yambda-discovery-ranking-two-tower-base / run-003-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:22.521Z 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 · 2026-08-31T18:25:24.305Z ## 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 · 2026-08-31T18:25:24.305Z 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 · 2026-08-31T18:25:24.305Z 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 · 2026-08-31T18:25:24.305Z 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-a051-7523-b366-dac73a0d01bf", "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 · 2026-08-31T18:25:24.314Z # 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 · 2026-08-31T18:25:27.521Z ### Thinking unavailable — no text recorded I’ll build and validate the complete two-tower pipeline in `/app/solution/`, beginning with data profiling and a fast reproducible baseline. I’ll preserve the best valid ranking as I iterate, then refit on train+validation and generate the cutoff-correct test submission. ### exec · 2026-08-31T18:25:37.432Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data, evaluator, and environment\",status:\"in_progress\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"pending\"},\n {step:\"Train and tune against public validation\",status:\"pending\"},\n {step:\"Refit on train plus validation and generate final submission\",status:\"pending\"},\n {step:\"Verify schema, eligibility, reproducibility, and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,280p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:25:37.505Z Script completed Wall time 0.1 seconds Output: /app /task/evaluate_public.py /task/ranking_evaluation.py /task/requirements.txt /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/train_undislikes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/val_undislikes.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/val_dislikes.parquet /task/data/train_likes.parquet /task/data/val_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/validation_preference_state.parquet #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-08-31T18:25:40.458Z ### Thinking unavailable — no text recorded The scoring contract is clear: only the first 10 positions affect quality, but all 100 rows must remain eligible and structurally valid. I’m profiling event scale, catalog coverage, temporal density, and candidate-mask sparsity now; those determine whether sampled-softmax, sequence aggregation, and explicit-state features are the best CPU tradeoff. ### exec · 2026-08-31T18:25:56.513Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, os\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print(p.name, 'shape', d.shape, 'memMB', round(d.memory_usage(deep=True).sum()/1e6,1))\\n print(' dtypes', {k:str(v) for k,v in d.dtypes.items()})\\n if 'uid' in d: print(' users',d.uid.nunique(), 'items',d.item_id.nunique() if 'item_id' in d else '', 'ts', (int(d.timestamp.min()),int(d.timestamp.max())) if 'timestamp' in d and len(d) else '')\\n if p.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n print(d.head(), '\\\\n')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n print(n,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print(' eligibility counts',np.quantile(counts,[0,.1,.5,.9,1]),'mean',counts.mean())\\nprint('cpu',os.cpu_count())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:25:56.650Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-08-31T18:25:59.183Z ### Thinking unavailable — no text recorded The default shell’s `python` is outside the task’s pinned environment and lacks the required packages. I’m locating the provided runtime rather than modifying the environment or installing anything. ### exec · 2026-08-31T18:26:04.983Z { "code": "const r = await tools.exec_command({cmd:\"command -v python; command -v python3; command -v uv || true; find /app /opt /usr/local -maxdepth 4 -type f -name 'python*' 2>/dev/null | sed -n '1,100p'; ls -la /app /task | sed -n '1,120p'; env | sort | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:26:05.055Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/local/lib/pkgconfig/python-3.12.pc /usr/local/lib/pkgconfig/python-3.12-embed.pc /usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python.o /usr/local/lib/python3.12/config-3.12-x86_64-linux-gnu/python-config.py /usr/local/bin/python3.12-config /usr/local/bin/python3.12 /usr/local/share/man/man1/python3.12.1 /usr/local/include/python3.12/cpython/pythonrun.h /usr/local/include/python3.12/pythonrun.h /app: total 12 drwxr-xr-x 3 agent agent 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. drwxr-xr-x 2 agent agent 4096 Aug 31 18:22 solution /task: 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-a03b-77b0-8377-4cc0db2ee879 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=82746f46db2f LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ## Step 10 · agent · 2026-08-31T18:26:08.355Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:11.810Z { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name pandas -o -name torch \\\\) 2>/dev/null | sed -n '1,120p'; python -m pip list --format=columns | sed -n '1,120p'; ls -la /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:26:12.790Z Script completed Wall time 1.0 seconds Output: /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/pandas /opt/task-venv/lib/python3.12/site-packages/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch Package Version ------- ------- pip 25.0.1 total 8 drwxr-xr-x 2 agent agent 4096 Aug 31 18:22 . drwxr-xr-x 3 agent agent 4096 Aug 31 18:22 .. ## Step 11 · agent · 2026-08-31T18:26:14.913Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:28.903Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, os\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print(p.name, 'shape', d.shape, 'memMB', round(d.memory_usage(deep=True).sum()/1e6,1))\\n print(' dtypes', {k:str(v) for k,v in d.dtypes.items()})\\n if 'uid' in d: print(' users',d.uid.nunique(), 'items',d.item_id.nunique() if 'item_id' in d else '', 'ts', (int(d.timestamp.min()),int(d.timestamp.max())) if 'timestamp' in d and len(d) else '')\\n if p.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n print(d.head(), '\\\\n')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n print(n,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print(' eligibility counts',np.quantile(counts,[0,.1,.5,.9,1]),'mean',counts.mean())\\nprint('cpu',os.cpu_count())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:26:32.715Z Script completed Wall time 3.8 seconds Output: candidate_catalog.parquet shape (30000, 4) memMB 0.7 dtypes {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'} item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 target_users.parquet shape (8456, 3) memMB 0.2 dtypes {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'} users 8456 items ts uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 test_preference_state.parquet shape (484776, 3) memMB 30.2 dtypes {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} users 7092 items 28863 ts uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked train_dislikes.parquet shape (101075, 4) memMB 1.3 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 5732 items 50876 ts (75, 24786580) train_likes.parquet shape (814831, 4) memMB 10.6 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 8144 items 172303 ts (80, 24786790) train_listens.parquet shape (43484159, 6) memMB 826.2 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} users 9238 items 845395 ts (0, 24786795) train_undislikes.parquet shape (19960, 4) memMB 0.3 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 2769 items 14725 ts (15950, 24785395) train_unlikes.parquet shape (292665, 4) memMB 3.8 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 6197 items 113072 ts (250, 24786570) val_dislikes.parquet shape (3563, 4) memMB 0.0 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 955 items 3135 ts (24789920, 25393280) val_likes.parquet shape (37795, 4) memMB 0.5 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 4114 items 22902 ts (24788815, 25393390) val_listens.parquet shape (1501697, 6) memMB 28.5 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'} users 8196 items 191747 ts (24788600, 25393395) val_undislikes.parquet shape (606, 4) memMB 0.0 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 205 items 579 ts (24810715, 25393035) val_unlikes.parquet shape (9346, 4) memMB 0.1 dtypes {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'} users 1313 items 8080 ts (24789005, 25393385) validation_preference_state.parquet shape (469124, 3) memMB 29.2 dtypes {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'} users 7038 items 28814 ts uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligibility counts [24657. 28038.5 29242. 29852. 30000. ] mean 29067.306291390727 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligibility counts [24552. 28001. 29220. 29835. 30000.] mean 29041.3178807947 cpu 8 ## Step 12 · agent · 2026-08-31T18:26:52.305Z ### Thinking unavailable — no text recorded ## Step 13 · agent · 2026-08-31T18:27:10.956Z ### Thinking unavailable — no text recorded ## Step 14 · agent · 2026-08-31T18:27:16.043Z ### Thinking unavailable — no text recorded The dataset has 43.5M training listens but only 30K retrieval items and 8,456 target users. I’ll stream-filter events rather than materializing any user–item matrix. As a calibration point, I’m scoring an eligibility-filtered popularity ranking and measuring how much validation relevance repeats prior behavior; that will tell me how aggressively the learned tower should emphasize personal history versus item priors. ### exec · 2026-08-31T18:27:57.705Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/profile_and_baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Small read-only profiler and eligible popularity baseline.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def load_eligibility(name):\\n+ with np.load(D / name) as z:\\n+ return z[\\\"uids\\\"].astype(np.int64), z[\\\"item_ids\\\"].astype(np.int64), np.unpackbits(\\n+ z[\\\"packed_eligible\\\"], axis=1, count=len(z[\\\"item_ids\\\"]), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+\\n+\\n+def write_popularity():\\n+ uids, items, eligible = load_eligibility(\\\"validation_eligible_candidates.npz\\\")\\n+ # Candidate archive order is catalog popularity order, but make this explicit.\\n+ cat = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+ pop = dict(zip(cat.item_id.astype(int), cat.popularity_rank.astype(int)))\\n+ order = np.argsort(np.array([pop[int(x)] for x in items]))\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ chosen = order[eligible[r, order]][:100]\\n+ rows.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": items[chosen], \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / \\\"validation_popularity.parquet\\\", index=False)\\n+\\n+\\n+def truth_and_overlap():\\n+ uids, items, eligible = load_eligibility(\\\"validation_eligible_candidates.npz\\\")\\n+ uid_pos = {int(x): i for i, x in enumerate(uids)}\\n+ item_pos = {int(x): i for i, x in enumerate(items)}\\n+ strong = pd.read_parquet(D / \\\"val_listens.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ strong = strong[(strong.is_organic == 1) & (strong.played_ratio_pct >= 80)]\\n+ likes = pd.read_parquet(D / \\\"val_likes.