconds Output: ## Step 39 · agent · 2026-08-31T18:38:22.522Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:23.890Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:28.909Z Script completed Wall time 5.0 seconds Output: histories 2000, examples 13768 ## Step 40 · agent · 2026-08-31T18:38:31.231Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:32.611Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:37.629Z Script completed Wall time 5.0 seconds Output: ## Step 41 · agent · 2026-08-31T18:38:38.971Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:40.423Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:45.445Z Script completed Wall time 5.0 seconds Output: histories 3000, examples 20630 ## Step 42 · agent · 2026-08-31T18:38:46.963Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:48.417Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:53.436Z Script completed Wall time 5.0 seconds Output: ## Step 43 · agent · 2026-08-31T18:38:54.785Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:56.123Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:01.141Z Script completed Wall time 5.0 seconds Output: histories 4000, examples 27686 ## Step 44 · agent · 2026-08-31T18:39:03.250Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:04.647Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:09.669Z Script completed Wall time 5.0 seconds Output: ## Step 45 · agent · 2026-08-31T18:39:11.162Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:12.482Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:17.500Z Script completed Wall time 5.0 seconds Output: histories 5000, examples 34699 ## Step 46 · agent · 2026-08-31T18:39:19.004Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:20.346Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:25.364Z Script completed Wall time 5.0 seconds Output: ## Step 47 · agent · 2026-08-31T18:39:27.154Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:28.474Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:33.492Z Script completed Wall time 5.0 seconds Output: histories 6000, examples 41314 ## Step 48 · agent · 2026-08-31T18:39:34.934Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:36.307Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:41.326Z Script completed Wall time 5.0 seconds Output: ## Step 49 · agent · 2026-08-31T18:39:42.711Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:44.054Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:49.076Z Script completed Wall time 5.0 seconds Output: histories 7000, examples 48162 ## Step 50 · agent · 2026-08-31T18:39:50.544Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:51.926Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:56.945Z Script completed Wall time 5.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:39:58.379Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:59.711Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:04.730Z Script completed Wall time 5.0 seconds Output: histories 8000, examples 55110 ## Step 52 · agent · 2026-08-31T18:40:06.069Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:07.431Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:12.450Z Script completed Wall time 5.0 seconds Output: ## Step 53 · agent · 2026-08-31T18:40:13.925Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:15.265Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:20.287Z Script completed Wall time 5.0 seconds Output: histories 9000, examples 62054 ## Step 54 · agent · 2026-08-31T18:40:22.370Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:23.746Z { "code": "const r = await tools.write_stdin({session_id:30602,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:23.787Z Script completed Wall time 0.0 seconds Output: Traceback (most recent call last): File "/app/solution/train_ranker.py", line 505, in main() File "/app/solution/train_ranker.py", line 490, in main examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 372, in build_data raise RuntimeError(f"missing inference histories for {len(missing)} targets") RuntimeError: missing inference histories for 4 targets ## Step 55 · agent · 2026-08-31T18:40:28.950Z ### Thinking unavailable — no text recorded Preprocessing found 62,054 usable rolling examples. Four target users have no catalog-mapped listen history even though they satisfy the broader target criterion; I’m handling them with their explicit preference channels (or a cold-start zero history) rather than dropping them, preserving exact target coverage. ### exec · 2026-08-31T18:40:38.871Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n finish(current_uid, chunks)\\n missing = targets - set(inference)\\n- if missing:\\n- raise RuntimeError(f\\\"missing inference histories for {len(missing)} targets\\\")\\n+ # A few qualified targets have positive history entirely outside the 30k\\n+ # retrieval catalog. Their explicit-state bags still provide a valid\\n+ # history-derived encoding; if those too are empty this is a true cold start.\\n+ for uid in missing:\\n+ supplied = pref_by_uid.get(uid, (np.empty(0,np.int32), np.empty(0,np.int32)))\\n+ inference[uid] = make_context(np.empty((0,4), np.int64), actions.get(uid),\\n+ end, *supplied)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:40:38.934Z Script completed Wall time 0.0 seconds Output: {} ## Step 56 · agent · 2026-08-31T18:40:41.298Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:44.649Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation.parquet --epochs 5 --dim 64 --tune\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:40:45.830Z Script completed Wall time 1.2 seconds Output: SESSION_ID=66415 ## Step 57 · agent · 2026-08-31T18:40:47.678Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:49.010Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:00.031Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 58 · agent · 2026-08-31T18:41:02.104Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:02.871Z { "cell_id": "33", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:41:13.873Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 59 · agent · 2026-08-31T18:41:15.933Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:16.718Z { "cell_id": "33", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:41:19.033Z Script completed Wall time 2.3 seconds Output: histories 1000, examples 6766 ## Step 60 · agent · 2026-08-31T18:41:21.300Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:22.712Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:33.731Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:41:35.150Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:35.965Z { "cell_id": "34", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:41:46.967Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 62 · agent · 2026-08-31T18:41:48.489Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:49.270Z { "cell_id": "34", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:41:52.734Z Script completed Wall time 3.5 seconds Output: histories 2000, examples 13768 histories 3000, examples 20630 ## Step 63 · agent · 2026-08-31T18:41:54.755Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:56.124Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:07.143Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 64 · agent · 