yambda-discovery-ranking-two-tower-base / run-006-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:09.557Z 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 · 2026-08-31T18:25:12.971Z ## 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 · 2026-08-31T18:25:12.971Z 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 · 2026-08-31T18:25:12.971Z 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 · 2026-08-31T18:25:12.971Z 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": "01a05911-6dad-7812-83dc-c234cb825814", "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 · 2026-08-31T18:25:12.980Z # 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 · 2026-08-31T18:25:16.311Z ### Thinking unavailable — no text recorded I’ll inspect the data shapes, schemas, eligibility density, and available CPU resources first. Then I’ll build a leakage-safe validation pipeline, benchmark a few scalable two-tower training variants, preserve the best valid ranking, and refit on train plus validation for the final submission. ### exec · 2026-08-31T18:25:27.244Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data, schemas, sizes, and evaluation helpers\",status:\"in_progress\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"pending\"},\n {step:\"Train and compare validation variants; save best valid ranking\",status:\"pending\"},\n {step:\"Refit on train + validation and generate test submission\",status:\"pending\"},\n {step:\"Validate artifacts and write concise README\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt && lscpu | sed -n '1,24p' && free -h\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:25:27.322Z Script completed Wall time 0.1 seconds Output: /app candidate_catalog.parquet 349352 bytes target_users.parquet 84127 bytes test_eligible_candidates.npz 7736341 bytes test_preference_state.parquet 1092329 bytes train_dislikes.parquet 857213 bytes train_likes.parquet 5374652 bytes train_listens.parquet 319183255 bytes train_undislikes.parquet 155988 bytes train_unlikes.parquet 1855885 bytes val_dislikes.parquet 36571 bytes val_likes.parquet 298484 bytes val_listens.parquet 9947609 bytes val_undislikes.parquet 7845 bytes val_unlikes.parquet 72536 bytes validation_eligible_candidates.npz 7573821 bytes validation_preference_state.parquet 1061761 bytes """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 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R13 Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 1 BogoMIPS: 5299.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 128 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 2 MiB (4 instances) L3 cache: 16 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 /bin/sh: 1: free: not found ## Step 8 · agent · 2026-08-31T18:25:30.155Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:38.800Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n m=pq.ParquetFile(p).metadata\\n df=pd.read_parquet(p).head(3)\\n print(p.name, 'rows',m.num_rows,'cols',list(df.columns), 'dtypes',dict(df.dtypes))\\n print(df.to_dict('records')[:2])\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'eligible_mean',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).mean())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:25:38.897Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-08-31T18:25:41.186Z ### Thinking unavailable — no text recorded The shell’s default `python` is not the benchmark environment, so I’m locating the pinned interpreter before touching the implementation. The dataset itself is intact; this is just an environment-path mismatch. ### exec · 2026-08-31T18:25:47.068Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\nfind /opt /usr/local /task -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -30\\nls -la /task | sed -n '1,120p'\\nenv | sort | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:25:47.127Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:09 data -r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05911-6d99-7d43-bf68-29aef44477b3 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=aec47db0eab4 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 · 2026-08-31T18:25:49.621Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:53.712Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list --format=columns | sed -n '1,120p'\\nfind / -type d \\\\( -name 'site-packages' -o -name 'dist-packages' \\\\) 2>/dev/null | head -50\\nfind / -type f -path '*/bin/python*' 2>/dev/null | head -50\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:25:54.884Z Script completed Wall time 1.2 seconds Output: Package Version ------- ------- pip 25.0.1 /opt/task-venv/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages /usr/local/bin/python3.12-config /usr/local/bin/python3.12 ## Step 11 · agent · 2026-08-31T18:25:56.786Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:00.519Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,100p'\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas,torch,pyarrow,numpy\\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:04.537Z Script completed Wall time 4.0 seconds Output: total 124 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . dr-xr-xr-x 1 root root 4096 Aug 31 18:22 .. -r--r--r-- 1 root root 9033 Aug 31 18:22 Activate.ps1 -r--r--r-- 1 root root 2158 Aug 31 18:22 activate -r--r--r-- 1 root root 920 Aug 31 18:22 activate.csh -r--r--r-- 1 root root 2195 Aug 31 18:22 activate.fish -r-xr-xr-x 1 root root 302 Aug 31 18:22 chardetect -r-xr-xr-x 1 root root 304 Aug 31 18:22 dotenv -r-xr-xr-x 1 root root 308 Aug 31 18:22 f2py -r-xr-xr-x 1 root root 299 Aug 31 18:22 idna -r-xr-xr-x 1 root root 297 Aug 31 18:22 isympy -r-xr-xr-x 1 root root 305 Aug 31 18:22 jsonschema -r-xr-xr-x 1 root root 311 Aug 31 18:22 jupyter -r-xr-xr-x 1 root root 311 Aug 31 18:22 jupyter-migrate -r-xr-xr-x 1 root root 316 Aug 31 18:22 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Aug 31 18:22 jupyter-trust -r-xr-xr-x 1 root root 311 Aug 31 18:22 jupytext -r-xr-xr-x 1 root root 315 Aug 31 18:22 jupytext-config -r-xr-xr-x 1 root root 301 Aug 31 18:22 kaggle -r-xr-xr-x 1 root root 312 Aug 31 18:22 markdown-it -r-xr-xr-x 1 root root 325 Aug 31 18:22 normalizer -r-xr-xr-x 1 root root 308 Aug 31 18:22 numpy-config -r-xr-xr-x 1 root root 313 Aug 31 18:22 pip -r-xr-xr-x 1 root root 313 Aug 31 18:22 pip3 -r-xr-xr-x 1 root root 313 Aug 31 18:22 pip3.12 lrwxrwxrwx 1 root root 21 Aug 31 18:22 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 18:22 python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 18:22 python3.12 -> python -r-xr-xr-x 1 root root 307 Aug 31 18:22 slugify -r-xr-xr-x 1 root root 321 Aug 31 18:22 torchfrtrace -r-xr-xr-x 1 root root 312 Aug 31 18:22 torchrun -r-xr-xr-x 1 root root 299 Aug 31 18:22 tqdm 2.3.3 2.8.0+cpu 18.1.0 2.2.6 ## Step 12 · agent · 2026-08-31T18:26:06.941Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:16.815Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n m=pq.ParquetFile(p).metadata\\n df=pd.read_parquet(p).head(2)\\n print('\\\\n',p.name, 'rows',m.num_rows,'cols',list(df.columns), 'dtypes',{k:str(v) for k,v in df.dtypes.items()})\\n print(df.to_dict('records'))\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('\\\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'eligible',np.percentile(mask.sum(1),[0,25,50,75,100]).tolist())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:19.958Z Script completed Wall time 3.1 seconds Output: candidate_catalog.parquet rows 30000 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] dtypes {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'} [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}] target_users.parquet rows 8456 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] dtypes {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'} [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}] test_preference_state.parquet rows 484776 cols ['uid', 'item_id', 'state'] dtypes {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] train_dislikes.parquet rows 101075 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}] train_likes.parquet rows 814831 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}] train_listens.parquet rows 43484159 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}] train_undislikes.parquet rows 19960 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}] train_unlikes.parquet rows 292665 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}] val_dislikes.parquet rows 3563 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}] val_likes.parquet rows 37795 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}] val_listens.parquet rows 1501697 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} [{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}] val_undislikes.parquet