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ likes = likes[likes.is_organic == 1]\\n+ truth = set()\\n+ for df in (strong, likes):\\n+ for u, i in df[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().itertuples(index=False, name=None):\\n+ ur, ic = uid_pos.get(int(u)), item_pos.get(int(i))\\n+ if ur is not None and ic is not None and eligible[ur, ic]: truth.add((int(u), int(i)))\\n+ print(\\\"eligible truth pairs/users\\\", len(truth), len({u for u, _ in truth}))\\n+ remain = set(truth)\\n+ seen_pos, seen_strong, seen_org = set(), set(), set()\\n+ pf = pq.ParquetFile(D / \\\"train_listens.parquet\\\")\\n+ for batch in pf.iter_batches(batch_size=1_000_000, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]):\\n+ x = batch.to_pandas()\\n+ x = x[x.uid.isin(uid_pos) & x.item_id.isin(item_pos)]\\n+ if len(x):\\n+ pairs = list(x[[\\\"uid\\\", \\\"item_id\\\"]].itertuples(index=False, name=None))\\n+ ratio = x.played_ratio_pct.to_numpy()\\n+ org = x.is_organic.to_numpy()\\n+ for k, pair in enumerate(pairs):\\n+ p = (int(pair[0]), int(pair[1]))\\n+ if p in remain:\\n+ if ratio[k] >= 50: seen_pos.add(p)\\n+ if ratio[k] >= 80: seen_strong.add(p)\\n+ if org[k] and ratio[k] >= 50: seen_org.add(p)\\n+ print(\\\"truth repeat positive/strong/organic\\\", len(seen_pos), len(seen_strong), len(seen_org))\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ write_popularity()\\n+ truth_and_overlap()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T18:27:57.787Z Script completed Wall time 0.0 seconds Output: {} ## Step 15 · agent · 2026-08-31T18:28:00.539Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:03.404Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/profile_and_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:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:28:14.425Z Script running with cell ID 7 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-08-31T18:28:16.885Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:17.664Z { "cell_id": "7", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:28:32.508Z Script completed Wall time 14.8 seconds Output: {"chunk_id":"ba3b93","wall_time_seconds":28.922263601,"exit_code":0,"original_token_count":55,"output":"eligible truth pairs/users 37769 4263\ntruth repeat positive/strong/organic 0 0 0\n{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n"} ## Step 17 · agent · 2026-08-31T18:28:53.866Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-08-31T18:29:12.512Z ### Thinking unavailable — no text recorded ## Step 19 · agent · 2026-08-31T18:29:19.761Z ### Thinking unavailable — no text recorded The key finding is that eligible validation positives never repeat a prior positive listen, so memorizing consumed tracks is useless and eligibility already enforces discovery. The model must transfer taste across items. I’m therefore using temporal next-item training: a history encoder learns from older catalog interactions, while a separate candidate encoder learns to retrieve first-time organic strong-listen/like items in later weekly windows using sampled softmax. No dense or decomposed global interaction matrix is involved. ### exec · 2026-08-31T18:29:35.252Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nitems=np.sort(pd.read_parquet('/task/data/candidate_catalog.parquet',columns=['item_id']).item_id.to_numpy())\\npf=pq.ParquetFile('/task/data/train_listens.parquet')\\ntot=np.zeros(8,dtype=np.int64); t=time.time()\\nfor b in pf.iter_batches(batch_size=2_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n x=b.to_pandas(); inc=np.isin(x.item_id.to_numpy(),items); pos=x.played_ratio_pct.to_numpy()>=50; strong=x.played_ratio_pct.to_numpy()>=80; org=x.is_organic.to_numpy()==1\\n tot += [len(x),inc.sum(),(inc&pos).sum(),(inc&strong).sum(),(inc&strong&org).sum(),(inc&pos&(x.timestamp.to_numpy()>=24786800-7776000)).sum(),(inc&pos&(x.timestamp.to_numpy()>=24786800-2419200)).sum(),(inc&pos&(x.timestamp.to_numpy()>=24786800-604800)).sum()]\\nprint(tot,'sec',time.time()-t)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:29:37.941Z Script completed Wall time 2.7 seconds Output: [43484159 31249613 20056329 18801138 8699160 7755443 2608539 674579] sec 1.0982708930969238 ## Step 20 · agent · 2026-08-31T18:29:43.822Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:50.494Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nfor f in ['train_listens.parquet','train_likes.parquet']:\\n p=pq.ParquetFile('/task/data/'+f); print(f,p.num_row_groups)\\n for i in list(range(min(4,p.num_row_groups)))+list(range(max(4,p.num_row_groups-4),p.num_row_groups)):\\n rg=p.metadata.row_group(i); ci=p.schema.names.index('timestamp'); s=rg.column(ci).statistics\\n print(i,rg.num_rows,s.min,s.max)\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});text(rest);text(output);" } ### Tool output · 2026-08-31T18:29:50.984Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"db5840","wall_time_seconds":0.3121892,"exit_code":0,"original_token_count":64} train_listens.parquet 42 0 1048576 15 24786690 1 1048576 185 24786725 2 1048576 15320 24786790 3 1048576 3625 24786795 38 1048576 190 24786705 39 1048576 10 24786760 40 1048576 230 24786710 41 492543 20 24786690 train_likes.parquet 1 0 814831 80 24786790 ## Step 21 · agent · 2026-08-31T18:29:55.037Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:59.965Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nfor f in ['train_listens.parquet','train_likes.parquet']:\\n p=pq.read_table('/task/data/'+f,columns=['uid','timestamp']).slice(0,30).to_pandas();print(f);print(p.to_string(index=False));print('monos',p.uid.is_monotonic_increasing,p.timestamp.is_monotonic_increasing)\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:30:01.844Z Script completed Wall time 1.9 seconds Output: train_listens.parquet uid timestamp 100 39420 100 39420 100 39625 100 40110 100 40360 100 40380 100 40640 100 40785 100 40885 100 41130 100 41545 100 41580 100 41580 100 42115 100 42115 100 42115 100 42115 100 42115 100 42115 100 42210 100 42380 100 42520 100 43425 100 43425 100 43425 100 43425 100 43425 100 43720 100 43720 100 43910 monos True True train_likes.parquet uid timestamp 100 44755 100 1155860 100 1259125 100 1260005 100 1263935 100 2055015 100 2494620 100 3939350 100 3940755 100 6902675 100 6903355 100 7035995 100 10314390 100 18304655 100 23471875 100 23540595 100 23865945 100 24148190 100 24642765 300 54135 300 54285 300 54325 300 944000 300 944170 300 944215 300 1866435 300 1866480 300 1866495 300 1866885 300 2575485 monos True False ## Step 22 · agent · 2026-08-31T18:30:21.502Z ### Thinking unavailable — no text recorded ## Step 23 · agent · 2026-08-31T18:30:40.136Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-08-31T18:30:58.793Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T18:31:16.525Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:29.152Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data/'\\nz=np.load(D+'validation_eligible_candidates.npz'); us={int(x):r for r,x in enumerate(z['uids'])}; its={int(x):c for c,x in enumerate(z['item_ids'])}; pack=z['packed_eligible']\\ndef ok(u,i):\\n r=us.get(int(u));c=its.get(int(i));return r is not None and c is not None and ((int(pack[r,c//8])>>(c%8))&1)\\nl=pd.read_parquet(D+'val_listens.parquet'); l=l[(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nk=pd.read_parquet(D+'val_likes.parquet');k=k[k.is_organic==1][['uid','item_id']].drop_duplicates()\\nls={(int(u),int(i)) for u,i in l.itertuples(index=False,name=None) if ok(u,i)}\\nks={(int(u),int(i)) for u,i in k.itertuples(index=False,name=None) if ok(u,i)}\\nprint('strong',len(ls),'likes',len(ks),'both',len(ls&ks),'union',len(ls|ks),'users strong like',len({u for u,i in ls}),len({u for u,i in ks}))\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:31:31.135Z Script completed Wall time 2.0 seconds Output: strong 35597 likes 4417 both 2245 union 37769 users strong like 4042 1431 ## Step 26 · agent · 2026-08-31T18:31:51.805Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-08-31T18:32:10.455Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:32:29.126Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:32:47.769Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:32:55.181Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:03.458Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data/'; z=np.load(D+'validation_eligible_candidates.npz'); us=set(map(int,z['uids'])); its=set(map(int,z['item_ids']))\\nl=pd.read_parquet(D+'val_listens.parquet'); l=l[(l.uid.isin(us))&(l.item_id.isin(its))&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nk=pd.read_parquet(D+'val_likes.parquet'); k=k[(k.uid.isin(us))&(k.item_id.isin(its))&(k.is_organic==1)][['uid','item_id']].drop_duplicates()\\nprint(len(l),len(k),len(pd.concat([l,k]).drop_duplicates()))\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:1000});text(rest);text(output);" } ### Tool output · 2026-08-31T18:33:05.331Z Script completed Wall time 1.9 seconds Output: {"chunk_id":"d25a4a","wall_time_seconds":1.6963813129999998,"exit_code":0,"original_token_count":5} 170791 8528 175729 ## Step 31 · agent · 2026-08-31T18:33:12.619Z ### Thinking unavailable — no text recorded Eligibility is much stricter than simple catalog membership: only about 38K of 176K otherwise relevant catalog pairs remain. I’m checking whether prior exposure—not merely a completed play—is the exclusion rule, then I’ll mirror that rule in temporal training labels. This alignment matters more than adding model depth. ### exec · 2026-08-31T18:33:36.118Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\\nD='/task/data/';z=np.load(D+'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];ur={int(x):r for r,x in enumerate(uids)};ic={int(x):c for c,x in enumerate(items)};p=z['packed_eligible']\\ndef elig(q):\\n return {(u,i) for u,i in q if ((int(p[ur[u],ic[i]//8])>>(ic[i]%8))&1)}\\nl=pd.read_parquet(D+'val_listens.parquet');l=l[(l.uid.isin(uids))&(l.item_id.isin(items))&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nk=pd.read_parquet(D+'val_likes.parquet');k=k[(k.uid.isin(uids))&(k.item_id.isin(items))&(k.is_organic==1)][['uid','item_id']].drop_duplicates()\\nallq={(int(u),int(i)) for u,i in pd.concat([l,k]).drop_duplicates().itertuples(index=False,name=None)}; eligible=elig(allq); excluded=allq-eligible\\nseen=set()\\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=2000000,columns=['uid','item_id']):\\n x=b.to_pandas(); x=x[x.uid.isin(uids)&x.item_id.isin(items)]\\n for q in x.drop_duplicates().itertuples(index=False,name=None):\\n q=(int(q[0]),int(q[1]))\\n if q in allq:seen.add(q)\\nst=pd.read_parquet(D+'validation_preference_state.parquet');state={(int(u),int(i)) for u,i in st[['uid','item_id']].itertuples(index=False,name=None)}\\nprint('all eligible excluded',len(allq),len(eligible),len(excluded))\\nprint('prior-any all/excluded/eligible',len(seen),len(seen&excluded),len(seen&eligible))\\nprint('state all/excluded/eligible',len(state&allq),len(state&excluded),len(state&eligible))\\nprint('excluded explained union',len(excluded&(seen|state)),'unexplained',len(excluded-(seen|state)))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:33:45.113Z Script completed Wall time 9.0 seconds Output: all eligible excluded 175729 37769 137960 prior-any all/excluded/eligible 137732 137732 0 state all/excluded/eligible 56308 56308 0 excluded explained union 137952 unexplained 8 ## Step 32 · agent · 2026-08-31T18:34:04.857Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-08-31T18:34:23.530Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-08-31T18:34:42.133Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-08-31T18:34:59.008Z ### Thinking unavailable — no text recorded Prior exposure explains essentially every excluded relevant pair (137,952 of 137,960). The temporal examples will therefore predict only items unseen at each simulated cutoff, matching the platform candidate boundary. The user tower will pool recent positive-listen history plus active like/dislike channels; the item tower will combine a trainable item embedding with catalog popularity features. Both export same-dimensional vectors, and ranking remains a single dot product. ### exec · 2026-08-31T18:38:45.647Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_and_submit.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based PyTorch two-tower ranker.\\n+\\n+The program streams interaction logs and constructs bounded per-user histories.\\n+It never creates a dense user-item matrix or performs matrix factorization.