2026-08-31T18:42:09.138Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:09.946Z { "cell_id": "35", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:20.948Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:42:22.555Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:23.394Z { "cell_id": "35", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:26.146Z Script completed Wall time 2.8 seconds Output: histories 4000, examples 27686 histories 5000, examples 34699 ## Step 66 · agent · 2026-08-31T18:42:27.653Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:29.524Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:40.542Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:42:42.005Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:42.788Z { "cell_id": "36", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:53.789Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 68 · agent · 2026-08-31T18:42:55.369Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:56.163Z { "cell_id": "36", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:59.547Z Script completed Wall time 3.4 seconds Output: histories 6000, examples 41314 histories 7000, examples 48162 ## Step 69 · agent · 2026-08-31T18:43:00.936Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:02.274Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:43:13.293Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 70 · agent · 2026-08-31T18:43:15.049Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:15.565Z { "cell_id": "37", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:43:26.566Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 71 · agent · 2026-08-31T18:43:28.048Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:28.807Z { "cell_id": "37", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:43:32.300Z Script completed Wall time 3.5 seconds Output: histories 8000, examples 55110 histories 9000, examples 62054 /app/solution/train_ranker.py:394: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) ## Step 72 · agent · 2026-08-31T18:43:34.137Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:35.511Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:43:46.530Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 73 · agent · 2026-08-31T18:43:48.021Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:48.818Z { "cell_id": "38", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:43:59.821Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 74 · agent · 2026-08-31T18:44:01.307Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:02.112Z { "cell_id": "38", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:05.537Z Script completed Wall time 3.4 seconds Output: built 63587 rolling examples and 8456 inference contexts epoch 1/5: loss=5.5704 scale=10.79 sec=13.2 epoch 2/5: loss=5.4493 scale=10.37 sec=13.1 epoch 3/5: loss=5.2270 scale=10.26 sec=13.1 ## Step 75 · agent · 2026-08-31T18:44:07.142Z ### Thinking unavailable — no text recorded Preprocessing is complete with full target coverage, and optimization is converging (contrastive loss fell from 5.57 to 5.23 by epoch 3). Once encoding finishes, I’ll tune only the two allowed tower-output popularity coordinates on public NDCG and independently verify the resulting file with the supplied evaluator. ### exec · 2026-08-31T18:44:11.289Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:44:22.319Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 76 · agent · 2026-08-31T18:44:23.931Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:24.740Z { "cell_id": "39", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:35.743Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T18:44:37.873Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:38.896Z { "cell_id": "39", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:41.330Z Script completed Wall time 2.4 seconds Output: epoch 4/5: loss=4.9430 scale=11.05 sec=13.1 epoch 5/5: loss=4.6822 scale=12.27 sec=13.0 bias (0.0, 0.0): ndcg=0.001191 bias (0.0, 0.01): ndcg=0.001542 bias (0.0, 0.03): ndcg=0.003831 bias (0.0, 0.06): ndcg=0.007696 bias (0.01, 0.0): ndcg=0.001478 bias (0.01, 0.01): ndcg=0.002701 bias (0.01, 0.03): ndcg=0.005176 bias (0.01, 0.06): ndcg=0.009149 ## Step 78 · agent · 2026-08-31T18:44:43.267Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:44.645Z { "code": "const r = await tools.write_stdin({session_id:66415,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:44:55.686Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:44:57.301Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:58.068Z { "cell_id": "40", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:45:09.075Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 80 · agent · 2026-08-31T18:45:10.635Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:11.443Z { "cell_id": "40", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:45:11.489Z Script completed Wall time 0.0 seconds Output: bias (0.02, 0.0): ndcg=0.002767 bias (0.02, 0.01): ndcg=0.003770 bias (0.02, 0.03): ndcg=0.006236 bias (0.02, 0.06): ndcg=0.009738 bias (0.04, 0.0): ndcg=0.005032 bias (0.04, 0.01): ndcg=0.006311 bias (0.04, 0.03): ndcg=0.009199 bias (0.04, 0.06): ndcg=0.010516 bias (0.07, 0.0): ndcg=0.008185 bias (0.07, 0.01): ndcg=0.009308 bias (0.07, 0.03): ndcg=0.010484 bias (0.07, 0.06): ndcg=0.011221 bias (0.1, 0.0): ndcg=0.009955 bias (0.1, 0.01): ndcg=0.010099 bias (0.1, 0.03): ndcg=0.010847 bias (0.1, 0.06): ndcg=0.011702 bias (0.15, 0.0): ndcg=0.010760 bias (0.15, 0.01): ndcg=0.010948 bias (0.15, 0.03): ndcg=0.011194 bias (0.15, 0.06): ndcg=0.011302 wrote /app/solution/validation.parquet; coefficients=(0.1, 0.06); validation_ndcg=0.011702 ## Step 81 · agent · 2026-08-31T18:45:31.489Z ### Thinking unavailable — no text recorded ## Step 82 · agent · 2026-08-31T18:45:50.144Z ### Thinking unavailable — no text recorded ## Step 83 · agent · 2026-08-31T18:46:08.768Z ### Thinking unavailable — no text recorded ## Step 84 · agent · 2026-08-31T18:46:10.562Z ### Thinking unavailable — no text recorded The first learned ranker scores 0.011702 internally, modestly above the 0.010710 popularity baseline. I found one concrete encoder defect before accepting that result: the disliked-history channel used signed weights in its normalization denominator, which could amplify that channel dramatically. I’m correcting the denominator to use absolute mass and removing duplicate-positive false negatives from the sampled-softmax batch, then retraining; both changes preserve the exact two-tower decomposition. ### exec · 2026-08-31T18:46:32.322Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n mask = (ids >= 0).to(weights.dtype)\\n w = weights * mask\\n- pooled = (emb * w[..., None]).sum(2) / w.sum(2, keepdim=True).clamp_min(1e-6)\\n+ pooled = (emb * w[..., None]).sum(2) / w.abs().sum(2, keepdim=True).clamp_min(1e-6)\\n@@\\n logits = scale * (u @ v.T)\\n labels = torch.arange(len(target), device=target.device)\\n- # Symmetric contrastive loss makes both independently used towers train.\\n- return 0.5 * (nn.functional.cross_entropy(logits, labels) +\\n- nn.functional.cross_entropy(logits.T, labels))\\n+ # Equal target IDs in a batch are the same positive vector, not\\n+ # legitimate negatives. Mask those duplicate columns for each row.