rows 606 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}] val_unlikes.parquet rows 9346 cols ['uid', 'timestamp', 'item_id', 'is_organic'] dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}] validation_preference_state.parquet rows 469124 cols ['uid', 'item_id', 'state'] dtypes {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible [24552.0, 28657.0, 29220.0, 29619.25, 30000.0] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible [24657.0, 28682.75, 29242.0, 29644.0, 30000.0] ## Step 13 · agent · 2026-08-31T18:26:36.595Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:42.924Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,12p' /proc/meminfo\\n/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nprint('rowgroups',p.num_row_groups)\\nfor i in [0,1,p.num_row_groups-1]:\\n rg=p.metadata.row_group(i)\\n print(i,rg.num_rows,[(rg.column(j).path_in_schema,rg.column(j).statistics.min,rg.column(j).statistics.max) for j in range(3)])\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:43.422Z Script completed Wall time 0.5 seconds Output: MemTotal: 15988980 kB MemFree: 2066596 kB MemAvailable: 14973136 kB Buffers: 227940 kB Cached: 12387292 kB SwapCached: 0 kB Active: 514536 kB Inactive: 12386704 kB Active(anon): 312460 kB Inactive(anon): 0 kB Active(file): 202076 kB Inactive(file): 12386704 kB rowgroups 42 0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541)] 1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541)] 41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508)] ## Step 14 · agent · 2026-08-31T18:27:03.583Z ### Thinking unavailable — no text recorded ## Step 15 · agent · 2026-08-31T18:27:06.792Z ### Thinking unavailable — no text recorded The scale is one million users and 43.5 million training listens, while only 30,000 items are retrieval candidates. I’m going to keep histories as sparse per-user event lists and train on sampled history→future pairs—no global interaction matrix. A popularity ranking gives us a hard baseline before spending CPU on representation learning. ### exec · 2026-08-31T18:27:32.062Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build a cutoff-valid popularity baseline and summarize public labels.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def main() -> None:\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].copy()\\n+ items = z[\\\"item_ids\\\"].copy()\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ rows = []\\n+ for row, uid in enumerate(uids):\\n+ mask = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ top = items[mask][:100]\\n+ rows.append(pd.DataFrame({\\\"uid\\\": int(uid), \\\"item_id\\\": top, \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / \\\"validation_popularity.parquet\\\", index=False)\\n+\\n+ target_set = set(targets.uid.astype(int))\\n+ strong = pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ strong = strong[(strong.uid.isin(target_set)) & (strong.is_organic == 1) & (strong.played_ratio_pct >= 80)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n+ likes = pd.read_parquet(DATA / \\\"val_likes.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ likes = likes[(likes.uid.isin(target_set)) & (likes.is_organic == 1)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n+ print(\\\"raw strong\\\", len(strong), \\\"users\\\", strong.uid.nunique(), \\\"likes\\\", len(likes), \\\"users\\\", likes.uid.nunique())\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(a);\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:39.452Z Script completed Wall time 7.4 seconds Output: {} raw strong 255608 users 6325 likes 17195 users 2714 { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 16 · agent · 2026-08-31T18:27:50.688Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:03.016Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.dataset as ds, pyarrow as pa, pandas as pd, numpy as np, time\\nD='/task/data/'\\ncat=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy()\\ntgt=pd.read_parquet(D+'target_users.parquet').uid.to_numpy()\\nfor name in ['train_listens','train_likes']:\\n d=ds.dataset(D+name+'.parquet',format='parquet')\\n filt=ds.field('item_id').isin(pa.array(cat))\\n t=time.time(); tab=d.to_table(columns=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if 'listens' in name else []),filter=filt)\\n print(name,'catalog rows',tab.num_rows,'secs',time.time()-t)\\n df=tab.to_pandas()\\n print('users',df.uid.nunique(),'target rows',df.uid.isin(tgt).sum(),'organic',int(df.is_organic.sum()))\\n if 'played_ratio_pct' in df: print('>=50',int((df.played_ratio_pct>=50).sum()),'>=80 organic',int(((df.played_ratio_pct>=80)&(df.is_organic==1)).sum()))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:28:05.536Z Script completed Wall time 2.5 seconds Output: train_listens catalog rows 31249613 secs 0.49226999282836914 users 9217 target rows 31062540 organic 15619474 >=50 20056329 >=80 organic 8699160 train_likes catalog rows 554824 secs 0.015738248825073242 users 7901 target rows 472889 organic 301527 ## Step 17 · agent · 2026-08-31T18:28:25.445Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-08-31T18:28:44.078Z ### Thinking unavailable — no text recorded ## Step 19 · agent · 2026-08-31T18:29:02.724Z ### Thinking unavailable — no text recorded ## Step 20 · agent · 2026-08-31T18:29:21.505Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T18:29:34.466Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:19.535Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/global_baselines.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Compare leakage-safe global priors derived only from training events.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow as pa\\n+import pyarrow.dataset as ds\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+\\n+\\n+def write_ranking(name: str, order: np.ndarray) -> None:\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ uids, items, packed = z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+ col = np.empty(int(items.max()) + 1, dtype=np.int32)\\n+ col.fill(-1)\\n+ col[items] = np.arange(len(items), dtype=np.int32)\\n+ order_cols = col[order]\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ eligible = np.unpackbits(packed[r], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ top_cols = order_cols[eligible[order_cols]][:100]\\n+ rows.append(pd.DataFrame({\\\"uid\\\": int(uid), \\\"item_id\\\": items[top_cols], \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / f\\\"validation_{name}.parquet\\\", index=False)\\n+\\n+\\n+def main() -> None:\\n+ cat = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ items = cat.item_id.to_numpy()\\n+ max_id = int(items.max())\\n+ idx = np.full(max_id + 1, -1, dtype=np.int32)\\n+ idx[items] = np.arange(len(items), dtype=np.int32)\\n+ filt = ds.field(\\\"item_id\\\").isin(pa.array(items))\\n+ tab = ds.dataset(DATA / \\\"train_listens.parquet\\\", format=\\\"parquet\\\").to_table(\\n+ columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"], filter=filt\\n+ )\\n+ ts = tab[\\\"timestamp\\\"].to_numpy(); ii = idx[tab[\\\"item_id\\\"].to_numpy()]\\n+ org = tab[\\\"is_organic\\\"].to_numpy().astype(bool); ratio = tab[\\\"played_ratio_pct\\\"].to_numpy()\\n+ strong = org & (ratio >= 80)\\n+ variants = {\\n+ \\\"organic_all\\\": np.bincount(ii[strong], minlength=len(items)).astype(float),\\n+ \\\"organic_30d\\\": np.bincount(ii[strong], weights=np.exp(-(CUTOFF-ts[strong])/(30*86400)), minlength=len(items)),\\n+ \\\"organic_90d\\\": np.bincount(ii[strong], weights=np.exp(-(CUTOFF-ts[strong])/(90*86400)), minlength=len(items)),\\n+ \\\"positive_30d\\\": np.bincount(ii[ratio>=50], weights=np.exp(-(CUTOFF-ts[ratio>=50])/(30*86400)), minlength=len(items)),\\n+ }\\n+ del tab, ts, ii, org, ratio, strong\\n+ likes = ds.dataset(DATA / \\\"train_likes.parquet\\\", format=\\\"parquet\\\").to_table(\\n+ columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"], filter=filt\\n+ )\\n+ ts=likes[\\\"timestamp\\\"].to_numpy(); ii=idx[likes[\\\"item_id\\\"].to_numpy()]; org=likes[\\\"is_organic\\\"].to_numpy().astype(bool)\\n+ variants[\\\"likes_all\\\"] = np.bincount(ii[org], minlength=len(items)).astype(float)\\n+ variants[\\\"likes_90d\\\"] = np.bincount(ii[org], weights=np.exp(-(CUTOFF-ts[org])/(90*86400)), minlength=len(items))\\n+ for name, score in variants.items():\\n+ order = items[np.argsort(-score, kind=\\\"stable\\\")]\\n+ write_ranking(name, order)\\n+ print(name, float(score.max()), int(np.count_nonzero(score)))\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/global_baselines.py\\nfor f in /app/solution/validation_organic_all.parquet /app/solution/validation_organic_30d.parquet /app/solution/validation_organic_90d.parquet /app/solution/validation_positive_30d.parquet /app/solution/validation_likes_all.parquet /app/solution/validation_likes_90d.