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+import time\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+from torch import nn\\n+import torch.nn.functional as F\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+FINAL_CUTOFF = 25_393_400\\n+WEEK = 7 * 24 * 3600\\n+GAP = 1800\\n+HISTORY_LEN = 256\\n+LIKED_LEN = 160\\n+DISLIKED_LEN = 80\\n+MAX_LABELS = 64\\n+\\n+\\n+def seed_all(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+def make_lookup(values: np.ndarray) -> np.ndarray:\\n+ lookup = np.full(int(values.max()) + 1, -1, dtype=np.int32)\\n+ lookup[values.astype(np.int64)] = np.arange(len(values), dtype=np.int32)\\n+ return lookup\\n+\\n+\\n+def mapped(raw: np.ndarray, lookup: np.ndarray) -> np.ndarray:\\n+ result = np.full(len(raw), -1, dtype=np.int32)\\n+ valid = raw < len(lookup)\\n+ result[valid] = lookup[raw[valid].astype(np.int64)]\\n+ return result\\n+\\n+\\n+def load_listens(files: list[str], uid_lookup: np.ndarray, item_lookup: np.ndarray):\\n+ \\\"\\\"\\\"Stream catalog/target-user listen events into compact typed arrays.\\\"\\\"\\\"\\n+ chunks = [[], [], [], [], []]\\n+ for name in files:\\n+ pf = pq.ParquetFile(DATA / name)\\n+ for batch in pf.iter_batches(\\n+ batch_size=2_000_000,\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ d = batch.to_pandas()\\n+ u = mapped(d.uid.to_numpy(), uid_lookup)\\n+ i = mapped(d.item_id.to_numpy(), item_lookup)\\n+ keep = (u >= 0) & (i >= 0)\\n+ if not keep.any():\\n+ continue\\n+ ratio = d.played_ratio_pct.to_numpy()[keep]\\n+ organic = d.is_organic.to_numpy()[keep].astype(bool)\\n+ chunks[0].append(u[keep].astype(np.uint16))\\n+ chunks[1].append(i[keep].astype(np.uint16))\\n+ chunks[2].append(d.timestamp.to_numpy()[keep].astype(np.uint32))\\n+ chunks[3].append((ratio >= 50).astype(np.uint8))\\n+ # 0=no target, 1=organic >=80% discovery target.\\n+ chunks[4].append((organic & (ratio >= 80)).astype(np.uint8))\\n+ arrays = [np.concatenate(x) for x in chunks]\\n+ # Individual files are uid/time sorted. Re-sort to merge train and validation.\\n+ if len(files) > 1:\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ arrays = [x[order] for x in arrays]\\n+ return arrays\\n+\\n+\\n+def load_actions(files: list[tuple[str, int]], uid_lookup: np.ndarray, item_lookup: np.ndarray):\\n+ \\\"\\\"\\\"Load explicit channel transitions; kind: +1 like, -1 unlike, +2 dislike, -2 undo.\\\"\\\"\\\"\\n+ out = [[], [], [], [], []]\\n+ for name, kind in files:\\n+ d = pd.read_parquet(DATA / name, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ u = mapped(d.uid.to_numpy(), uid_lookup)\\n+ i = mapped(d.item_id.to_numpy(), item_lookup)\\n+ keep = (u >= 0) & (i >= 0)\\n+ out[0].append(u[keep].astype(np.uint16))\\n+ out[1].append(i[keep].astype(np.uint16))\\n+ out[2].append(d.timestamp.to_numpy()[keep].astype(np.uint32))\\n+ out[3].append(np.full(keep.sum(), kind, dtype=np.int8))\\n+ out[4].append(d.is_organic.to_numpy()[keep].astype(np.uint8))\\n+ arrays = [np.concatenate(x) for x in out]\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ return [x[order] for x in arrays]\\n+\\n+\\n+def boundaries(uid_idx: np.ndarray, n_users: int) -> np.ndarray:\\n+ return np.searchsorted(uid_idx, np.arange(n_users + 1), side=\\\"left\\\")\\n+\\n+\\n+def pack_tail(items: np.ndarray, weights: np.ndarray, length: int, pad: int):\\n+ a = np.full(length, pad, dtype=np.int32)\\n+ w = np.zeros(length, dtype=np.float32)\\n+ if len(items):\\n+ take = min(length, len(items))\\n+ a[-take:] = items[-take:].astype(np.int32)\\n+ w[-take:] = weights[-take:].astype(np.float32)\\n+ return a, w\\n+\\n+\\n+def pack_set(values: set[int], length: int, pad: int):\\n+ a = np.full(length, pad, dtype=np.int32)\\n+ if values:\\n+ # Deterministic truncation; catalog order is popularity order.\\n+ v = np.asarray(sorted(values, reverse=True), dtype=np.int32)[:length]\\n+ a[: len(v)] = v\\n+ return a\\n+\\n+\\n+def item_features(catalog: pd.DataFrame) -> np.ndarray:\\n+ pos = catalog.positive_listens.to_numpy(dtype=np.float32)\\n+ org = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\\n+ rank = catalog.popularity_rank.to_numpy(dtype=np.float32)\\n+ x = np.column_stack(\\n+ [np.log1p(pos), np.log1p(org), org / np.maximum(pos, 1), np.log1p(rank)]\\n+ ).astype(np.float32)\\n+ return ((x - x.mean(0)) / (x.std(0) + 1e-6)).astype(np.float32)\\n+\\n+\\n+def build_contexts(stage: str, windows: int = 20):\\n+ \\\"\\\"\\\"Build rolling-cutoff examples and one inference context per target user.\\\"\\\"\\\"\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\").sort_values(\\\"uid\\\")\\n+ item_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ uids = targets.uid.to_numpy(dtype=np.int64)\\n+ uid_lookup, item_lookup = make_lookup(uids), make_lookup(item_ids)\\n+ include_val = stage == \\\"final\\\"\\n+ listen_files = [\\\"train_listens.parquet\\\"] + ([\\\"val_listens.parquet\\\"] if include_val else [])\\n+ prefix = [\\\"\\\"] + ([\\\"val_\\\"] if include_val else [])\\n+ action_files = []\\n+ for p in prefix:\\n+ action_files += [\\n+ (p + \\\"likes.parquet\\\" if p else \\\"train_likes.parquet\\\", 1),\\n+ (p + \\\"unlikes.parquet\\\" if p else \\\"train_unlikes.parquet\\\", -1),\\n+ (p + \\\"dislikes.parquet\\\" if p else \\\"train_dislikes.parquet\\\", 2),\\n+ (p + \\\"undislikes.parquet\\\" if p else \\\"train_undislikes.parquet\\\", -2),\\n+ ]\\n+ print(f\\\"loading {stage} listens\\\", flush=True)\\n+ lu, li, lt, lp, lg = load_listens(listen_files, uid_lookup, item_lookup)\\n+ print(f\\\"retained {len(lu):,} catalog exposures\\\", flush=True)\\n+ au, ai, at, ak, ao = load_actions(action_files, uid_lookup, item_lookup)\\n+ lb, ab = boundaries(lu, len(uids)), boundaries(au, len(uids))\\n+ cutoff = FINAL_CUTOFF if include_val else TRAIN_CUTOFF\\n+ cutoffs = [cutoff - GAP - WEEK * k for k in range(windows, 0, -1)]\\n+\\n+ hs, hws, likes_arr, dislikes_arr, feats, label_arr, label_w = [], [], [], [], [], [], []\\n+ infer = {\\\"history\\\": [], \\\"history_w\\\": [], \\\"liked\\\": [], \\\"disliked\\\": [], \\\"features\\\": []}\\n+ pad = len(item_ids)\\n+\\n+ for user in range(len(uids)):\\n+ sl = slice(lb[user], lb[user + 1])\\n+ it, ts, pos, grade = li[sl], lt[sl], lp[sl].astype(bool), lg[sl]\\n+ sa = slice(ab[user], ab[user + 1])\\n+ ait, ats, kind, aorg = ai[sa], at[sa], ak[sa], ao[sa]\\n+ liked, disliked = set(), set()\\n+ action_cursor = 0\\n+\\n+ for hcut in cutoffs:\\n+ while action_cursor < len(ats) and ats[action_cursor] <= hcut:\\n+ x, k = int(ait[action_cursor]), int(kind[action_cursor])\\n+ if k == 1: liked.add(x)\\n+ elif k == -1: liked.discard(x)\\n+ elif k == 2: disliked.add(x)\\n+ else: disliked.discard(x)\\n+ action_cursor += 1\\n+ hist_end = np.searchsorted(ts, hcut, side=\\\"right\\\")\\n+ out_start = np.searchsorted(ts, hcut + GAP, side=\\\"left\\\")\\n+ out_end = np.searchsorted(ts, hcut + GAP + WEEK, side=\\\"left\\\")\\n+ seen = set(map(int, np.unique(it[:hist_end])))\\n+ # Strong organic listens on items eligible (unseen and no explicit state) at cutoff.\\n+ q = it[out_start:out_end][grade[out_start:out_end] > 0]\\n+ lab = {int(x): 1.0 for x in np.unique(q) if int(x) not in seen and int(x) not in liked and int(x) not in disliked}\\n+ # Organic likes in the outcome window get gain-proportional sampling weight.\\n+ lo = np.searchsorted(ats, hcut + GAP, side=\\\"left\\\")\\n+ hi = np.searchsorted(ats, hcut + GAP + WEEK, side=\\\"left\\\")\\n+ for x, k, org in zip(ait[lo:hi], kind[lo:hi], aorg[lo:hi]):\\n+ xx = int(x)\\n+ if k == 1 and org and xx not in seen and xx not in liked and xx not in disliked:\\n+ lab[xx] = 4.0\\n+ if not lab:\\n+ continue\\n+ pidx = np.flatnonzero(pos[:hist_end])\\n+ hist_items = it[pidx]\\n+ # Organic/strong events carry more signal; repeated plays naturally get repeated weight.\\n+ hist_weights = 1.0 + 0.75 * grade[pidx].astype(np.float32)\\n+ ph, pw = pack_tail(hist_items, hist_weights, HISTORY_LEN, pad)\\n+ hs.append(ph); hws.append(pw)\\n+ likes_arr.append(pack_set(liked, LIKED_LEN, pad)); dislikes_arr.append(pack_set(disliked, DISLIKED_LEN, pad))\\n+ mean_pop = float(np.mean(catalog.popularity_rank.to_numpy()[hist_items[-HISTORY_LEN:]])) if len(hist_items) else 30000.0\\n+ feats.append([math.log1p(hist_end) / 12, math.log1p(len(hist_items)) / 10,\\n+ math.log1p(len(liked)) / 6, math.log1p(len(disliked)) / 5,\\n+ math.log1p(mean_pop) / 11])\\n+ pairs = sorted(lab.items(), key=lambda z: (-z[1], z[0]))[:MAX_LABELS]\\n+ la = np.full(MAX_LABELS, pad, dtype=np.int32); lw = np.zeros(MAX_LABELS, dtype=np.float32)\\n+ la[:len(pairs)] = [x for x, _ in pairs]; lw[:len(pairs)] = [w for _, w in pairs]\\n+ label_arr.append(la); label_w.append(lw)\\n+\\n+ # Inference uses the supplied cutoff-correct preference state below.\\n+ hist_end = np.searchsorted(ts, cutoff, side=\\\"right\\\")\\n+ pidx = np.flatnonzero(pos[:hist_end]); hist_items = it[pidx]\\n+ ph, pw = pack_tail(hist_items, 1.0 + 0.75 * grade[pidx].astype(np.float32), HISTORY_LEN, pad)\\n+ infer[\\\"history\\\"].append(ph); infer[\\\"history_w\\\"].append(pw)\\n+ mean_pop = float(np.mean(catalog.popularity_rank.to_numpy()[hist_items[-HISTORY_LEN:]])) if len(hist_items) else 30000.0\\n+ infer[\\\"features\\\"].append([math.log1p(hist_end) / 12, math.log1p(len(hist_items)) / 10,\\n+ 0.0, 0.0, math.log1p(mean_pop) / 11])\\n+\\n+ pref_name = \\\"test_preference_state.parquet\\\" if include_val else \\\"validation_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pu = mapped(pref.uid.to_numpy(), uid_lookup); pi = mapped(pref.item_id.to_numpy(), item_lookup)\\n+ keep = (pu >= 0) & (pi >= 0); pref = pref.loc[keep].copy(); pref[\\\"u\\\"] = pu[keep]; pref[\\\"i\\\"] = pi[keep]\\n+ by = {(int(u), str(s)): set(map(int, g.i)) for (u, s), g in pref.groupby([\\\"u\\\", \\\"state\\\"])}\\n+ for u in range(len(uids)):\\n+ lk, dk = by.get((u, \\\"liked\\\"), set()), by.get((u, \\\"disliked\\\"), set())\\n+ infer[\\\"liked\\\"].append(pack_set(lk, LIKED_LEN, pad)); infer[\\\"disliked\\\"].append(pack_set(dk, DISLIKED_LEN, pad))\\n+ infer[\\\"features\\\"][u][2] = math.log1p(len(lk)) / 6\\n+ infer[\\\"features\\\"][u][3] = math.log1p(len(dk)) / 5\\n+\\n+ train = {\\n+ \\\"history\\\": np.asarray(hs), \\\"history_w\\\": np.asarray(hws), \\\"liked\\\": np.asarray(likes_arr),\\n+ \\\"disliked\\\": np.asarray(dislikes_arr), \\\"features\\\": np.asarray(feats, dtype=np.float32),\\n+ \\\"labels\\\": np.asarray(label_arr), \\\"label_w\\\": np.asarray(label_w, dtype=np.float32),\\n+ }\\n+ infer = {k: np.asarray(v, dtype=np.float32 if k in (\\\"history_w\\\", \\\"features\\\") else np.int32) for k, v in infer.items()}\\n+ meta = {\\\"uids\\\": uids, \\\"item_ids\\\": item_ids, \\\"item_features\\\": item_features(catalog)}\\n+ print(f\\\"built {len(hs):,} temporal query contexts\\\", flush=True)\\n+ return train, infer, meta\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.pad = n_items\\n+ self.history = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.likes = nn.Embedding(n_items + 1, dim // 2, padding_idx=n_items)\\n+ self.dislikes = nn.Embedding(n_items + 1, dim // 2, padding_idx=n_items)\\n+ self.net = nn.Sequential(nn.Linear(dim * 2 + 5, dim * 2), nn.SiLU(), nn.Linear(dim * 2, dim))\\n+\\n+ @staticmethod\\n+ def mean_embedding(table, ids, weights=None):\\n+ x = table(ids)\\n+ mask = (ids != table.padding_idx).unsqueeze(-1)\\n+ if weights is None:\\n+ weights = mask.squeeze(-1).float()\\n+ else:\\n+ weights = weights * mask.squeeze(-1)\\n+ return (x * weights.unsqueeze(-1)).sum(1) / weights.sum(1, keepdim=True).clamp_min(1.0)\\n+\\n+ def forward(self, history, history_w, liked, disliked, features):\\n+ x = torch.cat([self.mean_embedding(self.history, history, history_w),\\n+ self.mean_embedding(self.likes, liked), self.mean_embedding(self.dislikes, disliked),\\n+ features], dim=1)\\n+ return F.normalize(self.net(x), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, features: torch.Tensor):\\n+ super().__init__()\\n+ self.ids = nn.Embedding(n_items, dim)\\n+ self.register_buffer(\\\"features\\\", features)\\n+ self.net = nn.Sequential(nn.Linear(dim + features.shape[1], dim * 2), nn.SiLU(), nn.Linear(dim * 2, dim))\\n+\\n+ def forward(self, ids):\\n+ return F.normalize(self.net(torch.cat([self.ids(ids), self.features[ids]], dim=1)), dim=1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, item_feat: torch.Tensor):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, dim, item_feat)\\n+ self.logit_scale = nn.Parameter(torch.tensor(math.log(16.0)))\\n+\\n+ def score(self, user_inputs, item_ids):\\n+ \\\"\\\"\\\"Exact request-time factorization: user_vector @ item_vector.T.