\\n+ duplicate = target[:, None].eq(target[None, :])\\n+ duplicate.fill_diagonal_(False)\\n+ logits = logits.masked_fill(duplicate, -torch.inf)\\n+ return nn.functional.cross_entropy(logits, labels)\\n@@\\n ap.add_argument(\\\"--tune\\\",action=\\\"store_true\\\")\\n+ ap.add_argument(\\\"--cache\\\",action=\\\"store_true\\\",help=\\\"reuse preprocessed stage data\\\")\\n@@\\n- examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\\n+ cache_path=OUT/f\\\"cache_{args.stage}.pt\\\"\\n+ if args.cache and cache_path.exists():\\n+ cached=torch.load(cache_path,weights_only=False)\\n+ examples,inference=cached[\\\"examples\\\"],cached[\\\"inference\\\"]\\n+ print(f\\\"loaded {len(examples)} examples from {cache_path}\\\")\\n+ else:\\n+ examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\\n+ if args.cache:\\n+ torch.save({\\\"examples\\\":examples,\\\"inference\\\":inference},cache_path)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:46:32.402Z Script completed Wall time 0.0 seconds Output: {} ## Step 85 · agent · 2026-08-31T18:46:34.709Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:39.018Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v2.parquet --epochs 8 --dim 64 --tune --cache\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:46:40.203Z Script completed Wall time 1.2 seconds Output: SESSION_ID=23054 ## Step 86 · agent · 2026-08-31T18:46:41.641Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:42.982Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:46:53.999Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T18:46:55.499Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:56.288Z { "cell_id": "43", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:07.290Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 88 · agent · 2026-08-31T18:47:09.947Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:10.726Z { "cell_id": "43", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:13.003Z Script completed Wall time 2.3 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.011702, "quality_users": 4263 } } histories 1000, examples 6766 ## Step 89 · agent · 2026-08-31T18:47:14.471Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:15.811Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:47:26.830Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 90 · agent · 2026-08-31T18:47:28.305Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:29.126Z { "cell_id": "44", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:40.128Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 91 · agent · 2026-08-31T18:47:41.619Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:42.400Z { "cell_id": "44", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:45.835Z Script completed Wall time 3.4 seconds Output: histories 2000, examples 13768 histories 3000, examples 20630 ## Step 92 · agent · 2026-08-31T18:47:47.210Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:48.603Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:47:59.624Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 93 · agent · 2026-08-31T18:48:01.299Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:02.111Z { "cell_id": "45", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:13.114Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 94 · agent · 2026-08-31T18:48:14.642Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:15.408Z { "cell_id": "45", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:18.630Z Script completed Wall time 3.2 seconds Output: histories 4000, examples 27686 histories 5000, examples 34699 ## Step 95 · agent · 2026-08-31T18:48:20.042Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:21.397Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:48:32.416Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T18:48:33.877Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:34.647Z { "cell_id": "46", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:45.649Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 97 · agent · 2026-08-31T18:48:47.081Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:47.857Z { "cell_id": "46", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:51.419Z Script completed Wall time 3.6 seconds Output: histories 6000, examples 41314 histories 7000, examples 48162 ## Step 98 · agent · 2026-08-31T18:48:53.040Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:54.239Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:49:05.257Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 99 · agent · 2026-08-31T18:49:06.895Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:07.688Z { "cell_id": "47", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:18.690Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T18:49:20.347Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:21.118Z { "cell_id": "47", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:24.260Z Script completed Wall time 3.1 seconds Output: histories 8000, examples 55110 histories 9000, examples 62054 ## Step 101 · agent · 2026-08-31T18:49:25.803Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:27.137Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:49:38.165Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T18:49:39.780Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:40.580Z { "cell_id": "48", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:51.583Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 103 · agent · 2026-08-31T18:49:53.427Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:54.202Z { "cell_id": "48", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:57.170Z Script completed Wall time 3.0 seconds Output: /app/solution/train_ranker.py:397: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) built 63587 rolling examples and 8456 inference contexts epoch 1/8: loss=5.5282 scale=11.33 sec=11.3 epoch 2/8: loss=5.2892 scale=10.95 sec=11.2 ## Step 104 · agent · 2026-08-31T18:49:58.554Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:59.905Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:50:10.924Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 105 · agent · 2026-08-31T18:50:12.741Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:13.534Z { "cell_id": "49", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:24.536Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T18:50:25.967Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:26.738Z { "cell_id": "49", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:29.931Z Script completed Wall time 3.2 seconds Output: epoch 3/8: loss=5.0591 scale=10.87 sec=11.4 epoch 4/8: loss=4.8768 scale=11.05 sec=11.3 epoch 5/8: loss=4.7087 scale=11.47 sec=11.2 ## Step 107 · agent · 2026-08-31T18:50:31.256Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:32.615Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:50:43.639Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 108 · agent · 2026-08-31T18:50:46.674Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:47.441Z { "cell_id": "50", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:58.447Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 109 · agent · 2026-08-31T18:51:00.143Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:00.881Z { "cell_id": "50", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:02.651Z Script completed Wall time 1.8 seconds Output: epoch 6/8: loss=4.5389 scale=12.30 sec=11.2 epoch 7/8: loss=4.3898 scale=13.22 sec=11.1 epoch 8/8: loss=4.2427 scale=14.25 sec=11.2 bias (0.0, 0.0): ndcg=0.003578 bias (0.0, 0.01): ndcg=0.004725 bias (0.0, 0.03): ndcg=0.009224 bias (0.0, 0.06): ndcg=0.014832 ## Step 110 · agent · 2026-08-31T18:51:04.263Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:05.610Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:51:16.660Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 111 · agent · 2026-08-31T18:51:18.304Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:19.103Z { "cell_id": "51", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:30.113Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 112 · agent · 2026-08-31T18:51:31.707Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:32.517Z { "cell_id": "51", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:35.673Z Script completed Wall time 3.2 seconds Output: bias (0.01, 0.0): ndcg=0.004982 bias (0.01, 0.01): ndcg=0.007230 bias (0.01, 0.03): ndcg=0.011423 bias (0.01, 0.06): ndcg=0.015878 bias (0.02, 0.0): ndcg=0.007246 bias (0.02, 0.01): ndcg=0.009582 bias (0.02, 0.03): ndcg=0.013610 bias (0.02, 0.06): ndcg=0.016754 bias (0.04, 0.0): ndcg=0.011249 bias (0.04, 0.01): ndcg=0.013616 bias (0.04, 0.03): ndcg=0.015797 bias (0.04, 0.06): ndcg=0.016895 bias (0.07, 0.0): ndcg=0.015038 bias (0.07, 0.01): ndcg=0.015999 bias (0.07, 0.03): ndcg=0.016344 bias (0.07, 0.06): ndcg=0.015374 bias (0.1, 0.0): ndcg=0.014908 bias (0.1, 0.01): ndcg=0.015174 bias (0.1, 0.03): ndcg=0.014464 bias (0.1, 0.06): ndcg=0.014235 bias (0.15, 0.0): ndcg=0.013171 bias (0.15, 0.01): ndcg=0.013020 bias (0.15, 0.03): ndcg=0.012914 ## Step 113 · agent · 2026-08-31T18:51:37.390Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:38.747Z { "code": "const r = await tools.write_stdin({session_id:23054,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:51:39.375Z Script completed Wall time 0.6 seconds Output: bias (0.15, 0.06): ndcg=0.012770 wrote /app/solution/validation_v2.parquet; coefficients=(0.04, 0.06); validation_ndcg=0.016895 ## Step 114 · agent · 2026-08-31T18:51:48.352Z ### Thinking unavailable — no text recorded The corrected model improves public-style NDCG to 0.016895, a 58% relative gain over popularity and 44% over the first neural run. The personalized latent score is now contributing meaningfully. I’m checking whether a few continuation epochs improve or overfit, while retaining `validation_v2.parquet` as the best valid ranking. ### exec · 2026-08-31T18:51:56.755Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n ap.add_argument(\\\"--cache\\\",action=\\\"store_true\\\",help=\\\"reuse preprocessed stage data\\\")\\n+ ap.add_argument(\\\"--resume\\\",type=Path,help=\\\"checkpoint whose state_dict is continued\\\")\\n@@\\n model=TwoTower(len(raw),args.dim,torch.from_numpy(feats))\\n+ if args.resume:\\n+ model.load_state_dict(torch.load(args.resume,weights_only=False)[\\\"state_dict\\\"])\\n+ print(f\\\"resumed weights from {args.resume}\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:51:56.844Z Script completed Wall time 0.0 seconds Output: {} ## Step 115 · agent · 2026-08-31T18:51:59.029Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:03.722Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_v2.parquet\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v3.parquet --epochs 5 --dim 64 --tune --cache --resume /app/solution/model_validation.pt\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:52:04.901Z Script completed Wall time 1.2 seconds Output: SESSION_ID=48086 ## Step 116 · agent · 2026-08-31T18:52:06.559Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:07.968Z { "code": "const r = await tools.write_stdin({session_id:48086,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:52:18.985Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 117 · agent · 2026-08-31T18:52:21.834Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:22.993Z { "cell_id": "55", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:33.995Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 118 · agent · 2026-08-31T18:52:35.550Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:36.434Z { "cell_id": "55", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:37.992Z Script completed Wall time 1.6 seconds Output: { "model": "/app/solution/validation_v2.parquet", "scores": { "quality_ndcg@10": 0.016895, "quality_users": 4263 } } /app/solution/train_ranker.py:397: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) loaded 63587 examples from /app/solution/cache_validation.pt resumed weights from /app/solution/model_validation.pt epoch 1/5: loss=4.0056 scale=15.43 sec=12.7 ## Step 119 · agent · 2026-08-31T18:52:39.565Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:40.909Z { "code": "const r = await tools.write_stdin({session_id:48086,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:52:51.937Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 120 · agent · 2026-08-31T18:52:53.396Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:54.200Z { "cell_id": "56", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:05.203Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 121 · agent · 2026-08-31T18:53:06.773Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:07.591Z { "cell_id": "56", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:10.941Z Script completed Wall time 3.3 seconds Output: epoch 2/5: loss=3.8502 scale=16.59 sec=12.5 epoch 3/5: loss=3.7115 scale=17.59 sec=12.1 epoch 4/5: loss=3.5775 scale=18.78 sec=11.2 ## Step 122 · agent · 2026-08-31T18:53:12.474Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:13.852Z { "code": "const r = await tools.write_stdin({session_id:48086,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:53:24.878Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 123 · agent · 2026-08-31T18:53:26.613Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:27.426Z { "cell_id": "57", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:38.440Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 124 · agent · 2026-08-31T18:53:40.470Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:41.192Z { "cell_id": "57", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:43.892Z Script completed Wall time 2.7 seconds Output: epoch 5/5: loss=3.4537 scale=19.86 sec=11.6 bias (0.0, 0.0): ndcg=0.004542 bias (0.0, 0.01): ndcg=0.006073 bias (0.0, 0.03): ndcg=0.009917 bias (0.0, 0.06): ndcg=0.013442 bias (0.01, 0.0): ndcg=0.006166 bias (0.01, 0.01): ndcg=0.007927 bias (0.01, 0.03): ndcg=0.011645 bias (0.01, 0.06): ndcg=0.013743 bias (0.02, 0.0): ndcg=0.007880 bias (0.02, 0.01): ndcg=0.010147 bias (0.02, 0.03): ndcg=0.012709 bias (0.02, 0.06): ndcg=0.013925 bias (0.04, 0.0): ndcg=0.010892 bias (0.04, 0.01): ndcg=0.012560 bias (0.04, 0.03): ndcg=0.013488 bias (0.04, 0.06): ndcg=0.013309 bias (0.07, 0.0): ndcg=0.012164 bias (0.07, 0.01): ndcg=0.012335 bias (0.07, 0.03): ndcg=0.012639 ## Step 125 · agent · 2026-08-31T18:54:03.604Z ### Thinking unavailable — no text recorded ## Step 126 · agent · 2026-08-31T18:54:18.863Z ### Thinking unavailable — no text recorded Continuation training overfit (the latent fit improved but validation NDCG fell), so eight epochs remains selected. I’m now testing an orthogonal, production-safe feature: recent catalog discovery momentum computed from event counts. It belongs entirely in the offline item tower and can improve temporal freshness without any request-time work or user–item matrix. ### exec · 2026-08-31T18:54:23.079Z { "code": "const r = await tools.write_stdin({session_id:48086,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:54:23.135Z Script completed Wall time 0.0 seconds Output: bias (0.07, 0.06): ndcg=0.012804 bias (0.1, 0.0): ndcg=0.011666 bias (0.1, 0.01): ndcg=0.011992 bias (0.1, 0.03): ndcg=0.012538 bias (0.1, 0.06): ndcg=0.012092 bias (0.15, 0.0): ndcg=0.011728 bias (0.15, 0.01): ndcg=0.011775 bias (0.15, 0.03): ndcg=0.012099 bias (0.15, 0.06): ndcg=0.011700 wrote /app/solution/validation_v3.parquet; coefficients=(0.02, 0.06); validation_ndcg=0.013925 ## Step 127 · agent · 2026-08-31T18:54:26.209Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:56.589Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,subprocess\\nD=Path('/task/data');O=Path('/app/solution'); end=24786800;day=86400\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank'); raw=cat.