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:8000});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(a); text(r.output); }\n" } ### Tool output · 2026-08-31T18:30:30.556Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 22 · agent · 2026-08-31T18:30:32.681Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:30:33.469Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T18:30:49.722Z Script completed Wall time 16.3 seconds Output: {"session_id":63356,"output":"/app/solution/global_baselines.py:45: RuntimeWarning: overflow encountered in exp\n \"organic_30d\": np.bincount(ii[strong], weights=np.exp(-(CUTOFF-ts[strong])/(30*86400)), minlength=len(items)),\n/app/solution/global_baselines.py:47: RuntimeWarning: overflow encountered in exp\n \"positive_30d\": np.bincount(ii[ratio>=50], weights=np.exp(-(CUTOFF-ts[ratio>=50])/(30*86400)), minlength=len(items)),\norganic_all 19362.0 29974\norganic_30d inf 29974\norganic_90d 6.761620250105525e+243 29974\npositive_30d inf 30000\nlikes_all 565.0 27464\nlikes_90d 2.022302271801164e+242 27464\n{ \"model\": \"/app/solution/validation_organic_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010551, \"quality_users\": 4263 } } \n{ \"model\": \"/app/solution/validation_organic_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n"} ## Step 23 · agent · 2026-08-31T18:30:57.050Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-08-31T18:30:57.602Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:10.858Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/global_baselines.py\\n@@\\n- ts = tab[\\\"timestamp\\\"].to_numpy(); ii = idx[tab[\\\"item_id\\\"].to_numpy()]\\n+ ts = tab[\\\"timestamp\\\"].to_numpy().astype(np.float64); ii = idx[tab[\\\"item_id\\\"].to_numpy()]\\n@@\\n- ts=likes[\\\"timestamp\\\"].to_numpy(); ii=idx[likes[\\\"item_id\\\"].to_numpy()]; org=likes[\\\"is_organic\\\"].to_numpy().astype(bool)\\n+ ts=likes[\\\"timestamp\\\"].to_numpy().astype(np.float64); ii=idx[likes[\\\"item_id\\\"].to_numpy()]; org=likes[\\\"is_organic\\\"].to_numpy().astype(bool)\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/global_baselines.py >/app/solution/global_baselines.log\\nfor n in organic_all organic_30d organic_90d positive_30d likes_all likes_90d; do echo -n \\\"$n \\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_${n}.parquet\\\" | grep -E 'quality_ndcg|quality_users'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(a); text(r.output); }\n" } ### Tool output · 2026-08-31T18:31:21.881Z Script running with cell ID 11 Wall time 11.0 seconds Output: ## Step 25 · agent · 2026-08-31T18:31:24.777Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:31:25.629Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:31:41.042Z Script completed Wall time 15.4 seconds Output: {"session_id":44323,"output":"organic_all \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\norganic_30d \"quality_ndcg@10\": 0.016254,\n \"quality_users\": 4263\norganic_90d \"quality_ndcg@10\": 0.015031,\n \"quality_users\": 4263\npositive_30d "} ## Step 26 · agent · 2026-08-31T18:31:45.718Z ### Thinking unavailable — no text recorded The best global prior so far is recent organic completion popularity at 0.016254 NDCG@10, about 52% above catalog popularity. That confirms both the organic target and recency matter. I’m now using that prior inside the item tower and testing whether history-conditioned embeddings add lift. ### exec · 2026-08-31T18:31:55.736Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nD=Path('/task/data'); O=Path('/app/solution')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); elig=CandidateSet(D/'validation_eligible_candidates.npz'); gold=validation_truth(elig,targets)\\nfor n in ['organic_all','organic_30d','organic_90d','positive_30d','likes_all','likes_90d']:\\n print(n,score(load_submission(O/f'validation_{n}.parquet',elig,targets),gold))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:32:05.319Z Script completed Wall time 9.6 seconds Output: organic_all {'quality_ndcg@10': 0.010551, 'quality_users': 4263} organic_30d {'quality_ndcg@10': 0.016254, 'quality_users': 4263} organic_90d {'quality_ndcg@10': 0.015031, 'quality_users': 4263} positive_30d {'quality_ndcg@10': 0.018115, 'quality_users': 4263} likes_all {'quality_ndcg@10': 0.012054, 'quality_users': 4263} likes_90d {'quality_ndcg@10': 0.016093, 'quality_users': 4263} ## Step 27 · agent · 2026-08-31T18:32:24.889Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:32:43.613Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:33:02.184Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:33:20.848Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:33:39.519Z ### Thinking unavailable — no text recorded ## Step 32 · agent · 2026-08-31T18:33:50.802Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:26.688Z { "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-based PyTorch two-tower ranker.\\n+\\n+The script never constructs a user-item matrix. It aggregates an event stream\\n+to bounded per-user histories, trains on sampled history/positive pairs, and\\n+scores independently precomputed item vectors with one user-vector dot product.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import random\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow as pa\\n+import pyarrow.dataset as ds\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+DAY = 86_400\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"Encode a bounded interaction history; there is deliberately no user ID.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.history_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.feature_gate = nn.Sequential(nn.Linear(3, 16), nn.GELU(), nn.Linear(16, 1))\\n+ self.network = nn.Sequential(\\n+ nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+\\n+ def forward(self, items: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ valid = items != self.history_embedding.padding_idx\\n+ gate = self.feature_gate(features).squeeze(-1).clamp(-6, 6)\\n+ weight = torch.exp(gate) * valid\\n+ pooled = (self.history_embedding(items) * weight.unsqueeze(-1)).sum(1)\\n+ pooled = pooled / weight.sum(1, keepdim=True).clamp_min(1e-6)\\n+ return F.normalize(self.network(pooled) + pooled, dim=-1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently precomputable candidate encoder.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.item_embedding = nn.Embedding(n_items, dim)\\n+ self.network = nn.Sequential(\\n+ nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+\\n+ def forward(self, items: torch.Tensor) -> torch.Tensor:\\n+ base = self.item_embedding(items)\\n+ return F.normalize(self.network(base) + base, dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, dim)\\n+ self.logit_scale = nn.Parameter(torch.tensor(np.log(10.0), dtype=torch.float32))\\n+\\n+ def forward(self, histories: torch.Tensor, features: torch.Tensor, positives: torch.Tensor) -> torch.Tensor:\\n+ users = self.user_tower(histories, features)\\n+ items = self.item_tower(positives)\\n+ return users @ items.T * self.logit_scale.exp().clamp(max=50)\\n+\\n+\\n+@dataclass\\n+class Prepared:\\n+ uids: np.ndarray\\n+ history_items: torch.Tensor\\n+ history_features: torch.Tensor\\n+ positives: list[np.ndarray]\\n+ popularity: np.ndarray\\n+ item_ids: np.ndarray\\n+\\n+\\n+def load_catalog_events(stage: str, item_ids: np.ndarray) -> tuple[pd.DataFrame, pd.DataFrame, int]:\\n+ \\\"\\\"\\\"Read only catalog events, then append validation history for final refit.\\\"\\\"\\\"\\n+ filt = ds.field(\\\"item_id\\\").isin(pa.array(item_ids))\\n+ listen_cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ like_cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ listens = ds.dataset(DATA / \\\"train_listens.parquet\\\", format=\\\"parquet\\\").to_table(columns=listen_cols, filter=filt).to_pandas()\\n+ likes = ds.dataset(DATA / \\\"train_likes.parquet\\\", format=\\\"parquet\\\").to_table(columns=like_cols, filter=filt).to_pandas()\\n+ cutoff = TRAIN_CUTOFF\\n+ if stage == \\\"final\\\":\\n+ listens = pd.concat([listens, ds.dataset(DATA / \\\"val_listens.parquet\\\", format=\\\"parquet\\\").to_table(columns=listen_cols, filter=filt).to_pandas()], ignore_index=True)\\n+ likes = pd.concat([likes, ds.dataset(DATA / \\\"val_likes.parquet\\\", format=\\\"parquet\\\").to_table(columns=like_cols, filter=filt).to_pandas()], ignore_index=True)\\n+ cutoff = int(max(listens.timestamp.max(), likes.timestamp.max()))\\n+ return listens, likes, cutoff\\n+\\n+\\n+def prepare(stage: str, history_length: int) -> Prepared:\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ item_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ direct = np.full(int(item_ids.max()) + 1, -1, dtype=np.int32)\\n+ direct[item_ids] = np.arange(len(item_ids), dtype=np.int32)\\n+ target_uids = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\\n+ listens, likes, cutoff = load_catalog_events(stage, item_ids)\\n+ listens = listens[listens.uid.isin(target_uids)].copy()\\n+ likes = likes[likes.uid.isin(target_uids)].copy()\\n+ listens[\\\"item\\\"] = direct[listens.item_id.to_numpy()]\\n+ likes[\\\"item\\\"] = direct[likes.item_id.to_numpy()]\\n+\\n+ # Global recent positive prior is both a useful cold-start feature and the\\n+ # final dot-product bias coordinate.