\\\"\\\"\\\"\\n+ u = self.user_tower(*user_inputs)\\n+ v = self.item_tower(item_ids)\\n+ return u @ v.T\\n+\\n+\\n+def to_tensors(data):\\n+ return {k: torch.from_numpy(v).long() if k not in (\\\"history_w\\\", \\\"features\\\", \\\"label_w\\\") else torch.from_numpy(v).float() for k, v in data.items()}\\n+\\n+\\n+def train_model(train, meta, args):\\n+ t = to_tensors(train); n = len(t[\\\"history\\\"]); n_items = len(meta[\\\"item_ids\\\"])\\n+ model = TwoTower(n_items, args.dim, torch.from_numpy(meta[\\\"item_features\\\"])).train()\\n+ opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=2e-5)\\n+ rng = torch.Generator().manual_seed(args.seed + 17)\\n+ for epoch in range(args.epochs):\\n+ # Gain-aware random label per temporal query, redrawn every epoch.\\n+ r = -torch.log(torch.rand(t[\\\"label_w\\\"].shape, generator=rng).clamp_min(1e-7)) / t[\\\"label_w\\\"].clamp_min(1e-8)\\n+ chosen = r.argmin(1); positives = t[\\\"labels\\\"].gather(1, chosen[:, None]).squeeze(1)\\n+ perm = torch.randperm(n, generator=rng); total = 0.0; seen = 0\\n+ for start in range(0, n, args.batch_size):\\n+ idx = perm[start:start + args.batch_size]; b = len(idx); pos = positives[idx]\\n+ neg = torch.randint(n_items, (args.negatives,), generator=rng)\\n+ candidates = torch.cat([pos, neg])\\n+ user_in = (t[\\\"history\\\"][idx], t[\\\"history_w\\\"][idx], t[\\\"liked\\\"][idx], t[\\\"disliked\\\"][idx], t[\\\"features\\\"][idx])\\n+ logits = model.score(user_in, candidates) * model.logit_scale.exp().clamp(max=100)\\n+ # Do not count another row's identical positive or an accidental sampled positive as negative.\\n+ collision = pos[:, None].eq(candidates[None, :])\\n+ collision[torch.arange(b), torch.arange(b)] = False\\n+ logits = logits.masked_fill(collision, -1e4)\\n+ loss = F.cross_entropy(logits, torch.arange(b))\\n+ opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(), 5.0); opt.step()\\n+ total += float(loss) * b; seen += b\\n+ print(f\\\"epoch {epoch+1}/{args.epochs} loss={total/seen:.4f} scale={model.logit_scale.exp().item():.2f}\\\", flush=True)\\n+ return model.eval()\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(model, infer, meta, stage: str, pop_weight: float, suffix: str = \\\"\\\"):\\n+ t = to_tensors(infer); n_items = len(meta[\\\"item_ids\\\"])\\n+ item_vec = model.item_tower(torch.arange(n_items))\\n+ # Popularity coordinate is an explicit item-tower feature and a constant user-tower coordinate.\\n+ pop = torch.from_numpy((-meta[\\\"item_features\\\"][:, 3]).astype(np.float32))\\n+ item_vec = torch.cat([item_vec, (pop_weight * pop)[:, None]], 1)\\n+ elig_name = \\\"test_eligible_candidates.npz\\\" if stage == \\\"final\\\" else \\\"validation_eligible_candidates.npz\\\"\\n+ with np.load(DATA / elig_name) as z:\\n+ eu = z[\\\"uids\\\"].astype(np.int64); eitems = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(eu, meta[\\\"uids\\\"]) and np.array_equal(eitems, meta[\\\"item_ids\\\"])\\n+ rows = []\\n+ for start in range(0, len(eu), 64):\\n+ idx = slice(start, min(start + 64, len(eu)))\\n+ user_in = (t[\\\"history\\\"][idx], t[\\\"history_w\\\"][idx], t[\\\"liked\\\"][idx], t[\\\"disliked\\\"][idx], t[\\\"features\\\"][idx])\\n+ uv = torch.cat([model.user_tower(*user_in), torch.ones(len(t[\\\"history\\\"][idx]), 1)], 1)\\n+ scores = (uv @ item_vec.T).numpy()\\n+ mask = np.unpackbits(packed[idx], axis=1, count=n_items, bitorder=\\\"little\\\").astype(bool)\\n+ scores[~mask] = -np.inf\\n+ top = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ top_scores = np.take_along_axis(scores, top, axis=1)\\n+ order = np.argsort(-top_scores, axis=1, kind=\\\"stable\\\")\\n+ top = np.take_along_axis(top, order, axis=1)\\n+ for j, u in enumerate(eu[idx]):\\n+ rows.append(pd.DataFrame({\\\"uid\\\": int(u), \\\"item_id\\\": eitems[top[j]], \\\"rank\\\": np.arange(1, 101, dtype=np.int64)}))\\n+ frame = pd.concat(rows, ignore_index=True).astype({\\\"uid\\\":\\\"int64\\\", \\\"item_id\\\":\\\"int64\\\", \\\"rank\\\":\\\"int64\\\"})\\n+ name = \\\"submission.parquet\\\" if stage == \\\"final\\\" and not suffix else f\\\"{stage}{suffix}.parquet\\\"\\n+ path = OUT / name; frame.to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+def main():\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ ap.add_argument(\\\"--epochs\\\", type=int, default=6); ap.add_argument(\\\"--dim\\\", type=int, default=64)\\n+ ap.add_argument(\\\"--batch-size\\\", type=int, default=256); ap.add_argument(\\\"--negatives\\\", type=int, default=768)\\n+ ap.add_argument(\\\"--lr\\\", type=float, default=0.002); ap.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ ap.add_argument(\\\"--windows\\\", type=int, default=20); ap.add_argument(\\\"--pop-weights\\\", default=\\\"0.0\\\")\\n+ args = ap.parse_args(); seed_all(args.seed); torch.set_num_threads(8)\\n+ started = time.time(); train, infer, meta = build_contexts(args.stage, args.windows)\\n+ model = train_model(train, meta, args)\\n+ weights = [float(x) for x in args.pop_weights.split(\\\",\\\")]\\n+ paths = [str(rank(model, infer, meta, args.stage, w, \\\"\\\" if len(weights)==1 else f\\\"_pop{w:g}\\\")) for w in weights]\\n+ torch.save({\\\"model\\\": model.state_dict(), \\\"args\\\": vars(args), \\\"item_ids\\\": meta[\\\"item_ids\\\"]}, OUT / f\\\"{args.stage}_model.pt\\\")\\n+ with open(OUT / f\\\"{args.stage}_run.json\\\", \\\"w\\\") as f:\\n+ json.dump({\\\"seconds\\\": time.time()-started, \\\"contexts\\\": len(train[\\\"history\\\"]), \\\"rankings\\\": paths}, f, indent=2)\\n+ print(\\\"wrote\\\", *paths, flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T18:38:45.679Z Script completed Wall time 0.0 seconds Output: {} ## Step 36 · agent · 2026-08-31T18:38:50.424Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:59.508Z { "code": "const p = await tools.update_plan({explanation:\"Profiling established that candidate eligibility removes prior exposures, so temporal examples now mirror the platform boundary.\",plan:[\n {step:\"Inspect data, evaluator, and environment\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"in_progress\"},\n {step:\"Train and tune against public validation\",status:\"pending\"},\n {step:\"Refit on train plus validation and generate final submission\",status:\"pending\"},\n {step:\"Verify schema, eligibility, reproducibility, and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_and_submit.py /app/solution/profile_and_baseline.py && /opt/task-venv/bin/python /app/solution/train_and_submit.py --stage validation --epochs 4 --windows 16 --dim 64 --batch-size 256 --negatives 768 --pop-weights=0,0.01,0.03,0.06\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:39:10.529Z Script running with cell ID 15 Wall time 11.0 seconds Output: ## Step 37 · agent · 2026-08-31T18:39:12.076Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:12.881Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:39:29.689Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"d5d967","wall_time_seconds":30.000616767,"session_id":72456,"original_token_count":17,"output":"loading validation listens\nretained 31,062,540 catalog exposures\n"} ## Step 38 · agent · 2026-08-31T18:39:33.312Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:34.097Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:39:34.176Z Script failed Wall time 0.0 seconds Output: Script error: exec cell 15 not found ## Step 39 · agent · 2026-08-31T18:39:36.026Z ### Thinking unavailable — no text recorded The event stream is prepared successfully: 31.1M catalog exposures were processed as a compact log, with no dense interaction table. Context construction and training are still running; once the first checkpoint finishes I’ll evaluate several popularity-coordinate weights from the same trained towers. ### exec · 2026-08-31T18:39:39.756Z { "code": "const r = await tools.write_stdin({session_id:72456,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:39:50.782Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T18:39:52.490Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:53.283Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:40:09.789Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"1339b8","wall_time_seconds":30.001693612,"session_id":72456,"original_token_count":114,"output":"built 58,521 temporal query contexts\n/app/solution/train_and_submit.py:322: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total += float(loss) * b; seen += b\nepoch 1/4 loss=6.7585 scale=14.22\nepoch 2/4 loss=6.6553 scale=13.70\nepoch 3/4 loss=6.5911 scale=13.12\n"} ## Step 41 · agent · 2026-08-31T18:40:11.451Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:13.812Z { "code": "const r = await tools.write_stdin({session_id:72456,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:40:22.517Z Script completed Wall time 8.7 seconds Output: {"chunk_id":"bc8490","wall_time_seconds":8.673338929,"exit_code":0,"original_token_count":51,"output":"epoch 4/4 loss=6.5225 scale=12.86\nwrote /app/solution/validation_pop0.parquet /app/solution/validation_pop0.01.parquet /app/solution/validation_pop0.03.parquet /app/solution/validation_pop0.06.parquet\n"} ## Step 42 · agent · 2026-08-31T18:40:24.616Z ### Thinking unavailable — no text recorded The first model finished in about two minutes end-to-end. Its sampled-softmax loss is still improving, so this is a conservative checkpoint; I’m scoring its retrieval variants now before deciding whether extra epochs or a larger temporal sample are worthwhile. ### exec · 2026-08-31T18:40:30.381Z { "code": "const cmds = [\"0\",\"0.01\",\"0.03\",\"0.06\"].map(w => tools.exec_command({cmd:`/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop${w}.parquet`,workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000}));\nconst rs=await Promise.all(cmds); rs.forEach(r=>text(r.output));" } ### Tool output · 2026-08-31T18:40:34.162Z Script completed Wall time 3.8 seconds Output: { "model": "/app/solution/validation_pop0.parquet", "scores": { "quality_ndcg@10": 0.013059, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.01.parquet", "scores": { "quality_ndcg@10": 0.015145, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.03.parquet", "scores": { "quality_ndcg@10": 0.012256, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.06.parquet", "scores": { "quality_ndcg@10": 0.010873, "quality_users": 4263 } } ## Step 43 · agent · 2026-08-31T18:40:49.197Z ### Thinking unavailable — no text recorded ## Step 44 · agent · 2026-08-31T18:41:07.867Z ### Thinking unavailable — no text recorded ## Step 45 · agent · 2026-08-31T18:41:16.401Z ### Thinking unavailable — no text recorded The learned towers improve NDCG from 0.010710 to 0.015145, with a small learned popularity coordinate helping and larger weights hurting. I’m decomposing that gain by like versus strong-listen outcomes and then tightening the training mix; grade-3 likes carry seven times the DCG gain, so their retrieval deserves explicit weighting rather than relying on aggregate loss alone. ### exec · 2026-08-31T18:41:33.878Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ranked_items,validate_ranking,score_rankings\\nfrom evaluate_public import validation_truth\\nD='/task/data'; targets=set(map(int,pd.read_parquet(D+'/target_users.parquet').uid)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); truth=validation_truth(e,targets)\\nfor f in ['validation_popularity.parquet','validation_pop0.parquet','validation_pop0.01.parquet']:\\n x=pd.read_parquet('/app/solution/'+f).sort_values(['uid','rank']); ranks={int(u):list(map(int,g.item_id)) for u,g in x.groupby('uid')}\\n users=list(truth); hit=likehit=sh=0; rr=[]\\n for u,g in truth.items():\\n top=ranks[u][:10]; h=[i for i in top if i in g]; hit+=bool(h); likehit+=any(g[i]==3 for i in h); sh+=sum(i in g for i in top); rr += [1/(top.index(i)+1) for i in h]\\n print(f,'users hit',hit/len(users),'likehit',likehit/sum(any(v==3 for v in g.values()) for g in truth.values()),'items/user',sh/len(users),'meanRRhit',np.mean(rr) if rr else 0)\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:41:37.421Z Script completed Wall time 3.5 seconds Output: validation_popularity.parquet users hit 0.053718038939713816 likehit 0.019566736547868623 items/user 0.08632418484635233 meanRRhit 0.360348731884058 validation_pop0.parquet users hit 0.0602861834388928 likehit 0.037037037037037035 items/user 0.07623739150832747 meanRRhit 0.419079365079365 validation_pop0.01.parquet users hit 0.07295331925873798 likehit 0.04122990915443746 items/user 0.09758386113065916 meanRRhit 0.4087749923687424 ## Step 46 · agent · 2026-08-31T18:41:48.050Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:56.388Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_submit.py\\n@@\\n- lab[xx] = 4.0\\n+ # Match the evaluator's 2^grade-1 gain (7 versus 1).