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(raw)}\\nsr=np.sort(raw); sti=np.array([mp[int(x)] for x in sr]); periods=[3,7,14,30,90]; counts={d:np.zeros(len(raw),np.float64) for d in periods}; allc={d:np.zeros(len(raw),np.float64) for d in periods}\\npf=pq.ParquetFile(D/'train_listens.parquet')\\nfor b in pf.iter_batches(batch_size=1000000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n ts=b.column(0).to_numpy();x=b.column(1).to_numpy();org=b.column(2).to_numpy();ratio=b.column(3).to_numpy(); pos=np.searchsorted(sr,x);ok=pos=end-d*day)&(q>=80);allc[d]+=np.bincount(idx[m],minlength=len(raw));m&=(o==1);counts[d]+=np.bincount(idx[m],minlength=len(raw))\\n# likes recent\\nlikes=pd.read_parquet(D/'train_likes.parquet');likes=likes[likes.item_id.isin(mp) & (likes.is_organic==1)]; like30=np.zeros(len(raw)); ii=likes.item_id.map(mp).to_numpy();m=likes.timestamp.to_numpy()>=end-30*day;like30+=np.bincount(ii[m],minlength=len(raw))\\nfeatures={'org3':np.log1p(counts[3]),'org7':np.log1p(counts[7]),'org14':np.log1p(counts[14]),'org30':np.log1p(counts[30]),'all7':np.log1p(allc[7]),'like30':np.log1p(like30),'mix':np.log1p(counts[14])+0.5*np.log1p(like30)+0.2*np.log1p(allc[7])}\\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'];items=z['item_ids'];packed=z['packed_eligible']\\nassert np.array_equal(items,raw)\\nfor name,s in features.items():\\n order=np.argsort(-s,kind='stable');rows=[]\\n for r,u in enumerate(uids):\\n mask=np.unpackbits(packed[r],count=len(raw),bitorder='little').astype(bool);chosen=order[mask[order]][:100];rows.extend((int(u),int(raw[k]),j+1) for j,k in enumerate(chosen))\\n path=O/f'validation_{name}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\\n out=subprocess.check_output(['/opt/task-venv/bin/python','/task/evaluate_public.py',str(path)],text=True)\\n print(name,out.replace('\\\\n',' '),flush=True)\\nnp.savez_compressed(O/'recent_features_validation.npz',**features)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:55:07.609Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 128 · agent · 2026-08-31T18:55:09.262Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:10.053Z { "cell_id": "59", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:21.055Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 129 · agent · 2026-08-31T18:55:22.895Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:23.711Z { "cell_id": "59", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:26.772Z Script completed Wall time 3.1 seconds Output: org3 { "model": "/app/solution/validation_org3.parquet", "scores": { "quality_ndcg@10": 0.025721, "quality_users": 4263 } } org7 { "model": "/app/solution/validation_org7.parquet", "scores": { "quality_ndcg@10": 0.028131, "quality_users": 4263 } } org14 { "model": "/app/solution/validation_org14.parquet", "scores": { "quality_ndcg@10": 0.022878, "quality_users": 4263 } } org30 { "model": "/app/solution/validation_org30.parquet", "scores": { "quality_ndcg@10": 0.013314, "quality_users": 4263 } } all7 { "model": "/app/solution/validation_all7.parquet", "scores": { "quality_ndcg@10": 0.030107, "quality_users": 4263 } } ## Step 130 · agent · 2026-08-31T18:55:47.362Z ### Thinking unavailable — no text recorded ## Step 131 · agent · 2026-08-31T18:56:00.887Z ### Thinking unavailable — no text recorded Recent item momentum is much stronger than lifetime popularity: a seven-day strong-listen count alone reaches 0.030107 public NDCG, nearly 3× the original baseline. I’m folding cutoff-correct seven-day organic and all-surface momentum into the item tower, then training the personalized model around those offline features. This remains latency-neutral: the features are precomputed once per item, and request-time scoring is still one dot product. ### exec · 2026-08-31T18:56:34.718Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def catalog_info():\\n+def recent_item_features(stage: str, raw_ids: np.ndarray, item_to_index: dict[int,int]):\\n+ \\\"\\\"\\\"Cutoff-correct seven-day item momentum; computed entirely offline.\\\"\\\"\\\"\\n+ end = TRAIN_END if stage == \\\"validation\\\" else VAL_END\\n+ files = [DATA / \\\"train_listens.parquet\\\"] if stage == \\\"validation\\\" else [DATA / \\\"val_listens.parquet\\\"]\\n+ all_count = np.zeros(len(raw_ids), np.float64)\\n+ org_count = np.zeros(len(raw_ids), np.float64)\\n+ sorted_raw = np.sort(raw_ids)\\n+ sorted_to_index = np.array([item_to_index[int(x)] for x in sorted_raw], np.int32)\\n+ for path in files:\\n+ pf = pq.ParquetFile(path)\\n+ for b in pf.iter_batches(batch_size=1_000_000,\\n+ columns=[\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\",\\\"played_ratio_pct\\\"]):\\n+ ts=b.column(0).to_numpy(); raw=b.column(1).to_numpy()\\n+ org=b.column(2).to_numpy(); ratio=b.column(3).to_numpy()\\n+ idx,ok=map_catalog(raw,sorted_raw,sorted_to_index)\\n+ m=ok & (ts >= end-7*DAY) & (ts < end) & (ratio >= 80)\\n+ all_count += np.bincount(idx[m], minlength=len(raw_ids))\\n+ mo=m & (org == 1)\\n+ org_count += np.bincount(idx[mo], minlength=len(raw_ids))\\n+ return np.log1p(all_count).astype(np.float32), np.log1p(org_count).astype(np.float32)\\n+\\n+\\n+def catalog_info(stage: str):\\n@@\\n- feats = np.stack([(pop - pop.mean()) / pop.std(),\\n+ recent_all,recent_org=recent_item_features(stage,raw_ids,item_to_index)\\n+ feats = np.stack([(recent_all-recent_all.mean())/recent_all.std(),\\n+ (recent_org-recent_org.mean())/recent_org.std(),\\n+ (pop - pop.mean()) / pop.std(),\\n (org - org.mean()) / org.std(),\\n (ratio - ratio.mean()) / ratio.std()], 1).astype(np.float32)\\n@@\\n- bias = coeff[0] * feats[:, 0] + coeff[1] * feats[:, 1]\\n+ bias = feats[:, :len(coeff)] @ np.asarray(coeff, np.float32)\\n@@\\n- grid = [(a,b) for a in [0.0,0.01,0.02,0.04,0.07,0.10,0.15]\\n- for b in [0.0,0.01,0.03,0.06]]\\n+ # [recent all-surface strong, recent organic strong, lifetime positive]\\n+ grid = [(a,b,c) for a in [0.02,0.04,0.06,0.10,0.15,0.25]\\n+ for b in [0.0,0.02,0.04,0.06]\\n+ for c in [0.0,0.03]]\\n@@\\n- cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info()\\n+ cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info(args.stage)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:56:34.820Z Script completed Wall time 0.0 seconds Output: {} ## Step 132 · agent · 