\\n+ pos = listens.played_ratio_pct.to_numpy() >= 50\\n+ age = (cutoff - listens.timestamp.to_numpy(dtype=np.float64)) / (30 * DAY)\\n+ popularity = np.bincount(\\n+ listens.item.to_numpy()[pos], weights=np.exp(-np.maximum(age[pos], 0)),\\n+ minlength=len(item_ids),\\n+ ).astype(np.float32)\\n+ pop_rank = np.empty(len(item_ids), dtype=np.int32)\\n+ pop_rank[np.argsort(-popularity, kind=\\\"stable\\\")] = np.arange(1, len(item_ids) + 1)\\n+ popularity = -np.log(pop_rank.astype(np.float32))\\n+ popularity = (popularity - popularity.mean()) / popularity.std()\\n+\\n+ listens[\\\"strong\\\"] = ((listens.is_organic == 1) & (listens.played_ratio_pct >= 80)).astype(np.uint8)\\n+ listens = listens[listens.played_ratio_pct >= 50]\\n+ grouped = listens.groupby([\\\"uid\\\", \\\"item\\\"], sort=False, observed=True).agg(\\n+ last=(\\\"timestamp\\\", \\\"max\\\"), count=(\\\"timestamp\\\", \\\"size\\\"),\\n+ organic=(\\\"is_organic\\\", \\\"mean\\\"), strong=(\\\"strong\\\", \\\"max\\\"),\\n+ ).reset_index()\\n+\\n+ pref_file = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_file)\\n+ pref = pref[(pref.state == \\\"liked\\\") & pref.uid.isin(target_uids) & (pref.item_id <= direct.shape[0] - 1)].copy()\\n+ pref[\\\"item\\\"] = direct[pref.item_id.to_numpy()]\\n+ pref = pref[pref.item >= 0][[\\\"uid\\\", \\\"item\\\"]].drop_duplicates()\\n+ liked_pairs = set(zip(pref.uid.astype(int), pref.item.astype(int)))\\n+\\n+ n_users, pad = len(target_uids), len(item_ids)\\n+ hist_items = np.full((n_users, history_length), pad, dtype=np.int64)\\n+ hist_features = np.zeros((n_users, history_length, 3), dtype=np.float32)\\n+ positives: list[list[int]] = [[] for _ in range(n_users)]\\n+\\n+ # Bounded, sparse per-user states. Selection mixes recency and repeat\\n+ # strength; features let the network learn the pooling weights.\\n+ grouped[\\\"selection\\\"] = grouped[\\\"last\\\"].astype(np.float64) + 30 * DAY * np.log1p(grouped[\\\"count\\\"])\\n+ for uid, g in grouped.groupby(\\\"uid\\\", sort=False):\\n+ row = uid_to_row[int(uid)]\\n+ strong_items = g.loc[g.strong == 1, \\\"item\\\"].to_numpy(dtype=np.int32)\\n+ positives[row].extend(strong_items.tolist())\\n+ h = g.nlargest(history_length, \\\"selection\\\")\\n+ ids = h.item.to_numpy(dtype=np.int64)\\n+ k = len(ids)\\n+ hist_items[row, :k] = ids\\n+ hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\\\"last\\\"].to_numpy(), 0) / (90 * DAY))\\n+ hist_features[row, :k, 1] = np.log1p(h[\\\"count\\\"].to_numpy()) / 5.0\\n+ hist_features[row, :k, 2] = h[\\\"organic\\\"].to_numpy()\\n+\\n+ # Organic likes are high-grade training targets. Active likes also receive\\n+ # an explicit history feature without ever becoming a request-time filter.\\n+ organic_likes = likes[likes.is_organic == 1][[\\\"uid\\\", \\\"item\\\"]].drop_duplicates()\\n+ for uid, item in organic_likes.itertuples(index=False, name=None):\\n+ row = uid_to_row[int(uid)]\\n+ positives[row].extend([int(item), int(item), int(item)])\\n+ for row, uid in enumerate(target_uids):\\n+ for j, item in enumerate(hist_items[row]):\\n+ if item == pad:\\n+ break\\n+ if (int(uid), int(item)) in liked_pairs:\\n+ hist_features[row, j, 2] += 1.5\\n+\\n+ positive_arrays = [np.asarray(p, dtype=np.int64) for p in positives]\\n+ del listens, likes, grouped, pref\\n+ return Prepared(\\n+ target_uids, torch.from_numpy(hist_items), torch.from_numpy(hist_features),\\n+ positive_arrays, popularity, item_ids,\\n+ )\\n+\\n+\\n+def train_model(data: Prepared, dim: int, epochs: int, batch_size: int, seed: int) -> TwoTower:\\n+ torch.manual_seed(seed); np.random.seed(seed); random.seed(seed)\\n+ torch.set_num_threads(min(8, torch.get_num_threads()))\\n+ model = TwoTower(len(data.item_ids), dim)\\n+ nn.init.normal_(model.user_tower.history_embedding.weight, std=0.08)\\n+ nn.init.normal_(model.item_tower.item_embedding.weight, std=0.08)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ valid_users = np.asarray([i for i, p in enumerate(data.positives) if len(p) and (data.history_items[i] != len(data.item_ids)).sum() >= 2])\\n+ rng = np.random.default_rng(seed)\\n+ steps_per_epoch = max(1, len(valid_users) // batch_size)\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(valid_users)\\n+ losses = []\\n+ for start in range(0, len(order) - batch_size + 1, batch_size):\\n+ users_np = order[start:start + batch_size]\\n+ targets_np = np.asarray([rng.choice(data.positives[u]) for u in users_np], dtype=np.int64)\\n+ histories = data.history_items[users_np].clone()\\n+ features = data.history_features[users_np]\\n+ targets = torch.from_numpy(targets_np)\\n+ histories[histories == targets[:, None]] = len(data.item_ids)\\n+ logits = model(histories, features, targets)\\n+ labels = torch.arange(len(users_np))\\n+ loss = F.cross_entropy(logits, labels)\\n+ optimizer.zero_grad(set_to_none=True); loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step(); losses.append(float(loss.detach()))\\n+ if epoch == 0 or (epoch + 1) % 10 == 0 or epoch + 1 == epochs:\\n+ print(json.dumps({\\\"epoch\\\": epoch + 1, \\\"steps\\\": steps_per_epoch, \\\"loss\\\": round(float(np.mean(losses)), 5), \\\"scale\\\": round(float(model.logit_scale.exp()), 3)}), flush=True)\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def write_rankings(model: TwoTower, data: Prepared, stage: str, blends: list[float]) -> list[Path]:\\n+ model.eval()\\n+ eligibility_file = \\\"validation_eligible_candidates.npz\\\" if stage == \\\"validation\\\" else \\\"test_eligible_candidates.npz\\\"\\n+ with np.load(DATA / eligibility_file) as z:\\n+ elig_uids, eligible_items, packed = z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(elig_uids.astype(np.int64), data.uids)\\n+ assert np.array_equal(eligible_items.astype(np.int64), data.item_ids)\\n+ all_items = torch.arange(len(data.item_ids))\\n+ item_latent = model.item_tower(all_items)\\n+ frames: dict[float, list[pd.DataFrame]] = {b: [] for b in blends}\\n+ batch = 128\\n+ for start in range(0, len(data.uids), batch):\\n+ end = min(start + batch, len(data.uids))\\n+ user_latent = model.user_tower(data.history_items[start:end], data.history_features[start:end])\\n+ neural = user_latent @ item_latent.T\\n+ mask = np.unpackbits(packed[start:end], axis=1, count=len(data.item_ids), bitorder=\\\"little\\\").astype(bool)\\n+ mask_t = torch.from_numpy(mask)\\n+ for blend in blends:\\n+ score = neural * blend + torch.from_numpy(data.popularity)[None, :]\\n+ score.masked_fill_(~mask_t, -torch.inf)\\n+ top = torch.topk(score, 100, dim=1, sorted=True).indices.numpy()\\n+ frames[blend].append(pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(data.uids[start:end], 100),\\n+ \\\"item_id\\\": data.item_ids[top.reshape(-1)],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101), end - start),\\n+ }))\\n+ paths = []\\n+ for blend in blends:\\n+ label = str(blend).replace(\\\".\\\", \\\"p\\\")\\n+ path = OUT / (f\\\"validation_twotower_b{label}.parquet\\\" if stage == \\\"validation\\\" else \\\"submission.parquet\\\")\\n+ pd.concat(frames[blend], ignore_index=True).to_parquet(path, index=False)\\n+ paths.append(path)\\n+ return paths\\n+\\n+\\n+def main() -> None:\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ ap.add_argument(\\\"--epochs\\\", type=int, default=50)\\n+ ap.add_argument(\\\"--dim\\\", type=int, default=48)\\n+ ap.add_argument(\\\"--history-length\\\", type=int, default=192)\\n+ ap.add_argument(\\\"--batch-size\\\", type=int, default=384)\\n+ ap.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ ap.add_argument(\\\"--blend\\\", type=float, default=4.0, help=\\\"chosen latent-vs-popularity coefficient for final\\\")\\n+ args = ap.parse_args()\\n+ data = prepare(args.stage, args.history_length)\\n+ print(json.dumps({\\\"stage\\\": args.stage, \\\"users\\\": len(data.uids), \\\"items\\\": len(data.item_ids), \\\"history_shape\\\": list(data.history_items.shape), \\\"positive_pairs\\\": int(sum(map(len, data.positives)))}), flush=True)\\n+ model = train_model(data, args.dim, args.epochs, args.batch_size, args.seed)\\n+ checkpoint = OUT / f\\\"model_{args.stage}.pt\\\"\\n+ torch.save({\\\"state_dict\\\": model.state_dict(), \\\"dim\\\": args.dim, \\\"history_length\\\": args.history_length, \\\"seed\\\": args.seed, \\\"blend\\\": args.blend}, checkpoint)\\n+ blends = [0.0, 0.5, 1.0, 2.0, 4.0, 8.0] if args.stage == \\\"validation\\\" else [args.blend]\\n+ print(\\\"wrote\\\", *map(str, write_rankings(model, data, args.stage, blends)), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst p = await tools.update_plan({explanation:\"Data inspection and leakage-safe global baselines are complete; the scalable history-to-item two-tower pipeline is implemented.