\\n+ lab[xx] = 7.0\\n@@\\n- total += float(loss) * b; seen += b\\n+ total += loss.detach().item() * b; seen += b\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_and_submit.py --stage validation --epochs 10 --windows 20 --dim 96 --batch-size 256 --negatives 1024 --lr 0.0015 --pop-weights=0.004,0.008,0.012,0.016,0.022\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:42:07.410Z Script running with cell ID 20 Wall time 11.0 seconds Output: {} ## Step 47 · agent · 2026-08-31T18:42:09.097Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:09.916Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:26.575Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"8c0e07","wall_time_seconds":30.001568088,"session_id":87933,"original_token_count":17,"output":"loading validation listens\nretained 31,062,540 catalog exposures\n"} ## Step 48 · agent · 2026-08-31T18:42:28.336Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:29.760Z { "code": "const r = await tools.write_stdin({session_id:87933,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:42:40.781Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 49 · agent · 2026-08-31T18:42:42.553Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:43.467Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:59.797Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"6db0e8","wall_time_seconds":30.001923442,"session_id":87933,"original_token_count":10,"output":"built 70,727 temporal query contexts\n"} ## Step 50 · agent · 2026-08-31T18:43:01.498Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:02.973Z { "code": "const r = await tools.write_stdin({session_id:87933,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:43:14.016Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:43:15.837Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:16.689Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:43:33.024Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"12d289","wall_time_seconds":30.00108308,"session_id":87933,"original_token_count":27,"output":"epoch 1/10 loss=6.9358 scale=14.44\nepoch 2/10 loss=6.8214 scale=13.93\nepoch 3/10 loss=6.7487 scale=13.64\n"} ## Step 52 · agent · 2026-08-31T18:43:34.784Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:36.150Z { "code": "const r = await tools.write_stdin({session_id:87933,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:43:47.189Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 53 · agent · 2026-08-31T18:43:49.033Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:49.812Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:06.206Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"2e8003","wall_time_seconds":30.001682154,"session_id":87933,"original_token_count":27,"output":"epoch 4/10 loss=6.6761 scale=13.47\nepoch 5/10 loss=6.5959 scale=13.34\nepoch 6/10 loss=6.5102 scale=13.31\n"} ## Step 54 · agent · 2026-08-31T18:44:07.753Z ### Thinking unavailable — no text recorded The longer 96-dimensional run is learning steadily (loss 6.94 → 6.51 by epoch 6), and it uses 70,727 rolling query contexts. I’ve also aligned grade-3 sampling weight exactly with the evaluator’s gain of 7. I’ll let this checkpoint finish, score the narrow blend grid, and retain whichever validation file is genuinely best. ### exec · 2026-08-31T18:44:12.354Z { "code": "const r = await tools.write_stdin({session_id:87933,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:44:23.423Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 55 · agent · 2026-08-31T18:44:25.799Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:26.139Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:42.427Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"8afbc9","wall_time_seconds":30.001894976,"session_id":87933,"original_token_count":36,"output":"epoch 7/10 loss=6.4153 scale=13.45\nepoch 8/10 loss=6.3069 scale=13.89\nepoch 9/10 loss=6.2012 scale=14.45\nepoch 10/10 loss=6.0831 scale=15.30\n"} ## Step 56 · agent · 2026-08-31T18:44:44.987Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:45.767Z { "code": "const r = await tools.write_stdin({session_id:87933,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:44:56.347Z Script completed Wall time 10.6 seconds Output: {"chunk_id":"85f42b","wall_time_seconds":10.546959989,"exit_code":0,"original_token_count":54,"output":"wrote /app/solution/validation_pop0.004.parquet /app/solution/validation_pop0.008.parquet /app/solution/validation_pop0.012.parquet /app/solution/validation_pop0.016.parquet /app/solution/validation_pop0.022.parquet\n"} ## Step 57 · agent · 2026-08-31T18:44:58.653Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:02.206Z { "code": "const ws=[\"0.004\",\"0.008\",\"0.012\",\"0.016\",\"0.022\"];\nconst rs=await Promise.all(ws.map(w=>tools.exec_command({cmd:`/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop${w}.parquet`,workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000})));\nrs.forEach(r=>text(r.output));" } ### Tool output · 2026-08-31T18:45:07.267Z Script completed Wall time 5.1 seconds Output: { "model": "/app/solution/validation_pop0.004.parquet", "scores": { "quality_ndcg@10": 0.010644, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.008.parquet", "scores": { "quality_ndcg@10": 0.0118, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.012.parquet", "scores": { "quality_ndcg@10": 0.012728, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.016.parquet", "scores": { "quality_ndcg@10": 0.013634, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.022.parquet", "scores": { "quality_ndcg@10": 0.014932, "quality_users": 4263 } } ## Step 58 · agent · 2026-08-31T18:45:27.179Z ### Thinking unavailable — no text recorded ## Step 59 · agent · 2026-08-31T18:45:45.838Z ### Thinking unavailable — no text recorded ## Step 60 · agent · 2026-08-31T18:45:51.496Z ### Thinking unavailable — no text recorded The 10-epoch model overfit: its raw personalization weakened even as training loss improved. I’m retaining the earlier 0.015145 checkpoint as the best valid result. One targeted ablation remains promising: restricting the pooled history to organic ≥80% plays, which matches the discovery label and avoids drowning taste signal in recommendation-surface feedback. ### exec · 2026-08-31T18:46:03.901Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_submit.py\\n@@\\n- pidx = np.flatnonzero(pos[:hist_end])\\n+ # The objective is organic discovery; high-confidence organic history\\n+ # is a substantially cleaner taste signal than surface-driven plays.\\n+ pidx = np.flatnonzero(grade[:hist_end] > 0)\\n@@\\n- pidx = np.flatnonzero(pos[:hist_end]); hist_items = it[pidx]\\n+ pidx = np.flatnonzero(grade[:hist_end] > 0); hist_items = it[pidx]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_and_submit.py --stage validation --epochs 5 --windows 16 --dim 64 --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0,0.005,0.01,0.015,0.02,0.03\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:46:14.922Z Script running with cell ID 27 Wall time 11.0 seconds Output: {} ## Step 61 · agent · 2026-08-31T18:46:16.693Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:17.475Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:46:34.088Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"a95a87","wall_time_seconds":30.001503873,"session_id":25485,"original_token_count":17,"output":"loading validation listens\nretained 31,062,540 catalog exposures\n"} ## Step 62 · agent · 2026-08-31T18:46:35.671Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:37.065Z { "code": "const r = await tools.write_stdin({session_id:25485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:46:48.083Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 63 · agent · 2026-08-31T18:46:49.916Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:50.841Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:07.094Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"4dff38","wall_time_seconds":30.001108838,"session_id":25485,"original_token_count":27,"output":"built 58,521 temporal query contexts\nepoch 1/5 loss=6.7623 scale=14.23\nepoch 2/5 loss=6.6620 scale=13.70\n"} ## Step 64 · agent · 2026-08-31T18:47:09.731Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:11.148Z { "code": "const r = await tools.write_stdin({session_id:25485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:47:22.184Z Script running with cell ID 29 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:47:24.253Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:25.065Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:39.649Z Script completed Wall time 14.6 seconds Output: {"chunk_id":"b01dff","wall_time_seconds":28.458671463,"exit_code":0,"original_token_count":89,"output":"epoch 3/5 loss=6.5952 scale=13.13\nepoch 4/5 loss=6.5212 scale=12.86\nepoch 5/5 loss=6.4422 scale=12.66\nwrote /app/solution/validation_pop0.parquet /app/solution/validation_pop0.005.parquet /app/solution/validation_pop0.01.parquet /app/solution/validation_pop0.015.parquet /app/solution/validation_pop0.02.parquet /app/solution/validation_pop0.03.parquet\n"} ## Step 66 · agent · 2026-08-31T18:47:41.802Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:45.385Z { "code": "const ws=[\"0\",\"0.005\",\"0.01\",\"0.015\",\"0.02\",\"0.03\"];\nconst rs=await Promise.all(ws.map(w=>tools.exec_command({cmd:`/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop${w}.parquet`,workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000})));\nrs.forEach(r=>text(r.output));" } ### Tool output · 2026-08-31T18:47:50.940Z Script completed Wall time 5.5 seconds Output: { "model": "/app/solution/validation_pop0.parquet", "scores": { "quality_ndcg@10": 0.008209, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.005.parquet", "scores": { "quality_ndcg@10": 0.010288, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.01.parquet", "scores": { "quality_ndcg@10": 0.012851, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.015.parquet", "scores": { "quality_ndcg@10": 0.013438, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.02.parquet", "scores": { "quality_ndcg@10": 0.013039, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.03.parquet", "scores": { "quality_ndcg@10": 0.01181, "quality_users": 4263 } } ## Step 67 · agent · 2026-08-31T18:48:01.884Z ### Thinking unavailable — no text recorded Organic-only history underperformed (best 0.013438), so the earlier result remains the selected approach: mixed completed-play history with extra organic weight. I’m making one final low-risk refinement—retain all ≥50% plays but increase the organic contribution—then I’ll lock the validation choice and refit on train+validation. ### exec · 2026-08-31T18:48:21.370Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_submit.py\\n@@\\n- # Match the evaluator's 2^grade-1 gain (7 versus 1).\\n- lab[xx] = 7.0\\n+ # Oversample scarce grade-3 actions without letting them\\n+ # overwhelm the much broader strong-listen signal.\\n+ lab[xx] = 4.0\\n@@\\n- # The objective is organic discovery; high-confidence organic history\\n- # is a substantially cleaner taste signal than surface-driven plays.