2026-08-31T18:56:38.710Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:42.973Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v4.parquet --epochs 8 --dim 64 --tune --cache\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:56:44.152Z Script completed Wall time 1.2 seconds Output: SESSION_ID=53441 ## Step 133 · agent · 2026-08-31T18:56:45.640Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:46.969Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:56:57.986Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 134 · agent · 2026-08-31T18:56:59.992Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:00.764Z { "cell_id": "62", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:11.765Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 135 · agent · 2026-08-31T18:57:13.394Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:14.178Z { "cell_id": "62", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:16.991Z Script completed Wall time 2.8 seconds Output: /app/solution/train_ranker.py:422: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) loaded 63587 examples from /app/solution/cache_validation.pt epoch 1/8: loss=5.5225 scale=11.25 sec=12.2 ## Step 136 · agent · 2026-08-31T18:57:18.486Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:19.859Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:57:30.884Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 137 · agent · 2026-08-31T18:57:33.119Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:34.121Z { "cell_id": "63", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:45.124Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T18:57:46.663Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:47.462Z { "cell_id": "63", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:49.890Z Script completed Wall time 2.4 seconds Output: epoch 2/8: loss=5.2636 scale=10.76 sec=11.6 epoch 3/8: loss=5.0220 scale=10.71 sec=11.1 epoch 4/8: loss=4.8108 scale=11.14 sec=11.2 ## Step 139 · agent · 2026-08-31T18:57:52.226Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:53.568Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:58:04.593Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 140 · agent · 2026-08-31T18:58:06.128Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:06.941Z { "cell_id": "64", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:17.944Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 141 · agent · 2026-08-31T18:58:19.646Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:20.393Z { "cell_id": "64", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:23.597Z Script completed Wall time 3.2 seconds Output: epoch 5/8: loss=4.6241 scale=11.88 sec=11.3 epoch 6/8: loss=4.4507 scale=12.74 sec=11.1 epoch 7/8: loss=4.2903 scale=13.77 sec=11.1 ## Step 142 · agent · 2026-08-31T18:58:25.190Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:26.587Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:58:37.618Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 143 · agent · 2026-08-31T18:58:39.225Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:40.148Z { "cell_id": "65", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:51.161Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T18:58:52.952Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:53.733Z { "cell_id": "65", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:56.621Z Script completed Wall time 2.9 seconds Output: epoch 8/8: loss=4.1477 scale=14.76 sec=11.0 bias (0.02, 0.0, 0.0): ndcg=0.012449 bias (0.02, 0.0, 0.03): ndcg=0.018579 bias (0.02, 0.02, 0.0): ndcg=0.019595 bias (0.02, 0.02, 0.03): ndcg=0.023754 bias (0.02, 0.04, 0.0): ndcg=0.025810 bias (0.02, 0.04, 0.03): ndcg=0.027449 bias (0.02, 0.06, 0.0): ndcg=0.028744 bias (0.02, 0.06, 0.03): ndcg=0.029683 bias (0.04, 0.0, 0.0): ndcg=0.019006 bias (0.04, 0.0, 0.03): ndcg=0.022419 bias (0.04, 0.02, 0.0): ndcg=0.025235 bias (0.04, 0.02, 0.03): ndcg=0.026779 bias (0.04, 0.04, 0.0): ndcg=0.028912 bias (0.04, 0.04, 0.03): ndcg=0.029780 bias (0.04, 0.06, 0.0): ndcg=0.030835 bias (0.04, 0.06, 0.03): ndcg=0.030733 bias (0.06, 0.0, 0.0): ndcg=0.024298 bias (0.06, 0.0, 0.03): ndcg=0.026187 bias (0.06, 0.02, 0.0): ndcg=0.028527 bias (0.06, 0.02, 0.03): ndcg=0.029188 ## Step 145 · agent · 2026-08-31T18:58:59.535Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:00.948Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:59:11.985Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 146 · agent · 2026-08-31T18:59:13.693Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:14.473Z { "cell_id": "66", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:25.484Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 147 · agent · 2026-08-31T18:59:27.289Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:28.070Z { "cell_id": "66", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:30.993Z Script completed Wall time 2.9 seconds Output: bias (0.06, 0.04, 0.0): ndcg=0.031096 bias (0.06, 0.04, 0.03): ndcg=0.030841 bias (0.06, 0.06, 0.0): ndcg=0.032421 bias (0.06, 0.06, 0.03): ndcg=0.031884 bias (0.1, 0.0, 0.0): ndcg=0.030256 bias (0.1, 0.0, 0.03): ndcg=0.030034 bias (0.1, 0.02, 0.0): ndcg=0.032876 bias (0.1, 0.02, 0.03): ndcg=0.031677 bias (0.1, 0.04, 0.0): ndcg=0.033616 bias (0.1, 0.04, 0.03): ndcg=0.032646 bias (0.1, 0.06, 0.0): ndcg=0.034451 bias (0.1, 0.06, 0.03): ndcg=0.033447 bias (0.15, 0.0, 0.0): ndcg=0.034157 bias (0.15, 0.0, 0.03): ndcg=0.032445 bias (0.15, 0.02, 0.0): ndcg=0.034944 bias (0.15, 0.02, 0.03): ndcg=0.033349 bias (0.15, 0.04, 0.0): ndcg=0.034905 bias (0.15, 0.04, 0.03): ndcg=0.033986 bias (0.15, 0.06, 0.0): ndcg=0.035645 bias (0.15, 0.06, 0.03): ndcg=0.034372 bias (0.25, 0.0, 0.0): ndcg=0.036656 bias (0.25, 0.0, 0.03): ndcg=0.034691 bias (0.25, 0.02, 0.0): ndcg=0.036834 bias (0.25, 0.02, 0.03): ndcg=0.035042 bias (0.25, 0.04, 0.0): ndcg=0.036748 bias (0.25, 0.04, 0.03): ndcg=0.035130 ## Step 148 · agent · 2026-08-31T18:59:32.618Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:34.022Z { "code": "const r = await tools.write_stdin({session_id:53441,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:59:36.011Z Script completed Wall time 2.0 seconds Output: bias (0.25, 0.06, 0.0): ndcg=0.036501 bias (0.25, 0.06, 0.03): ndcg=0.035404 wrote /app/solution/validation_v4.parquet; coefficients=(0.25, 0.02, 0.0); validation_ndcg=0.036834 ## Step 149 · agent · 2026-08-31T18:59:52.470Z ### Thinking unavailable — no text recorded The freshness-aware two-tower reaches 0.036834 on the supplied evaluator—over 3.4× lifetime popularity and 22% above the recent-momentum-only baseline. I’ve selected the eight-epoch model with retrieval coefficients `(0.25, 0.02, 0.0)`. I’m writing a valid test-stage momentum fallback first, then refitting from scratch on train + validation and replacing it with the final personalized ranking. ### exec · 2026-08-31T19:00:12.458Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_v4.