\",plan:[\n {step:\"Inspect data, schemas, sizes, and evaluation helpers\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and compare validation variants; save best valid ranking\",status:\"in_progress\"},\n {step:\"Refit on train + validation and generate test submission\",status:\"pending\"},\n {step:\"Validate artifacts and write concise README\",status:\"pending\"}\n]});\ntext(a);\ntext(p);\n" } ### Tool output · 2026-08-31T18:36:26.790Z Script completed Wall time 0.0 seconds Output: {} {} ## Step 33 · agent · 2026-08-31T18:36:30.351Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:34.530Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --epochs 50 --dim 48 --history-length 192 --batch-size 384 2>&1 | tee /app/solution/train_validation.log\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:36:45.551Z Script running with cell ID 14 Wall time 11.0 seconds Output: ## Step 34 · agent · 2026-08-31T18:36:49.414Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:36:50.251Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:37:04.723Z Script completed Wall time 14.5 seconds Output: Warning: truncated output (original token count: 5212) Total output lines: 1 {"session_id":59049,"output":"Warning: truncated output (original token count: 378261)\n... 464466 bytes omitted ...\n\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in …212 tokens truncated…numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n/app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast\r\n hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\"last\"].to_numpy(), 0) / (90 * DAY))\r\n{\"stage\": \"validation\", \"users\": 8456, \"items\": 30000, \"history_shape\": [8456, 192], \"positive_pairs\": 2581120}\r\n/app/solution/train_ranker.py:215: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n print(json.dumps({\"epoch\": epoch + 1, \"steps\": steps_per_epoch, \"loss\": round(float(np.mean(losses)), 5), \"scale\": round(float(model.logit_scale.exp()), 3)}), flush=True)\r\n{\"epoch\": 1, \"steps\": 20, \"loss\": NaN, \"scale\": NaN}\r\n{\"epoch\": 10, \"steps\": 20, \"loss\": NaN, \"scale\": NaN}\r\n"} ## Step 35 · agent · 2026-08-31T18:37:15.135Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-08-31T18:37:16.213Z ### Thinking unavailable — no text recorded The first neural run exposed an unsigned-timestamp arithmetic bug before producing a usable checkpoint: negating an unsigned age overflowed the recency feature and caused NaNs. I’ve isolated it to feature construction, will cast ages before exponentiation, and will restart cleanly; the saved popularity baseline remains valid. ### exec · 2026-08-31T18:37:30.732Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h[\\\"last\\\"].to_numpy(), 0) / (90 * DAY))\\n+ raw_last = h[\\\"last\\\"].to_numpy(dtype=np.float64)\\n+ hist_features[row, :k, 0] = np.exp(-np.maximum(float(cutoff) - raw_last, 0.0) / (90 * DAY))\\n@@\\n- print(json.dumps({\\\"epoch\\\": epoch + 1, \\\"steps\\\": steps_per_epoch, \\\"loss\\\": round(float(np.mean(losses)), 5), \\\"scale\\\": round(float(model.logit_scale.exp()), 3)}), flush=True)\\n+ print(json.dumps({\\\"epoch\\\": epoch + 1, \\\"steps\\\": steps_per_epoch, \\\"loss\\\": round(float(np.mean(losses)), 5), \\\"scale\\\": round(float(model.logit_scale.exp().detach()), 3)}), flush=True)\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"ps -eo pid,etime,cmd | grep '[t]rain_ranker.py' || true\\ntail -30 /app/solution/train_validation.log | sed -n '1,30p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:4000});\ntext(a);\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:37:30.788Z Script completed Wall time 0.0 seconds Output: {} /bin/sh: 1: ps: not found hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) /app/solution/train_ranker.py:162: RuntimeWarning: overflow encountered in cast hist_features[row, :k, 0] = np.exp(-np.maximum(cutoff - h["last"].to_numpy(), 0) / (90 * DAY)) {"stage": "validation", "users": 8456, "items": 30000, "history_shape": [8456, 192], "positive_pairs": 2581120} /app/solution/train_ranker.py:215: 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.) print(json.dumps({"epoch": epoch + 1, "steps": steps_per_epoch, "loss": round(float(np.mean(losses)), 5), "scale": round(float(model.logit_scale.exp()), 3)}), flush=True) {"epoch": 1, "steps": 20, "loss": NaN, "scale": NaN} {"epoch": 10, "steps": 20, "loss": NaN, "scale": NaN} {"epoch": 20, "steps": 20, "loss": NaN, "scale": NaN} {"epoch": 30, "steps": 20, "loss": NaN, "scale": NaN} {"epoch": 40, "steps": 20, "loss": NaN, "scale": NaN} {"epoch": 50, "steps": 20, "loss": NaN, "scale": NaN} wrote /app/solution/validation_twotower_b0p0.parquet /app/solution/validation_twotower_b0p5.parquet /app/solution/validation_twotower_b1p0.parquet /app/solution/validation_twotower_b2p0.parquet /app/solution/validation_twotower_b4p0.parquet /app/solution/validation_twotower_b8p0.parquet ## Step 37 · agent · 2026-08-31T18:37:33.459Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:37.671Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --epochs 60 --dim 48 --history-length 192 --batch-size 384 2>&1 | tee /app/solution/train_validation_fixed.log\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:37:48.693Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 38 · agent · 2026-08-31T18:37:50.286Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:51.084Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:38:07.853Z Script completed Wall time 16.8 seconds Output: {"session_id":81193,"output":"{\"stage\": \"validation\", \"users\": 8456, \"items\": 30000, \"history_shape\": [8456, 192], \"positive_pairs\": 2581120}\r\n{\"epoch\": 1, \"steps\": 20, \"loss\": 6.25228, \"scale\": 9.694}\r\n{\"epoch\": 10, \"steps\": 20, \"loss\": 5.77807, \"scale\": 9.64}\r\n"} ## Step 39 · agent · 2026-08-31T18:38:09.976Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:13.332Z { "code": "const r = await tools.exec_command({cmd:\"tail -30 /app/solution/train_validation_fixed.log\\nls -l /app/solution/model_validation.pt /app/solution/validation_twotower_b4p0.parquet 2>/dev/null || true\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:13.414Z Script completed Wall time 0.1 seconds Output: {"stage": "validation", "users": 8456, "items": 30000, "history_shape": [8456, 192], "positive_pairs": 2581120} {"epoch": 1, "steps": 20, "loss": 6.25228, "scale": 9.694} {"epoch": 10, "steps": 20, "loss": 5.77807, "scale": 9.64} {"epoch": 20, "steps": 20, "loss": 5.44374, "scale": 10.042} -rw-r--r-- 1 agent agent 11603723 Aug 31 18:37 /app/solution/model_validation.pt -rw-r--r-- 1 agent agent 935917 Aug 31 18:37 /app/solution/validation_twotower_b4p0.parquet ## Step 40 · agent · 2026-08-31T18:38:21.659Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:23.019Z { "code": "const r = await tools.write_stdin({session_id:81193,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:32.910Z Script completed Wall time 9.9 seconds Output: {"epoch": 20, "steps": 20, "loss": 5.44374, "scale": 10.042} {"epoch": 30, "steps": 20, "loss": 5.24213, "scale": 10.871} {"epoch": 40, "steps": 20, "loss": 5.03169, "scale": 12.086} {"epoch": 50, "steps": 20, "loss": 4.88639, "scale": 13.506} {"epoch": 60, "steps": 20, "loss": 4.75961, "scale": 14.766} wrote /app/solution/validation_twotower_b0p0.parquet /app/solution/validation_twotower_b0p5.parquet /app/solution/validation_twotower_b1p0.parquet /app/solution/validation_twotower_b2p0.parquet /app/solution/validation_twotower_b4p0.parquet /app/solution/validation_twotower_b8p0.parquet ## Step 41 · agent · 2026-08-31T18:38:34.616Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:42.051Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nD=Path('/task/data'); O=Path('/app/solution'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=validation_truth(e,targets)\\nfor b in ['0p0','0p5','1p0','2p0','4p0','8p0']:\\n p=O/f'validation_twotower_b{b}.parquet'; print(b,score(load_submission(p,e,targets),g))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:38:52.164Z Script completed Wall time 10.1 seconds Output: 0p0 {'quality_ndcg@10': 0.018098, 'quality_users': 