\\n- pidx = np.flatnonzero(grade[:hist_end] > 0)\\n+ pidx = np.flatnonzero(pos[:hist_end])\\n@@\\n- hist_weights = 1.0 + 0.75 * grade[pidx].astype(np.float32)\\n+ hist_weights = 1.0 + 2.0 * grade[pidx].astype(np.float32)\\n@@\\n- pidx = np.flatnonzero(grade[:hist_end] > 0); hist_items = it[pidx]\\n- ph, pw = pack_tail(hist_items, 1.0 + 0.75 * grade[pidx].astype(np.float32), HISTORY_LEN, pad)\\n+ pidx = np.flatnonzero(pos[:hist_end]); hist_items = it[pidx]\\n+ ph, pw = pack_tail(hist_items, 1.0 + 2.0 * grade[pidx].astype(np.float32), HISTORY_LEN, pad)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_and_submit.py --stage validation --epochs 4 --windows 16 --dim 64 --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.006,0.009,0.012,0.015,0.018\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:48:32.390Z Script running with cell ID 31 Wall time 11.0 seconds Output: {} ## Step 68 · agent · 2026-08-31T18:48:34.291Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:34.838Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:51.555Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"9202a0","wall_time_seconds":30.001593278,"session_id":36407,"original_token_count":17,"output":"loading validation listens\nretained 31,062,540 catalog exposures\n"} ## Step 69 · agent · 2026-08-31T18:48:53.437Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:54.859Z { "code": "const r = await tools.write_stdin({session_id:36407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:49:05.882Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 70 · agent · 2026-08-31T18:49:07.736Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:08.531Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:24.891Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"b475db","wall_time_seconds":30.001596841,"session_id":36407,"original_token_count":27,"output":"built 58,521 temporal query contexts\nepoch 1/4 loss=6.7596 scale=14.23\nepoch 2/4 loss=6.6571 scale=13.71\n"} ## Step 71 · agent · 2026-08-31T18:49:26.725Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:28.131Z { "code": "const r = await tools.write_stdin({session_id:36407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:49:39.179Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 72 · agent · 2026-08-31T18:49:42.329Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:43.087Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:46.453Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"a907db","wall_time_seconds":18.274738014,"exit_code":0,"original_token_count":71,"output":"epoch 3/4 loss=6.5930 scale=13.13\nepoch 4/4 loss=6.5235 scale=12.86\nwrote /app/solution/validation_pop0.006.parquet /app/solution/validation_pop0.009.parquet /app/solution/validation_pop0.012.parquet /app/solution/validation_pop0.015.parquet /app/solution/validation_pop0.018.parquet\n"} ## Step 73 · agent · 2026-08-31T18:49:48.914Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:52.437Z { "code": "const ws=[\"0.006\",\"0.009\",\"0.012\",\"0.015\",\"0.018\"];\nconst rs=await Promise.all(ws.map(w=>tools.exec_command({cmd:`/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop${w}.parquet`,workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000})));\nrs.forEach(r=>text(r.output));" } ### Tool output · 2026-08-31T18:49:57.309Z Script completed Wall time 4.9 seconds Output: { "model": "/app/solution/validation_pop0.006.parquet", "scores": { "quality_ndcg@10": 0.014262, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.009.parquet", "scores": { "quality_ndcg@10": 0.014831, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.012.parquet", "scores": { "quality_ndcg@10": 0.015196, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.015.parquet", "scores": { "quality_ndcg@10": 0.015006, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.018.parquet", "scores": { "quality_ndcg@10": 0.014592, "quality_users": 4263 } } ## Step 74 · agent · 2026-08-31T18:50:10.668Z ### Thinking unavailable — no text recorded The refinement is the new best: public NDCG@10 is 0.015196 at popularity-coordinate weight 0.012, slightly above the prior 0.015145. I’m locking that configuration now and refitting it on train plus the full validation window, treating validation events only as observed history/training outcomes for the hidden-test cutoff. ### exec · 2026-08-31T18:50:21.874Z { "code": "const p = await tools.update_plan({explanation:\"Selected the mixed-history 64D model at public NDCG@10 0.015196 with popularity-coordinate weight 0.012.\",plan:[\n {step:\"Inspect data, evaluator, and environment\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and tune against public validation\",status:\"completed\"},\n {step:\"Refit on train plus validation and generate final submission\",status:\"in_progress\"},\n {step:\"Verify schema, eligibility, reproducibility, and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_and_submit.py --stage final --epochs 4 --windows 16 --dim 64 --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.012\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:50:32.895Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 75 · agent · 2026-08-31T18:50:35.216Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:35.965Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:52.056Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"d6b671","wall_time_seconds":30.000964726,"session_id":17121,"original_token_count":15,"output":"loading final listens\nretained 32,061,602 catalog exposures\n"} ## Step 76 · agent · 2026-08-31T18:50:53.832Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:55.196Z { "code": "const r = await tools.write_stdin({session_id:17121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:51:06.217Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T18:51:08.100Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:09.597Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:25.232Z Script completed Wall time 15.6 seconds Output: {"chunk_id":"74982a","wall_time_seconds":30.001383322,"session_id":17121,"original_token_count":27,"output":"built 59,621 temporal query contexts\nepoch 1/4 loss=6.7612 scale=14.27\nepoch 2/4 loss=6.6568 scale=13.70\n"} ## Step 78 · agent · 2026-08-31T18:51:27.020Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:28.424Z { "code": "const r = await tools.write_stdin({session_id:17121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:51:39.463Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:51:41.516Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:42.287Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:42.385Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c7a63a","wall_time_seconds":11.862146105,"exit_code":0,"original_token_count":27,"output":"epoch 3/4 loss=6.5902 scale=13.21\nepoch 4/4 loss=6.5185 scale=12.89\nwrote /app/solution/submission.parquet\n"} ## Step 80 · agent · 2026-08-31T18:52:03.347Z ### Thinking unavailable — no text recorded ## Step 81 · agent · 2026-08-31T18:52:05.705Z ### Thinking unavailable — no text recorded The final refit is complete and `/app/solution/submission.parquet` is safely written. I’m now running contract-level checks against the test eligibility archive, then documenting the exact architecture, validation score, and reproducible final command. I’m also making the exported popularity coordinate explicit inside the tower interfaces so the delivered implementation visibly preserves one-dot-product scoring end to end. ### exec · 2026-08-31T18:52:48.678Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_submit.py\\n@@\\n class UserTower(nn.Module):\\n@@\\n def forward(self, history, history_w, liked, disliked, features):\\n@@\\n return F.normalize(self.net(x), dim=1)\\n+\\n+ def retrieval_vector(self, history, history_w, liked, disliked, features):\\n+ \\\"\\\"\\\"Return the request-time vector, including the item-prior intercept.\\\"\\\"\\\"\\n+ base = self.forward(history, history_w, liked, disliked, features)\\n+ return torch.cat([base, torch.ones(len(base), 1, dtype=base.dtype, device=base.device)], dim=1)\\n@@\\n class ItemTower(nn.Module):\\n@@\\n def forward(self, ids):\\n return F.normalize(self.net(torch.cat([self.ids(ids), self.features[ids]], dim=1)), dim=1)\\n+\\n+ def retrieval_vector(self, ids, pop_weight: float = 0.0):\\n+ \\\"\\\"\\\"Return the independently precomputable candidate vector.\\\"\\\"\\\"\\n+ base = self.forward(ids)\\n+ popularity = -self.features[ids, 3:4] * pop_weight\\n+ return torch.cat([base, popularity], dim=1)\\n@@\\n- def score(self, user_inputs, item_ids):\\n+ def score(self, user_inputs, item_ids, pop_weight: float = 0.0):\\n \\\"\\\"\\\"Exact request-time factorization: user_vector @ item_vector.T.\\\"\\\"\\\"\\n- u = self.user_tower(*user_inputs)\\n- v = self.item_tower(item_ids)\\n+ u = self.user_tower.retrieval_vector(*user_inputs)\\n+ v = self.item_tower.retrieval_vector(item_ids, pop_weight)\\n return u @ v.T\\n@@\\n- item_vec = model.item_tower(torch.arange(n_items))\\n- # Popularity coordinate is an explicit item-tower feature and a constant user-tower coordinate.\\n- pop = torch.from_numpy((-meta[\\\"item_features\\\"][:, 3]).astype(np.float32))\\n- item_vec = torch.cat([item_vec, (pop_weight * pop)[:, None]], 1)\\n+ item_vec = model.item_tower.retrieval_vector(torch.arange(n_items), pop_weight)\\n@@\\n- uv = torch.cat([model.user_tower(*user_in), torch.ones(len(t[\\\"history\\\"][idx]), 1)], 1)\\n+ uv = model.user_tower.retrieval_vector(*user_in)\\n*** Add File: /app/solution/README.md\\n+# History-based PyTorch two-tower ranker\\n+\\n+`submission.parquet` is the final hidden-window ranking. It contains 100 eligible,\\n+distinct items (ranks 1–100) for each of the 8,456 target users.\\n+\\n+## Model\\n+\\n+The CPU PyTorch model has independent user and item towers. The user tower has\\n+separate trainable embedding pools for recent >=50%-completed listens, active\\n+likes, and active dislikes. It combines these bounded pools with five aggregate\\n+history features in a two-layer SiLU MLP. The item tower combines a trainable\\n+item embedding with four standardized catalog-popularity features in a two-layer\\n+SiLU MLP. Both produce normalized 64-D representations. A validation-calibrated\\n+popularity/intercept coordinate is appended by the two tower interfaces, yielding\\n+same-sized 65-D retrieval vectors. The final score is exactly one user-vector /\\n+precomputable-item-vector dot product. There is no user-ID embedding.\\n+\\n+Training uses 16 rolling seven-day temporal windows. Each example encodes only\\n+history available before its cutoff and targets cutoff-eligible, first-exposure\\n+organic >=80% listens and organic likes. It uses gain-aware label sampling and\\n+sampled-softmax cross-entropy with in-batch plus uniform catalog negatives. Event\\n+logs are streamed into compact arrays and histories are bounded; the pipeline\\n+never constructs or decomposes a global user-item matrix.\\n+\\n+## Selection and refit\\n+\\n+The selected validation configuration scored **0.015196 NDCG@10** over 4,263\\n+scored users with `/task/evaluate_public.py`. It was then refit using\\n+`train_* + val_*`; `test_preference_state.parquet` and test eligibility are used\\n+only at the final cutoff.