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/app/solution')\\nimport numpy as np,pandas as pd\\nfrom train_ranker import catalog_info,DATA\\ncat,raw,mp,sr,sti,feats=catalog_info('final')\\norder=np.argsort(-feats[:,0],kind='stable')\\nwith np.load(DATA/'test_eligible_candidates.npz') as z:\\n uids=z['uids']; packed=z['packed_eligible']; items=z['item_ids']\\nrows=[]\\nfor r,u in enumerate(uids):\\n m=np.unpackbits(packed[r],count=len(raw),bitorder='little').astype(bool)\\n top=order[m[order]][:100]\\n rows.extend((int(u),int(raw[k]),j+1) for j,k in enumerate(top))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/submission.parquet',index=False)\\nprint('fallback rows',len(rows))\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data');f=pd.read_parquet('/app/solution/submission.parquet');t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));n=validate_ranking(f,t);c=CandidateSet(D/'test_eligible_candidates.npz');print('valid',len(n),all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None)))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:00:23.480Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 150 · agent · 2026-08-31T19:00:25.279Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:26.087Z { "cell_id": "68", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:00:26.220Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation_v4.parquet", "scores": { "quality_ndcg@10": 0.036834, "quality_users": 4263 } } fallback rows 845600 valid 845600 True ## Step 151 · agent · 2026-08-31T19:00:28.107Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:31.937Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --epochs 8 --dim 64 --coefficients 0.25,0.02,0.0 --cache\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:00:33.124Z Script completed Wall time 1.2 seconds Output: SESSION_ID=73431 ## Step 152 · agent · 2026-08-31T19:00:35.174Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:36.547Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:00:47.569Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 153 · agent · 2026-08-31T19:00:49.309Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:50.085Z { "cell_id": "70", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:01.087Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 154 · agent · 2026-08-31T19:01:02.791Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:03.580Z { "cell_id": "70", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:06.573Z Script completed Wall time 3.0 seconds Output: histories 1000, examples 6919 ## Step 155 · agent · 2026-08-31T19:01:08.197Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:09.549Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:01:20.568Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 156 · agent · 2026-08-31T19:01:22.233Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:23.034Z { "cell_id": "71", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:34.037Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 157 · agent · 2026-08-31T19:01:35.743Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:36.530Z { "cell_id": "71", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:39.571Z Script completed Wall time 3.0 seconds Output: histories 2000, examples 14046 histories 3000, examples 21022 ## Step 158 · agent · 2026-08-31T19:01:41.317Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:42.668Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:01:53.686Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 159 · agent · 2026-08-31T19:01:55.355Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:56.099Z { "cell_id": "72", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:07.104Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 160 · agent · 2026-08-31T19:02:08.806Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:09.620Z { "cell_id": "72", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:12.690Z Script completed Wall time 3.1 seconds Output: histories 4000, examples 28244 histories 5000, examples 35374 ## Step 161 · agent · 2026-08-31T19:02:14.159Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:15.514Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:02:26.536Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 162 · agent · 2026-08-31T19:02:28.202Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:28.990Z { "cell_id": "73", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:39.992Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 163 · agent · 2026-08-31T19:02:41.607Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:42.358Z { "cell_id": "73", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:45.539Z Script completed Wall time 3.2 seconds Output: histories 6000, examples 42105 histories 7000, examples 49115 ## Step 164 · agent · 2026-08-31T19:02:47.906Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:49.305Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:03:00.322Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 165 · agent · 2026-08-31T19:03:03.367Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:04.180Z { "cell_id": "74", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:15.182Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 166 · agent · 2026-08-31T19:03:16.782Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:17.625Z { "cell_id": "74", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:19.327Z Script completed Wall time 1.7 seconds Output: histories 8000, examples 56243 histories 9000, examples 63348 ## Step 167 · agent · 2026-08-31T19:03:20.836Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:22.185Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:03:33.215Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 168 · agent · 2026-08-31T19:03:34.825Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:35.614Z { "cell_id": "75", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:46.616Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 169 · agent · 2026-08-31T19:03:48.415Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:49.179Z { "cell_id": "75", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:52.219Z Script completed Wall time 3.0 seconds Output: /app/solution/train_ranker.py:422: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) built 64912 rolling examples and 8456 inference contexts epoch 1/8: loss=5.5153 scale=11.21 sec=11.2 epoch 2/8: loss=5.2576 scale=10.66 sec=11.3 ## Step 170 · agent · 2026-08-31T19:03:53.531Z ### Thinking unavailable — no text recorded Final refit now includes the entire observed validation window as training history/outcomes and has produced 64,912 rolling examples. Optimization is proceeding normally; the valid momentum fallback remains untouched until the personalized file is fully written and verified. ### exec · 2026-08-31T19:03:56.955Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:04:07.979Z Script running with cell ID 76 Wall time 11.0 seconds Output: ## Step 171 · agent · 2026-08-31T19:04:09.623Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:10.370Z { "cell_id": "76", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:04:21.371Z Script running with cell ID 76 Wall time 11.0 seconds Output: ## Step 172 · agent · 2026-08-31T19:04:23.021Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:23.820Z { "cell_id": "76", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:04:26.983Z Script completed Wall time 3.2 seconds Output: epoch 3/8: loss=5.0133 scale=10.64 sec=11.1 epoch 4/8: loss=4.8138 scale=11.11 sec=11.2 epoch 5/8: loss=4.6385 scale=11.71 sec=11.2 ## Step 173 · agent · 2026-08-31T19:04:28.560Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:29.922Z { "code": "const r = await