4263} 0p5 {'quality_ndcg@10': 0.018276, 'quality_users': 4263} 1p0 {'quality_ndcg@10': 0.018673, 'quality_users': 4263} 2p0 {'quality_ndcg@10': 0.018985, 'quality_users': 4263} 4p0 {'quality_ndcg@10': 0.019669, 'quality_users': 4263} 8p0 {'quality_ndcg@10': 0.020561, 'quality_users': 4263} ## Step 42 · agent · 2026-08-31T18:38:55.913Z ### Thinking unavailable — no text recorded The corrected two-tower model is valid and lifts public NDCG@10 to 0.020561, roughly 13.5% over the strongest non-neural prior. The best tested mixture is still at the edge of the blend grid, so I’m extending that grid before locking the final refit coefficient. ### exec · 2026-08-31T18:39:05.358Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport torch\\nfrom train_ranker import prepare,TwoTower,write_rankings\\np=prepare('validation',192); ck=torch.load('model_validation.pt',weights_only=False); m=TwoTower(len(p.item_ids),ck['dim']); m.load_state_dict(ck['state_dict']); print([str(x) for x in write_rankings(m,p,'validation',[12.0,16.0,24.0,32.0,48.0])])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:39:16.378Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 43 · agent · 2026-08-31T18:39:18.534Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:19.312Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 3000 } ### Tool output · 2026-08-31T18:39:28.041Z Script completed Wall time 8.7 seconds Output: ['/app/solution/validation_twotower_b12p0.parquet', '/app/solution/validation_twotower_b16p0.parquet', '/app/solution/validation_twotower_b24p0.parquet', '/app/solution/validation_twotower_b32p0.parquet', '/app/solution/validation_twotower_b48p0.parquet'] ## Step 44 · agent · 2026-08-31T18:39:29.471Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:36.840Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); import pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nD=Path('/task/data'); O=Path('/app/solution'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\\nfor b in ['8p0','12p0','16p0','24p0','32p0','48p0']:\\n print(b,score(load_submission(O/f'validation_twotower_b{b}.parquet',e,t),g)['quality_ndcg@10'])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:46.657Z Script completed Wall time 9.8 seconds Output: 8p0 0.020561 12p0 0.022408 16p0 0.023008 24p0 0.021857 32p0 0.018982 48p0 0.013646 ## Step 45 · agent · 2026-08-31T18:40:05.805Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:19.070Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- grouped[\\\"selection\\\"] = grouped[\\\"last\\\"].astype(np.float64) + 30 * DAY * np.log1p(grouped[\\\"count\\\"])\\n+ grouped[\\\"selection\\\"] = grouped[\\\"last\\\"].astype(np.float64) + 30 * DAY * np.log1p(grouped[\\\"count\\\"])\\n+ # Guarantee that explicit active likes survive the bounded-history truncation.\\n+ liked_key = pd.MultiIndex.from_tuples(liked_pairs, names=[\\\"uid\\\", \\\"item\\\"])\\n+ grouped_key = pd.MultiIndex.from_frame(grouped[[\\\"uid\\\", \\\"item\\\"]])\\n+ grouped.loc[grouped_key.isin(liked_key), \\\"selection\\\"] += 20 * 365 * DAY\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"cp /app/solution/validation_twotower_b16p0.parquet /app/solution/validation.parquet\\ncp /app/solution/model_validation.pt /app/solution/model_validation_best.pt\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --epochs 100 --dim 64 --history-length 256 --batch-size 384 --seed 20260901 2>&1 | tee /app/solution/train_validation_v2.log\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(a); text(r.output); }\n" } ### Tool output · 2026-08-31T18:40:30.091Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T18:40:32.233Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:33.130Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:40:49.257Z Script completed Wall time 16.1 seconds Output: {"session_id":91115,"output":"{\"stage\": \"validation\", \"users\": 8456, \"items\": 30000, \"history_shape\": [8456, 256], \"positive_pairs\": 2581120}\r\n{\"epoch\": 1, \"steps\": 20, \"loss\": 6.13319, \"scale\": 9.703}\r\n{\"epoch\": 10, \"steps\": 20, \"loss\": 5.73095, \"scale\": 9.728}\r\n"} ## Step 47 · agent · 2026-08-31T18:40:50.780Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:52.476Z { "code": "const r = await tools.write_stdin({session_id:91115,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:03.508Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 48 · agent · 2026-08-31T18:41:05.409Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:06.214Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:41:22.513Z Script completed Wall time 16.3 seconds Output: {"epoch": 20, "steps": 20, "loss": 5.37465, "scale": 10.325} {"epoch": 30, "steps": 20, "loss": 5.10861, "scale": 11.656} {"epoch": 40, "steps": 20, "loss": 4.92659, "scale": 13.179} {"epoch": 50, "steps": 20, "loss": 4.80621, "scale": 14.687} {"epoch": 60, "steps": 20, "loss": 4.69788, "scale": 15.984} ## Step 49 · agent · 2026-08-31T18:41:24.510Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:25.856Z { "code": "const r = await tools.write_stdin({session_id:91115,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:36.885Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 50 · agent · 2026-08-31T18:41:38.930Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:39.679Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:41:52.168Z Script completed Wall time 12.5 seconds Output: {"epoch": 70, "steps": 20, "loss": 4.61173, "scale": 17.14} {"epoch": 80, "steps": 20, "loss": 4.53815, "scale": 18.363} {"epoch": 90, "steps": 20, "loss": 4.46672, "scale": 19.416} {"epoch": 100, "steps": 20, "loss": 4.43057, "scale": 20.331} wrote /app/solution/validation_twotower_b0p0.parquet /app/solution/validation_twotower_b0p5.parquet /app/solution/validation_twotower_b1p0.parquet /app/solution/validation_twotower_b2p0.parquet /app/solution/validation_twotower_b4p0.parquet /app/solution/validation_twotower_b8p0.parquet ## Step 51 · agent · 2026-08-31T18:41:53.785Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:07.839Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nfrom train_ranker import prepare,TwoTower,write_rankings\\nD=Path('/task/data'); O=Path('/app/solution'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\\nfor b in ['0p0','0p5','1p0','2p0','4p0','8p0']:\\n print('v2',b,score(load_submission(O/f'validation_twotower_b{b}.parquet',e,t),g)['quality_ndcg@10'])\\np=prepare('validation',256); ck=torch.load('model_validation.pt',weights_only=False); m=TwoTower(len(p.item_ids),ck['dim']); m.load_state_dict(ck['state_dict']); write_rankings(m,p,'validation',[12.,16.,20.,24.,32.])\\nfor b in ['12p0','16p0','20p0','24p0','32p0']:\\n print('v2',b,score(load_submission(O/f'validation_twotower_b{b}.parquet',e,t),g)['quality_ndcg@10'])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:42:18.859Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 52 · agent · 2026-08-31T18:42:20.503Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:21.319Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:42:38.021Z Script completed Wall time 16.7 seconds Output: {"session_id":69575,"output":"v2 0p0 0.018098\r\nv2 0p5 0.01826\r\nv2 1p0 0.018462\r\nv2 2p0 0.01867\r\nv2 4p0 0.019384\r\nv2 8p0 0.02\r\n"} ## Step 53 · agent · 2026-08-31T18:42:39.743Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:40.980Z { "code": "const r = await tools.write_stdin({session_id:69575,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:45.989Z Script completed Wall time 5.0 seconds Output: v2 12p0 0.021024 v2 16p0 0.02193 v2 20p0 0.022919 v2 24p0 0.02255 v2 32p0 0.020324 ## Step 54 · agent · 2026-08-31T18:43:05.643Z ### Thinking unavailable — no text recorded ## Step 55 · agent · 2026-08-31T18:43:10.154Z ### Thinking unavailable — no text recorded The larger second model overfit and peaked at 0.022919, below the preserved 0.023008 ranking. I’m retaining the first model and restoring its exact bounded-history recipe. I’ll do one targeted calibration pass on that checkpoint, then refit the selected 48-dimensional model on the expanded history. ### exec · 2026-08-31T18:43:23.863Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- # Guarantee that explicit active likes survive the bounded-history truncation.\\n- liked_key = pd.MultiIndex.from_tuples(liked_pairs, names=[\\\"uid\\\", \\\"item\\\"])\\n- grouped_key = pd.MultiIndex.from_frame(grouped[[\\\"uid\\\", \\\"item\\\"]])\\n- grouped.loc[grouped_key.isin(liked_key), \\\"selection\\\"] += 20 * 365 * DAY\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport torch\\nfrom train_ranker import prepare,TwoTower,write_rankings\\np=prepare('validation',192); ck=torch.load('model_validation_best.pt',weights_only=False); m=TwoTower(len(p.item_ids),ck['dim']); m.load_state_dict(ck['state_dict']); write_rankings(m,p,'validation',[8.,10.,12.,14.,16.,18.,20.,22.,24.])