\\n+\\n+Reproduce the final artifact with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_and_submit.py \\\\\\n+ --stage final --epochs 4 --windows 16 --dim 64 --batch-size 256 \\\\\\n+ --negatives 768 --lr 0.002 --pop-weights=0.012\\n+```\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:52:48.728Z Script completed Wall time 0.0 seconds Output: {} ## Step 82 · agent · 2026-08-31T18:53:00.974Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:19.854Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys, numpy as np, pandas as pd, torch\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nsys.path.insert(0,'/app/solution')\\nfrom train_and_submit import TwoTower, item_features\\nD=Path('/task/data'); S=Path('/app/solution')\\ntargets=set(map(int,pd.read_parquet(D/'target_users.parquet').uid))\\nx=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets)\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\nur={int(u):r for r,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(items)}\\nrows=x.uid.map(ur).to_numpy(); cols=x.item_id.map(ic).to_numpy()\\nassert np.all(cols>=0)\\nok=((packed[rows,cols//8] >> (cols%8)) & 1).astype(bool)\\nassert ok.all()\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nck=torch.load(S/'final_model.pt',map_location='cpu',weights_only=False)\\nm=TwoTower(len(cat),64,torch.from_numpy(item_features(cat))); m.load_state_dict(ck['model'],strict=True)\\nprint({'rows':len(x),'users':x.uid.nunique(),'items':x.item_id.nunique(),'eligible_rows':int(ok.sum()),'ranks_ok':bool(x.groupby('uid')['rank'].nunique().eq(100).all()),'checkpoint_load':'strict_ok','size_mb':round((S/'submission.parquet').stat().st_size/1e6,2)})\\nprint((S/'final_run.json').read_text())\\nPY\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_and_submit.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop0.012.parquet\\nls -lh /app/solution | sed -n '1,120p'\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);" } ### Tool output · 2026-08-31T18:53:27.877Z Script completed Wall time 8.0 seconds Output: {'rows': 845600, 'users': 8456, 'items': 6493, 'eligible_rows': 845600, 'ranks_ok': True, 'checkpoint_load': 'strict_ok', 'size_mb': 1.52} { "seconds": 74.24865627288818, "contexts": 59621, "rankings": [ "/app/solution/submission.parquet" ] } { "model": "/app/solution/validation_pop0.012.parquet", "scores": { "quality_ndcg@10": 0.015196, "quality_users": 4263 } } total 71M -rw-r--r-- 1 agent agent 1.9K Aug 31 18:52 README.md drwxr-xr-x 2 agent agent 4.0K Aug 31 18:53 __pycache__ -rw-r--r-- 1 agent agent 23M Aug 31 18:51 final_model.pt -rw-r--r-- 1 agent agent 115 Aug 31 18:51 final_run.json -rw-r--r-- 1 agent agent 3.1K Aug 31 18:27 profile_and_baseline.py -rw-r--r-- 1 agent agent 1.5M Aug 31 18:51 submission.parquet -rw-r--r-- 1 agent agent 20K Aug 31 18:52 train_and_submit.py -rw-r--r-- 1 agent agent 23M Aug 31 18:49 validation_model.pt -rw-r--r-- 1 agent agent 1.8M Aug 31 18:44 validation_pop0.004.parquet -rw-r--r-- 1 agent agent 1.6M Aug 31 18:47 validation_pop0.005.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:49 validation_pop0.006.parquet -rw-r--r-- 1 agent agent 1.7M Aug 31 18:44 validation_pop0.008.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:49 validation_pop0.009.parquet -rw-r--r-- 1 agent agent 1.6M Aug 31 18:47 validation_pop0.01.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:49 validation_pop0.012.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:49 validation_pop0.015.parquet -rw-r--r-- 1 agent agent 1.7M Aug 31 18:44 validation_pop0.016.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:49 validation_pop0.018.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:47 validation_pop0.02.parquet -rw-r--r-- 1 agent agent 1.6M Aug 31 18:44 validation_pop0.022.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:47 validation_pop0.03.parquet -rw-r--r-- 1 agent agent 1.2M Aug 31 18:40 validation_pop0.06.parquet -rw-r--r-- 1 agent agent 1.6M Aug 31 18:47 validation_pop0.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:28 validation_popularity.parquet -rw-r--r-- 1 agent agent 320 Aug 31 18:49 validation_run.json ## Step 83 · agent · 2026-08-31T18:53:47.901Z ### Thinking unavailable — no text recorded ## Step 84 · agent · 2026-08-31T18:54:01.980Z ### Thinking unavailable — no text recorded ## Step 85 · agent · 2026-08-31T18:54:02.403Z ### Thinking unavailable — no text recorded All final structural checks pass: 845,600 rows, exactly 100 ranks per user, and every row is test-eligible; the saved checkpoint also reloads strictly. Since time remains and single-seed variance is visible, I’m testing a small three-seed tower ensemble. Concatenating the independently trained user vectors and precomputed item vectors still yields one exact dot product, while often stabilizing sparse next-item retrieval. The current submission remains untouched unless this beats 0.015196 publicly. ### exec · 2026-08-31T18:54:37.581Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_and_submit.py\\n@@\\n-@torch.inference_mode()\\n-def rank(model, infer, meta, stage: str, pop_weight: float, suffix: str = \\\"\\\"):\\n+@torch.inference_mode()\\n+def rank(models, infer, meta, stage: str, pop_weight: float, suffix: str = \\\"\\\"):\\n+ \\\"\\\"\\\"Rank with one model or a dot-product-preserving concatenated ensemble.\\\"\\\"\\\"\\n+ if not isinstance(models, (list, tuple)):\\n+ models = [models]\\n t = to_tensors(infer); n_items = len(meta[\\\"item_ids\\\"])\\n- item_vec = model.item_tower.retrieval_vector(torch.arange(n_items), pop_weight)\\n+ scale = math.sqrt(len(models))\\n+ item_base = torch.cat([m.item_tower(torch.arange(n_items)) / scale for m in models], dim=1)\\n+ pop = -models[0].item_tower.features[:, 3:4] * pop_weight\\n+ item_vec = torch.cat([item_base, pop], dim=1)\\n@@\\n- uv = model.user_tower.retrieval_vector(*user_in)\\n+ user_base = torch.cat([m.user_tower(*user_in) / scale for m in models], dim=1)\\n+ uv = torch.cat([user_base, torch.ones(len(user_base), 1)], dim=1)\\n@@\\n ap.add_argument(\\\"--lr\\\", type=float, default=0.002); ap.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ ap.add_argument(\\\"--ensemble\\\", type=int, default=1)\\n@@\\n- model = train_model(train, meta, args)\\n+ models = []\\n+ for member in range(args.ensemble):\\n+ member_args = argparse.Namespace(**vars(args)); member_args.seed = args.seed + member * 1009\\n+ seed_all(member_args.seed)\\n+ print(f\\\"training ensemble member {member+1}/{args.ensemble} seed={member_args.seed}\\\", flush=True)\\n+ models.append(train_model(train, meta, member_args))\\n@@\\n- paths = [str(rank(model, infer, meta, args.stage, w, \\\"\\\" if len(weights)==1 else f\\\"_pop{w:g}\\\")) for w in weights]\\n- torch.save({\\\"model\\\": model.state_dict(), \\\"args\\\": vars(args), \\\"item_ids\\\": meta[\\\"item_ids\\\"]}, OUT / f\\\"{args.stage}_model.pt\\\")\\n+ paths = [str(rank(models, infer, meta, args.stage, w, \\\"\\\" if len(weights)==1 else f\\\"_pop{w:g}\\\")) for w in weights]\\n+ torch.save({\\\"models\\\": [m.state_dict() for m in models], \\\"model\\\": models[0].state_dict(),\\n+ \\\"args\\\": vars(args), \\\"item_ids\\\": meta[\\\"item_ids\\\"]}, OUT / f\\\"{args.stage}_model.pt\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"cp /app/solution/validation_pop0.012.parquet /app/solution/best_validation.parquet && /opt/task-venv/bin/python -m py_compile /app/solution/train_and_submit.py && /opt/task-venv/bin/python /app/solution/train_and_submit.py --stage validation --ensemble 3 --epochs 4 --windows 16 --dim 64 --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.006,0.009,0.012,0.015,0.018\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:54:48.602Z Script running with cell ID 40 Wall time 11.0 seconds Output: {} ## Step 86 · agent · 2026-08-31T18:54:50.319Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:51.120Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:07.765Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"3c5337","wall_time_seconds":30.000597965,"session_id":23705,"original_token_count":17,"output":"loading validation listens\nretained 31,062,540 catalog exposures\n"} ## Step 87 · agent · 2026-08-31T18:55:09.670Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:11.105Z { "code": "const r = await tools.write_stdin({session_id:23705,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:55:22.126Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 88 · agent · 2026-08-31T18:55:24.140Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:24.971Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:41.141Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"2023f5","wall_time_seconds":30.00145496,"session_id":23705,"original_token_count":37,"output":"built 58,521 temporal query contexts\ntraining ensemble member 1/3 seed=20260831\nepoch 1/4 loss=6.7596 scale=14.23\nepoch 2/4 loss=6.6571 scale=13.71\n"} ## Step 89 · agent · 2026-08-31T18:55:43.340Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:44.754Z { "code": "const r = await tools.write_stdin({session_id:23705,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:55:55.800Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 90 · agent · 2026-08-31T18:55:58.002Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:58.888Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:56:14.808Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"07eccf","wall_time_seconds":30.001794356,"session_id":23705,"original_token_count":54,"output":"epoch 3/4 loss=6.5930 scale=13.13\nepoch 4/4 loss=6.5235 scale=12.86\ntraining ensemble member 2/3 seed=20261840\nepoch 1/4 loss=6.7638 scale=14.33\nepoch 2/4 loss=6.6578 scale=13.80\nepoch 3/4 loss=6.5984 scale=13.36\n"} ## Step 91 · agent · 2026-08-31T18:56:17.894Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:19.268Z { "code": "const r = await tools.write_stdin({session_id:23705,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:56:30.293Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 92 · agent · 2026-08-31T18:56:32.070Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:32.914Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:56:49.303Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"fbb71d","wall_time_seconds":30.000687361,"session_id":23705,"original_token_count":54,"output":"epoch 4/4 loss=6.5282 scale=13.06\ntraining ensemble member 3/3 seed=20262849\nepoch 1/4 loss=6.7622 scale=14.34\nepoch 2/4 loss=6.6504 scale=13.79\nepoch 3/4 loss=6.5912 scale=13.30\nepoch 4/4 loss=6.5265 scale=12.99\n"} ## Step 93 · agent · 2026-08-31T18:56:51.303Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:52.663Z { "code": "const r = await tools.write_stdin({session_id:23705,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:57:00.432Z Script completed Wall time 7.8 seconds Output: {"chunk_id":"1ba6aa","wall_time_seconds":7.739172141,"exit_code":0,"original_token_count":54,"output":"wrote /app/solution/validation_pop0.006.parquet /app/solution/validation_pop0.009.parquet /app/solution/validation_pop0.012.parquet /app/solution/validation_pop0.015.parquet /app/solution/validation_pop0.018.parquet\n"} ## Step 94 · agent · 2026-08-31T18:57:02.169Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:05.680Z { "code": "const ws=[\"0.006\",\"0.009\",\"0.012\",\"0.015\",\"0.018\"];\nconst rs=await Promise.all(ws.map(w=>tools.exec_command({cmd:`/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop${w}.parquet`,workdir:\"/app\",yield_time_ms:30000,max_output_tokens:2000})));\nrs.forEach(r=>text(r.output));" } ### Tool output · 2026-08-31T18:57:10.517Z Script completed Wall time 4.8 seconds Output: { "model": "/app/solution/validation_pop0.006.parquet", "scores": { "quality_ndcg@10": 0.019634, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.009.parquet", "scores": { "quality_ndcg@10": 0.019549, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.012.parquet", "scores": { "quality_ndcg@10": 0.018806, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.015.parquet", "scores": { "quality_ndcg@10": 0.01754, "quality_users": 4263 } } { "model": "/app/solution/validation_pop0.018.parquet", "scores": { "quality_ndcg@10": 0.016327, "quality_users": 4263 } } ## Step 95 · agent · 2026-08-31T18:57:27.899Z ### Thinking unavailable — no text recorded The ensemble materially improves public quality to **0.019634 NDCG@10** at weight 0.006, versus 0.015196 for one seed. I’m adopting it: the three 64-D branches concatenate into one 193-D request vector (including the prior coordinate), so serving still computes one history-derived user embedding and performs one MIPS dot product. I’m refitting those three branches on train+validation now; the existing valid submission remains in place until replacement finishes. ### exec · 2026-08-31T18:57:41.955Z { "code": "const p = await tools.update_plan({explanation:\"A 3-branch concatenated two-tower ensemble improved public NDCG@10 to 0.019634 while retaining exact single-dot-product serving.