tools.write_stdin({session_id:73431,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:04:40.949Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 174 · agent · 2026-08-31T19:04:44.159Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:44.964Z { "cell_id": "77", "yield_time_ms": 10000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:04:53.020Z Script completed Wall time 8.1 seconds Output: epoch 6/8: loss=4.4644 scale=12.60 sec=11.1 epoch 7/8: loss=4.3126 scale=13.67 sec=11.3 epoch 8/8: loss=4.1699 scale=14.73 sec=11.1 wrote /app/solution/submission.parquet; coefficients=(0.25, 0.02, 0.0); validation_ndcg=0.000000 ## Step 175 · agent · 2026-08-31T19:05:12.595Z ### Thinking unavailable — no text recorded ## Step 176 · agent · 2026-08-31T19:05:13.779Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:35.061Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower ranker\\n+\\n+`submission.parquet` is the final test ranking (845,600 rows: 100 eligible,\\n+distinct items for each of 8,456 users). The selected public validation file is\\n+`validation.parquet` and scores **0.036834 graded NDCG@10** on 4,263 scored\\n+users with the supplied evaluator.\\n+\\n+## Model\\n+\\n+The model in `train_ranker.py` is an exact two-tower retrieval model. The user\\n+tower uses no user-ID embedding. It pools four interaction-history channels\\n+(recent positive listens, long-term positive taste, active likes, and active\\n+dislikes) through a trainable history-item embedding table, then applies a\\n+LayerNorm/GELU MLP to produce a normalized 64-dimensional vector. The item\\n+tower applies a separate trainable catalog embedding and LayerNorm/GELU MLP to\\n+the item ID and cutoff-correct offline features. Three additional item-only\\n+bias coordinates encode seven-day all-surface momentum, seven-day organic\\n+momentum, and lifetime popularity; the matching user coordinates are constants.\\n+Thus both tower outputs have dimension 67 and the complete score—including the\\n+selected freshness adjustment—is exactly one inner product. Catalog vectors\\n+can be computed offline and placed directly in a MIPS index.\\n+\\n+Training streams the Parquet log by user and never constructs or decomposes a\\n+global user-item matrix. It forms 18 rolling temporal next-discovery tasks,\\n+where targets are previously unseen organic 80%-complete listens or organic\\n+likes in the following seven days. The loss is in-batch sampled softmax with\\n+duplicate positives masked, optimized by AdamW for eight CPU epochs. Validation\\n+uses train history only. The final model is refit on train plus validation, and\\n+uses the supplied cutoff-specific preference state and eligibility mask.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app`:\\n+\\n+```sh\\n+/opt/task-venv/bin/python solution/train_ranker.py \\\\\\n+ --stage final \\\\\\n+ --output solution/submission.parquet \\\\\\n+ --epochs 8 --dim 64 \\\\\\n+ --coefficients 0.25,0.02,0.0 \\\\\\n+ --cache\\n+```\\n+\\n+Public validation check:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\\n+```\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:05:35.149Z Script completed Wall time 0.0 seconds Output: {} ## Step 177 · agent · 2026-08-31T19:05:41.546Z ### Thinking unavailable — no text recorded The final personalized submission has been written. I’m doing the last independent contract check now—exact user set, 100 ranks per user, no duplicates, test eligibility for all 845,600 rows—then I’ll align the canonical validation file with the selected 0.036834 run and report the artifact paths. ### exec · 2026-08-31T19:06:00.492Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/validation_v4.parquet /app/solution/validation.parquet\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys,json\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,torch\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data');S=Path('/app/solution')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=pd.read_parquet(S/'submission.parquet'); n=validate_ranking(f,targets)\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nelig=all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\\nck=torch.load(S/'model_final.pt',weights_only=False)\\nprint(json.dumps({'rows':len(n),'users':n.uid.nunique(),'min_rank':int(n['rank'].min()),'max_rank':int(n['rank'].max()),'duplicate_pairs':int(n.duplicated(['uid','item_id']).sum()),'all_test_eligible':elig,'model_stage':ck['stage'],'coefficients':ck['coefficients'],'checkpoint_bytes':(S/'model_final.pt').stat().st_size,'submission_bytes':(S/'submission.parquet').stat().st_size},indent=2))\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/model_final.pt /app/solution/train_ranker.py /app/solution/README.md /app/solution/metrics_final.json\\ncat /app/solution/metrics_final.json\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:06:09.208Z Script completed Wall time 8.7 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.036834, "quality_users": 4263 } } { "rows": 845600, "users": 8456, "min_rank": 1, "max_rank": 100, "duplicate_pairs": 0, "all_test_eligible": true, "model_stage": "final", "coefficients": [ 0.25, 0.02, 0.0 ], "checkpoint_bytes": 16207052, "submission_bytes": 1394896 } -rw-r--r-- 1 agent agent 2.1K Aug 31 19:05 /app/solution/README.md -rw-r--r-- 1 agent agent 131 Aug 31 19:04 /app/solution/metrics_final.json -rw-r--r-- 1 agent agent 16M Aug 31 19:04 /app/solution/model_final.pt -rw-r--r-- 1 agent agent 1.4M Aug 31 19:04 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 26K Aug 31 18:56 /app/solution/train_ranker.py -rw-r--r-- 1 agent agent 1.3M Aug 31 19:06 /app/solution/validation.parquet { "coefficients": [ 0.25, 0.02, 0.0 ], "validation_ndcg": 0.0, "examples": 64912, "epochs": 8, "dim": 64 } ## Step 178 · agent · 2026-08-31T19:06:18.827Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:23.426Z { "code": "const p = await tools.update_plan({explanation:\"Implemented, tuned, refit, and independently validated all deliverables.\",plan:[\n {step:\"Inspect data, schemas, environment, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-feature two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train validation models, evaluate, and tune\",status:\"completed\"},\n {step:\"Refit on train+validation and write a valid test submission\",status:\"completed\"},\n {step:\"Verify artifacts and document the solution\",status:\"completed\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T19:06:23.576Z Script completed Wall time 0.0 seconds Output: {} ## Step 179 · agent · 2026-08-31T19:06:25.917Z ### Thinking unavailable — no text recorded Implemented and verified the complete CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/inference code: [train_ranker.py](/app/solution/train_ranker.py) - Documentation: [README.md](/app/solution/README.md) - Final checkpoint: [model_final.pt](/app/solution/model_final.pt) - Selected validation ranking: [validation.parquet](/app/solution/validation.parquet) Results: - Public NDCG@10: **0.036834** - 845,600 submission rows - Exactly 100 distinct items for all 8,456 users - Ranks 1–100 with no ties - All submitted items test-eligible - Final model refit on train + validation - Exact 67-dimensional user/item vectors with dot-product scoring and offline-precomputable item embeddings