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(a); text(r.output); }\n" } ### Tool output · 2026-08-31T18:43:34.883Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 56 · agent · 2026-08-31T18:43:37.176Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:37.996Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 2000 } ### Tool output · 2026-08-31T18:43:48.157Z Script completed Wall time 10.2 seconds Output: {} ## Step 57 · agent · 2026-08-31T18:43:50.035Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:02.601Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); import pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score,ndcg_at_k\\nD=Path('/task/data'); O=Path('/app/solution'); tf=pd.read_parquet(D/'target_users.parquet'); t=set(tf.uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\\nrankings={}\\nfor b in [8,10,12,14,16,18,20,22,24]:\\n p=O/f\\\"validation_twotower_b{b}p0.parquet\\\"; rankings[b]=load_submission(p,e,t); print(b,score(rankings[b],g)['quality_ndcg@10'])\\n# Segment users with truth by recent positive-listen quartiles and show blend scores.\\nmeta=tf.set_index('uid').recent_positive_listens\\nq=pd.qcut(meta.loc[list(g)].rank(method='first'),4,labels=False)\\nfor group in range(4):\\n us=[u for u in g if q.loc[u]==group]\\n print('quartile',group,'n',len(us),[(b,round(sum(ndcg_at_k(rankings[b][u],g[u],10) for u in us)/len(us),5)) for b in rankings])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:44:04.128Z Script completed Wall time 1.5 seconds Output: Traceback (most recent call last): File "", line 4, in ImportError: cannot import name 'ndcg_at_k' from 'evaluate_public' (/task/evaluate_public.py) ## Step 58 · agent · 2026-08-31T18:44:05.821Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:17.860Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); import pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nfrom ranking_evaluation import ndcg_at_k\\nD=Path('/task/data'); O=Path('/app/solution'); tf=pd.read_parquet(D/'target_users.parquet'); t=set(tf.uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\\nrankings={}\\nfor b in [8,10,12,14,16,18,20,22,24]:\\n p=O/f\\\"validation_twotower_b{b}p0.parquet\\\"; rankings[b]=load_submission(p,e,t); print(b,score(rankings[b],g)['quality_ndcg@10'])\\nmeta=tf.set_index('uid').recent_positive_listens\\nq=pd.qcut(meta.loc[list(g)].rank(method='first'),4,labels=False)\\nfor group in range(4):\\n us=[u for u in g if q.loc[u]==group]\\n print('quartile',group,'n',len(us),[(b,round(sum(ndcg_at_k(rankings[b][u],g[u],10) for u in us)/len(us),5)) for b in rankings])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:44:28.880Z Script running with cell ID 29 Wall time 11.0 seconds Output: ## Step 59 · agent · 2026-08-31T18:44:30.408Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:31.211Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:44:32.524Z Script completed Wall time 1.3 seconds Output: 8 0.020561 10 0.021684 12 0.022408 14 0.022909 16 0.023008 18 0.022803 20 0.022458 22 0.022016 24 0.021857 quartile 0 n 1066 [(8, 0.02919), (10, 0.02946), (12, 0.03011), (14, 0.0294), (16, 0.02909), (18, 0.02883), (20, 0.0285), (22, 0.02751), (24, 0.02622)] quartile 1 n 1066 [(8, 0.0201), (10, 0.02159), (12, 0.02229), (14, 0.02398), (16, 0.02397), (18, 0.02453), (20, 0.02431), (22, 0.0249), (24, 0.02488)] quartile 2 n 1065 [(8, 0.01717), (10, 0.01826), (12, 0.01914), (14, 0.01926), (16, 0.01967), (18, 0.01913), (20, 0.01907), (22, 0.01908), (24, 0.01913)] quartile 3 n 1066 [(8, 0.01577), (10, 0.01742), (12, 0.01809), (14, 0.01899), (16, 0.0193), (18, 0.01872), (20, 0.01795), (22, 0.01658), (24, 0.01719)] ## Step 60 · agent · 2026-08-31T18:44:42.993Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:58.705Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); import pandas as pd\\nfrom evaluate_public import CandidateSet,validation_truth,load_submission,score\\nfrom ranking_evaluation import ndcg_at_k\\nD=Path('/task/data');O=Path('/app/solution');tf=pd.read_parquet(D/'target_users.parquet');t=set(tf.uid.astype(int));e=CandidateSet(D/'validation_eligible_candidates.npz');g=validation_truth(e,t)\\nbs=[10,12,14,16,18,20,22,24]; rs={b:load_submission(O/f'validation_twotower_b{b}p0.parquet',e,t) for b in bs}\\ntf['bin']=pd.qcut(tf.recent_positive_listens.rank(method='first'),4,labels=False); bins=tf.set_index('uid')['bin']\\nchoices={}\\nfor z in range(4):\\n us=[u for u in g if bins.loc[u]==z]; vals={b:sum(ndcg_at_k(rs[b][u],g[u],10) for u in us)/len(us) for b in bs}; choices[z]=max(vals,key=vals.get); print(z,len(us),choices[z],round(vals[choices[z]],6),[(b,round(vals[b],5)) for b in bs])\\nadapt={u:rs[choices[int(bins.loc[u])]][u] for u in t}; print('adaptive',score(adapt,g),choices)\\n# save adaptive\\nrows=[]\\nfor uid,items in adapt.items():\\n for rank,item in enumerate(items,1): rows.append((uid,item,rank))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_adaptive.parquet',index=False)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:45:09.726Z Script running with cell ID 30 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:45:11.552Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:12.381Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:45:12.442Z Script completed Wall time 0.0 seconds Output: 0 828 12 0.033136 [(10, 0.0327), (12, 0.03314), (14, 0.03231), (16, 0.03136), (18, 0.03018), (20, 0.02909), (22, 0.02836), (24, 0.02701)] 1 998 22 0.028831 [(10, 0.02387), (12, 0.02512), (14, 0.02653), (16, 0.02721), (18, 0.02829), (20, 0.02881), (22, 0.02883), (24, 0.02854)] 2 1159 14 0.017816 [(10, 0.01716), (12, 0.0173), (14, 0.01782), (16, 0.01748), (18, 0.01729), (20, 0.01712), (22, 0.0171), (24, 0.0172)] 3 1278 16 0.019327 [(10, 0.01694), (12, 0.01797), (14, 0.01861), (16, 0.01933), (18, 0.01873), (20, 0.01805), (22, 0.01704), (24, 0.01752)] adaptive {'quality_ndcg@10': 0.023823, 'quality_users': 4263} {0: 12, 1: 22, 2: 14, 3: 16} ## Step 62 · agent · 2026-08-31T18:45:33.049Z ### Thinking unavailable — no text recorded ## Step 63 · agent · 2026-08-31T18:45:39.866Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:14.014Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class UserTower(nn.Module):\\n@@\\n def forward(self, items: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n@@\\n return F.normalize(self.network(pooled) + pooled, dim=-1)\\n+\\n+ def retrieval_vector(self, items: torch.Tensor, features: torch.Tensor, blend: torch.Tensor) -> torch.Tensor:\\n+ \\\"\\\"\\\"Return the complete request-time vector, including the bias query.\\\"\\\"\\\"\\n+ latent = self(items, features) * blend.reshape(-1, 1)\\n+ return torch.cat([latent, torch.ones((len(latent), 1), dtype=latent.dtype)], dim=1)\\n@@\\n class ItemTower(nn.Module):\\n@@\\n def forward(self, items: torch.Tensor) -> torch.Tensor:\\n@@\\n return F.normalize(self.network(base) + base, dim=-1)\\n+\\n+ def retrieval_vector(self, items: torch.Tensor, popularity: torch.Tensor) -> torch.Tensor:\\n+ \\\"\\\"\\\"Return the offline-index vector with its learned-model prior feature.\\\"\\\"\\\"\\n+ return torch.cat([self(items), popularity.reshape(-1, 1)], dim=1)\\n@@\\n def write_rankings(model: TwoTower, data: Prepared, stage: str, blends: list[float]) -> list[Path]:\\n@@\\n- item_latent = model.item_tower(all_items)\\n+ item_vectors = model.item_tower.retrieval_vector(all_items, torch.from_numpy(data.popularity))\\n+ target_meta = pd.read_parquet(DATA / \\\"target_users.parquet\\\").copy()\\n+ target_meta[\\\"activity_bin\\\"] = pd.qcut(target_meta.recent_positive_listens.rank(method=\\\"first\\\"), 4, labels=False)\\n+ activity_bin = target_meta.set_index(\\\"uid\\\").loc[data.uids, \\\"activity_bin\\\"].to_numpy(dtype=np.int64)\\n+ adaptive_values = np.asarray([12.0, 22.0, 14.0, 16.0], dtype=np.float32)\\n@@\\n- user_latent = model.user_tower(data.history_items[start:end], data.history_features[start:end])\\n- neural = user_latent @ item_latent.T\\n mask = np.unpackbits(packed[start:end], axis=1, count=len(data.item_ids), bitorder=\\\"little\\\").astype(bool)\\n mask_t = torch.from_numpy(mask)\\n for blend in blends:\\n- score = neural * blend + torch.from_numpy(data.popularity)[None, :]\\n+ row_blend = adaptive_values[activity_bin[start:end]] if blend < 0 else np.full(end - start, blend, dtype=np.float32)\\n+ user_vectors = model.user_tower.retrieval_vector(\\n+ data.history_items[start:end], data.history_features[start:end], torch.from_numpy(row_blend)\\n+ )\\n+ score = user_vectors @ item_vectors.T\\n@@\\n- label = str(blend).replace(\\\".\\\", \\\"p\\\")\\n+ label = \\\"adaptive\\\" if blend < 0 else str(blend).replace(\\\".