\",plan:[\n {step:\"Inspect data, evaluator, and environment\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and tune against public validation\",status:\"completed\"},\n {step:\"Refit selected three-branch model on train plus validation\",status:\"in_progress\"},\n {step:\"Verify schema, eligibility, reproducibility, and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"cp /app/solution/validation_pop0.006.parquet /app/solution/best_validation.parquet && /opt/task-venv/bin/python /app/solution/train_and_submit.py --stage final --ensemble 3 --epochs 4 --windows 16 --dim 64 --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.006\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:57:52.975Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T18:57:55.535Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:56.345Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:12.136Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"b557fb","wall_time_seconds":30.001277621,"session_id":96714,"original_token_count":15,"output":"loading final listens\nretained 32,061,602 catalog exposures\n"} ## Step 97 · agent · 2026-08-31T18:58:13.813Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:15.214Z { "code": "const r = await tools.write_stdin({session_id:96714,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:58:26.231Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T18:58:28.058Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:28.880Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:45.245Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"e60377","wall_time_seconds":30.000462382,"session_id":96714,"original_token_count":37,"output":"built 59,621 temporal query contexts\ntraining ensemble member 1/3 seed=20260831\nepoch 1/4 loss=6.7612 scale=14.27\nepoch 2/4 loss=6.6568 scale=13.70\n"} ## Step 99 · agent · 2026-08-31T18:58:47.462Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:48.767Z { "code": "const r = await tools.write_stdin({session_id:96714,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:58:59.805Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T18:59:01.824Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:02.614Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:18.818Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"147a0d","wall_time_seconds":30.001242728,"session_id":96714,"original_token_count":54,"output":"epoch 3/4 loss=6.5902 scale=13.21\nepoch 4/4 loss=6.5185 scale=12.89\ntraining ensemble member 2/3 seed=20261840\nepoch 1/4 loss=6.7679 scale=14.30\nepoch 2/4 loss=6.6624 scale=13.76\nepoch 3/4 loss=6.5990 scale=13.31\n"} ## Step 101 · agent · 2026-08-31T18:59:20.548Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:22.398Z { "code": "const r = await tools.write_stdin({session_id:96714,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:59:33.444Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T18:59:35.649Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:36.451Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:52.451Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"0d64e6","wall_time_seconds":30.00190071,"session_id":96714,"original_token_count":54,"output":"epoch 4/4 loss=6.5278 scale=13.07\ntraining ensemble member 3/3 seed=20262849\nepoch 1/4 loss=6.7653 scale=14.36\nepoch 2/4 loss=6.6533 scale=13.77\nepoch 3/4 loss=6.5844 scale=13.33\nepoch 4/4 loss=6.5203 scale=13.14\n"} ## Step 103 · agent · 2026-08-31T18:59:54.649Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:56.053Z { "code": "const r = await tools.write_stdin({session_id:96714,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:59:56.147Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"dddc7a","wall_time_seconds":0.0000023,"exit_code":0,"original_token_count":10,"output":"wrote /app/solution/submission.parquet\n"} ## Step 104 · agent · 2026-08-31T19:00:02.957Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:17.568Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n-The CPU PyTorch model has independent user and item towers. The user tower has\\n+The CPU PyTorch model has independent user and item towers. Each user branch has\\n@@\\n-SiLU MLP. Both produce normalized 64-D representations. A validation-calibrated\\n-popularity/intercept coordinate is appended by the two tower interfaces, yielding\\n-same-sized 65-D retrieval vectors. The final score is exactly one user-vector /\\n-precomputable-item-vector dot product. There is no user-ID embedding.\\n+SiLU MLP. Each item branch combines a trainable item embedding with four\\n+standardized catalog-popularity features in a two-layer SiLU MLP. Three seeded\\n+64-D branches are concatenated (scaled so their dot products average), and a\\n+validation-calibrated popularity/intercept coordinate is appended. The result is\\n+a same-sized 193-D user/item vector pair. The final score is exactly one user\\n+vector / independently precomputable item vector dot product. There is no user-ID\\n+embedding.\\n@@\\n-The selected validation configuration scored **0.015196 NDCG@10** over 4,263\\n+The selected validation configuration scored **0.019634 NDCG@10** over 4,263\\n@@\\n- --stage final --epochs 4 --windows 16 --dim 64 --batch-size 256 \\\\\\n- --negatives 768 --lr 0.002 --pop-weights=0.012\\n+ --stage final --ensemble 3 --epochs 4 --windows 16 --dim 64 \\\\\\n+ --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.006\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:00:17.671Z Script completed Wall time 0.0 seconds Output: {} ## Step 105 · agent · 2026-08-31T19:00:19.906Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:39.289Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,240p' /app/solution/README.md && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys,numpy as np,pandas as pd,torch\\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\\nsys.path.insert(0,'/app/solution'); from train_and_submit import TwoTower,item_features\\nD=Path('/task/data');S=Path('/app/solution')\\nx=validate_ranking(pd.read_parquet(S/'submission.parquet'),set(map(int,pd.read_parquet(D/'target_users.parquet').uid)))\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64);items=z['item_ids'].astype(np.int64);packed=z['packed_eligible']\\nur={int(v):k for k,v in enumerate(uids)};ic={int(v):k for k,v in enumerate(items)}\\nr=np.fromiter((ur[int(v)] for v in x.uid),dtype=np.int32,count=len(x));c=np.fromiter((ic.get(int(v),-1) for v in x.item_id),dtype=np.int32,count=len(x));assert (c>=0).all();ok=((packed[r,c//8]>>(c%8))&1).astype(bool);assert ok.all()\\ncat=pd.read_parquet(D/'candidate_catalog.parquet');ck=torch.load(S/'final_model.pt',map_location='cpu',weights_only=False); assert len(ck['models'])==3\\nfor state in ck['models']:\\n m=TwoTower(len(cat),64,torch.from_numpy(item_features(cat)));m.load_state_dict(state,strict=True)\\nassert x.groupby('uid').size().eq(100).all() and x.groupby('uid').item_id.nunique().eq(100).all()\\nprint('FINAL_OK',{'rows':len(x),'users':x.uid.nunique(),'all_eligible':bool(ok.all()),'ensemble_members':len(ck['models']),'parquet_mb':round((S/'submission.parquet').stat().st_size/1e6,3)})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/best_validation.parquet\\ncat /app/solution/final_run.json\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-08-31T19:00:48.068Z Script completed Wall time 8.8 seconds Output: # History-based PyTorch two-tower ranker `submission.parquet` is the final hidden-window ranking. It contains 100 eligible, distinct items (ranks 1–100) for each of the 8,456 target users. ## Model The CPU PyTorch model has independent user and item towers. Each user branch has separate trainable embedding pools for recent >=50%-completed listens, active likes, and active dislikes. It combines these bounded pools with five aggregate history features in a two-layer SiLU MLP. The item tower combines a trainable item embedding with four standardized catalog-popularity features in a two-layer SiLU MLP. Each item branch combines a trainable item embedding with four standardized catalog-popularity features in a two-layer SiLU MLP. Three seeded 64-D branches are concatenated (scaled so their dot products average), and a validation-calibrated popularity/intercept coordinate is appended. The result is a same-sized 193-D user/item vector pair. The final score is exactly one user vector / independently precomputable item vector dot product. There is no user-ID embedding. Training uses 16 rolling seven-day temporal windows. Each example encodes only history available before its cutoff and targets cutoff-eligible, first-exposure organic >=80% listens and organic likes. It uses gain-aware label sampling and sampled-softmax cross-entropy with in-batch plus uniform catalog negatives. Event logs are streamed into compact arrays and histories are bounded; the pipeline never constructs or decomposes a global user-item matrix. ## Selection and refit The selected validation configuration scored **0.019634 NDCG@10** over 4,263 scored users with `/task/evaluate_public.py`. It was then refit using `train_* + val_*`; `test_preference_state.parquet` and test eligibility are used only at the final cutoff. Reproduce the final artifact with: ```sh /opt/task-venv/bin/python /app/solution/train_and_submit.py \ --stage final --ensemble 3 --epochs 4 --windows 16 --dim 64 \ --batch-size 256 --negatives 768 --lr 0.002 --pop-weights=0.006 ``` FINAL_OK {'rows': 845600, 'users': 8456, 'all_eligible': True, 'ensemble_members': 3, 'parquet_mb': 1.52} { "model": "/app/solution/best_validation.parquet", "scores": { "quality_ndcg@10": 0.019634, "quality_users": 4263 } } { "seconds": 129.48218536376953, "contexts": 59621, "rankings": [ "/app/solution/submission.parquet" ] } ## Step 106 · agent · 2026-08-31T19:00:52.583Z ### Thinking unavailable — no text recorded The selected ensemble refit is complete. Final verification passed again: 845,600 rows, 8,456 users, 100 distinct ranks each, 100% test eligibility, and all three checkpoint branches reload strictly. The retained best validation artifact independently reproduces the reported 0.019634 score. I’m correcting one duplicated README sentence, then the deliverable is finished. ### exec · 2026-08-31T19:01:04.347Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n-history features in a two-layer SiLU MLP. The item tower combines a trainable\\n-item embedding with four standardized catalog-popularity features in a two-layer\\n-SiLU MLP. Each item branch combines a trainable item embedding with four\\n+history features in a two-layer SiLU MLP. Each item branch combines a trainable item embedding with four\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\ntext(await tools.update_plan({explanation:\"Final ensemble ranking, checkpoint, validation artifact, documentation, and all structural/eligibility checks are complete.\",plan:[\n {step:\"Inspect data, evaluator, and environment\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train and tune against public validation\",status:\"completed\"},\n {step:\"Refit selected three-branch model on train plus validation\",status:\"completed\"},\n {step:\"Verify schema, eligibility, reproducibility, and document results\",status:\"completed\"}\n]}));" } ### Tool output · 2026-08-31T19:01:04.411Z Script completed Wall time 0.0 seconds Output: {} {} ## Step 107 · agent · 2026-08-31T19:01:07.841Z ### Thinking unavailable — no text recorded Completed the latency-efficient PyTorch two-tower ranker. - Final ranking: [submission.parquet](/app/solution/submission.parquet) - Training pipeline: [train_and_submit.py](/app/solution/train_and_submit.py) - Documentation: [README.md](/app/solution/README.md) - Final checkpoint: [final_model.pt](/app/solution/final_model.pt) - Public validation NDCG@10: **0.019634** - Verified: 845,600 rows, 8,456 users, ranks 1–100, distinct items, and 100% test eligibility. The final 193-D vectors use three concatenated history-based tower branches plus a calibrated popularity coordinate, with relevance computed by one exact dot product.