\\\", \\\"p\\\")\\n@@\\n- ap.add_argument(\\\"--blend\\\", type=float, default=4.0, help=\\\"chosen latent-vs-popularity coefficient for final\\\")\\n+ ap.add_argument(\\\"--blend\\\", type=float, default=-1.0, help=\\\"final latent coefficient; negative selects activity-calibrated coefficients\\\")\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst p = await tools.update_plan({explanation:\"Validation selected the preserved 48D model with activity-calibrated exact dot-product blending (public NDCG@10 0.023823).\",plan:[\n {step:\"Inspect data, schemas, sizes, and evaluation helpers\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and compare validation variants; save best valid ranking\",status:\"completed\"},\n {step:\"Refit on train + validation and generate test submission\",status:\"in_progress\"},\n {step:\"Validate artifacts and write concise README\",status:\"pending\"}\n]});\ntext(a);\ntext(p);\n" } ### Tool output · 2026-08-31T18:46:14.084Z Script completed Wall time 0.0 seconds Output: {} {} ## Step 64 · agent · 2026-08-31T18:46:15.488Z ### Thinking unavailable — no text recorded Validation selection is complete: activity-calibrated blending reaches 0.023823 NDCG@10. The retrieval representation is now explicitly 49-dimensional—48 neural coordinates plus one precomputable popularity coordinate—and ranking is implemented as a single exact dot product between tower outputs. I’m refitting that selected setup on train plus validation now. ### exec · 2026-08-31T18:46:22.951Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py --stage final --epochs 60 --dim 48 --history-length 192 --batch-size 384 --seed 20260831 --blend -1 2>&1 | tee /app/solution/train_final.log\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000,tty:true});\nif (r.session_id) { text(JSON.stringify({session_id:r.session_id, output:r.output})); } else { text(r.output); }\n" } ### Tool output · 2026-08-31T18:46:33.972Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:46:35.864Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:36.649Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:46:53.133Z Script completed Wall time 16.5 seconds Output: {"session_id":35858,"output":"{\"stage\": \"final\", \"users\": 8456, \"items\": 30000, \"history_shape\": [8456, 192], \"positive_pairs\": 2661891}\r\n{\"epoch\": 1, \"steps\": 21, \"loss\": 6.2371, \"scale\": 9.688}\r\n{\"epoch\": 10, \"steps\": 21, \"loss\": 5.78441, \"scale\": 9.625}\r\n"} ## Step 66 · agent · 2026-08-31T18:46:54.595Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:55.978Z { "code": "const r = await tools.write_stdin({session_id:35858,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:47:07.006Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:47:10.800Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:11.659Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:47:18.596Z Script completed Wall time 6.9 seconds Output: {"epoch": 20, "steps": 21, "loss": 5.43837, "scale": 10.033} {"epoch": 30, "steps": 21, "loss": 5.20693, "scale": 10.922} {"epoch": 40, "steps": 21, "loss": 5.01833, "scale": 12.108} {"epoch": 50, "steps": 21, "loss": 4.92044, "scale": 13.381} {"epoch": 60, "steps": 21, "loss": 4.79246, "scale": 14.798} wrote /app/solution/submission.parquet ## Step 68 · agent · 2026-08-31T18:47:34.511Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:07.979Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower ranker\\n+\\n+`train_ranker.py` implements the final model. It streams only catalog events,\\n+aggregates them into bounded sparse histories, and never constructs or\\n+decomposes a user-item interaction matrix.\\n+\\n+## Architecture\\n+\\n+- **User tower:** a trainable history-item embedding, a 3-feature neural gate\\n+ (recency, repeat strength, and organic/active-like strength), weighted\\n+ pooling over the latest 192 distinct positive interactions, and a residual\\n+ two-layer MLP. There is no user-ID embedding.\\n+- **Item tower:** a separate trainable item embedding and residual two-layer\\n+ MLP. Candidate outputs are computed independently of users.\\n+- Both latent outputs are L2-normalized. The production vectors have 49\\n+ coordinates: 48 neural coordinates plus one recent-popularity bias\\n+ coordinate. The user tower emits the corresponding constant query\\n+ coordinate. The final score in `write_rankings` is exactly one dot product;\\n+ candidate vectors can therefore be built offline and placed in a MIPS index.\\n+ A history-activity quartile controls only the user-side latent scale.\\n+\\n+## Training and validation\\n+\\n+Training samples one strong organic completion (80%+) or organic like from a\\n+user, removes that item from the bounded input history, and optimizes in-batch\\n+sampled-softmax cross-entropy. Organic likes are sampled at 3x weight. AdamW is\\n+used for 60 epochs on CPU.\\n+\\n+Model selection used only `train_*` as history and `val_*` as outcomes. The\\n+selected activity-calibrated model scores **0.023823 public NDCG@10** (4,263\\n+scored users). `validation.parquet` preserves that valid ranking. The final\\n+checkpoint was refit from scratch on `train_* + val_*`; validation events are\\n+therefore history only for the hidden-test ranking.\\n+\\n+## Final command\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_ranker.py \\\\\\n+ --stage final --epochs 60 --dim 48 --history-length 192 \\\\\\n+ --batch-size 384 --seed 20260831 --blend -1\\n+```\\n+\\n+This writes `/app/solution/submission.parquet` with the required columns and\\n+100 cutoff-eligible items per target user.\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"cp /app/solution/validation_adaptive.parquet /app/solution/validation.parquet\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\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'); p=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(pd.read_parquet(p),targets)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=sum(not e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(f),'users':f.uid.nunique(),'min_rank':int(f['rank'].min()),'max_rank':int(f['rank'].max()),'ineligible':bad,'duplicate_pairs':int(f.duplicated(['uid','item_id']).sum()),'dtypes':{c:str(t) for c,t in f.dtypes.items()}})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/model_final.pt /app/solution/validation.parquet /app/solution/train_ranker.py /app/solution/README.md\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(a);\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:48:14.091Z Script completed Wall time 6.1 seconds Output: {} { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.023823, "quality_users": 4263 } } {'rows': 845600, 'users': 8456, 'min_rank': 1, 'max_rank': 100, 'ineligible': 0, 'duplicate_pairs': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}} -rw-r--r-- 1 agent agent 2.1K Aug 31 18:48 /app/solution/README.md -rw-r--r-- 1 agent agent 12M Aug 31 18:47 /app/solution/model_final.pt -rw-r--r-- 1 agent agent 1.6M Aug 31 18:47 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 15K Aug 31 18:46 /app/solution/train_ranker.py -rw-r--r-- 1 agent agent 1.6M Aug 31 18:48 /app/solution/validation.parquet ## Step 69 · agent · 2026-08-31T18:48:17.184Z ### Thinking unavailable — no text recorded The final refit and verification are complete. The submission has 845,600 rows for 8,456 users, ranks 1–100 exactly once per user, no duplicate pairs, and zero test-ineligible items. The preserved validation artifact independently re-scores at 0.023823 NDCG@10. ### exec · 2026-08-31T18:48:24.965Z { "code": "const p = await tools.update_plan({explanation:\"Final train+validation refit completed; submission, checkpoint, source, README, public score, and eligibility/schema checks all passed.\",plan:[\n {step:\"Inspect data, schemas, sizes, and evaluation helpers\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and compare validation variants; save best valid ranking\",status:\"completed\"},\n {step:\"Refit on train + validation and generate test submission\",status:\"completed\"},\n {step:\"Validate artifacts and write concise README\",status:\"completed\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T18:48:25.032Z Script completed Wall time 0.0 seconds Output: {} ## Step 70 · agent · 2026-08-31T18:48:26.986Z ### Thinking unavailable — no text recorded Completed and validated. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training pipeline: [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) - Preserved validation ranking: [validation.parquet](/app/solution/validation.parquet) Public validation NDCG@10: **0.023823** Submission checks passed: 8,456 users, exactly 100 ranks each, no duplicate items, and zero ineligible candidates.