yambda-discovery-ranking-two-tower-base / run-004-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:24:59.657Z 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 · 2026-08-31T18:25:01.517Z ## 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 · 2026-08-31T18:25:01.517Z 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 · 2026-08-31T18:25:01.517Z 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 · 2026-08-31T18:25:01.517Z 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": "01a05911-4701-7dc1-9630-ab3e64138f49", "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 · 2026-08-31T18:25:01.526Z # 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 · 2026-08-31T18:25:04.940Z ### Thinking unavailable — no text recorded I’ll build this end to end: first audit the data and evaluator, then establish a valid baseline, train/tune the CPU two-tower model on the public split, and finally refit on train+validation to write the test submission. I’ll keep every artifact under `/app/solution/` and preserve the best valid ranking as I iterate. ### exec · 2026-08-31T18:25:15.791Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect schemas, data scale, eligibility, and public evaluator\",status:\"in_progress\"},\n {step:\"Implement scalable history features and PyTorch two-tower training\",status:\"pending\"},\n {step:\"Tune on validation and save the best valid ranking\",status:\"pending\"},\n {step:\"Refit with validation history and generate final test submission\",status:\"pending\"},\n {step:\"Verify artifacts and document architecture, score, and command\",status:\"pending\"}\n]}); text(p);\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,180p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:15.864Z Script completed Wall time 0.1 seconds Output: {} /app /task/requirements.txt /task/evaluate_public.py /task/ranking_evaluation.py /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/validation_preference_state.parquet #!/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 · 2026-08-31T18:25:18.023Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:28.314Z { "code": "const r = await tools.exec_command({cmd:\"du -h /task/data/* | sort -h\\npython - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n df=pd.read_parquet(p)\\n print(p.name, df.shape, dict(df.dtypes), 'uids', df.uid.nunique() if 'uid' in df else '-', 'items',df.item_id.nunique() if 'item_id' in df else '-')\\n if 'timestamp' in df: print(' ts',df.timestamp.min(),df.timestamp.max())\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n n=bits.sum(1)\\n print(' eligible min/median/mean/max',n.min(),np.median(n),n.mean(),n.max())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:28.452Z Script completed Wall time 0.1 seconds Output: 8.0K /task/data/val_undislikes.parquet 36K /task/data/val_dislikes.parquet 72K /task/data/val_unlikes.parquet 84K /task/data/target_users.parquet 156K /task/data/train_undislikes.parquet 292K /task/data/val_likes.parquet 344K /task/data/candidate_catalog.parquet 840K /task/data/train_dislikes.parquet 1.1M /task/data/test_preference_state.parquet 1.1M /task/data/validation_preference_state.parquet 1.8M /task/data/train_unlikes.parquet 5.2M /task/data/train_likes.parquet 7.3M /task/data/validation_eligible_candidates.npz 7.4M /task/data/test_eligible_candidates.npz 9.5M /task/data/val_listens.parquet 305M /task/data/train_listens.parquet Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-08-31T18:25:31.899Z ### Thinking unavailable — no text recorded The data is compact enough to iterate locally, but the default shell’s `python` is not the task virtual environment. I’m locating the pinned PyTorch/Pandas interpreter now, then I’ll quantify event and candidate distributions before choosing history sampling and negative mining settings. ### exec · 2026-08-31T18:25:37.601Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\nfind /opt /usr/local /app -maxdepth 4 -type f -name python 2>/dev/null | sed -n '1,80p'\\nls -la /task /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:37.709Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /app: total 12 drwxr-xr-x 3 agent agent 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:24 .. 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 18:24 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:08 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 ## Step 10 · agent · 2026-08-31T18:25:42.230Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:44.983Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list --format=columns | sed -n '1,100p'\\nenv | rg '^(PATH|VIRTUAL_ENV|PYTHONPATH|UV_)='\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:45.726Z Script completed Wall time 0.7 seconds Output: Package Version ------- ------- pip 25.0.1 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 ## Step 11 · agent · 2026-08-31T18:25:48.467Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:52.122Z { "code": "const r = await tools.exec_command({cmd:\"command -v uv || true\\ncommand -v python3.12 || true\\ncommand -v python3.13 || true\\nls -la /usr/local/bin | sed -n '1,160p'\\nfind /app -maxdepth 3 -type f -o -type l | sort\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:52.190Z Script completed Wall time 0.0 seconds Output: /usr/local/bin/python3.12 total 48 drwxr-xr-x 1 root root 4096 Aug 31 18:21 . drwxr-xr-x 1 root root 4096 Aug 3 00:00 .. lrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12 -rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12 lrwxrwxrwx 1 root root 53 Aug 31 18:21 codex -> /opt/codex-home/packages/standalone/current/bin/codex lrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3 lrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12 -rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12 lrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12 lrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3 lrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12 -rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12 lrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3 lrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config lrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12 lrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config -rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12 -rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config ## Step 12 · agent · 2026-08-31T18:25:58.822Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:02.309Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt\\nls -la /opt/venv /opt/python 2>/dev/null || true\\npython - <<'PY'\\nimport sys, site\\nprint(sys.executable)\\nprint('\\\\n'.join(sys.path))\\nprint(site.getsitepackages())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" } ### Tool output · 2026-08-31T18:26:02.375Z Script completed Wall time 0.1 seconds Output: total 16 drwxr-xr-x 1 root root 4096 Aug 31 18:21 . drwxr-xr-x 1 root root 4096 Aug 31 18:24 .. drwxr-xr-x 4 root root 4096 Aug 31 18:21 codex-home dr-xr-xr-x 1 root root 4096 Aug 31 18:21 task-venv /usr/local/bin/python /usr/local/lib/python312.zip /usr/local/lib/python3.12 /usr/local/lib/python3.12/lib-dynload /usr/local/lib/python3.12/site-packages ['/usr/local/lib/python3.12/site-packages'] ## Step 13 · agent · 2026-08-31T18:26:05.227Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:19.520Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,80p'\\n/opt/task-venv/bin/python - <<'PY'\\nimport torch,pandas,pyarrow,numpy\\nprint('ok',torch.__version__,pandas.__version__,pyarrow.__version__,numpy.__version__)\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n df=pd.read_parquet(p)\\n s=f'{p.name:38s} {str(df.shape):18s}'\\n if 'uid' in df: s+=f' uids={df.uid.nunique():7d}'\\n if 'item_id' in df: s+=f' items={df.item_id.nunique():7d}'\\n if 'timestamp' in df: s+=f' ts=[{df.timestamp.min()},{df.timestamp.max()}]'\\n print(s)\\n print(' ',dict(df.dtypes))\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n n=bits.sum(1)\\n print(' eligible min/median/mean/max',n.min(),np.median(n),round(n.mean(),1),n.max())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-08-31T18:26:27.059Z Script completed Wall time 7.5 seconds Output: total 124 dr-xr-xr-x 1 root root 4096 Aug 31 18:21 . dr-xr-xr-x 1 root root 4096 Aug 31 18:21 .. -r--r--r-- 1 root root 9033 Aug 31 18:21 Activate.ps1 -r--r--r-- 1 root root 2158 Aug 31 18:21 activate -r--r--r-- 1 root root 920 Aug 31 18:21 activate.csh -r--r--r-- 1 root root 2195 Aug 31 18:21 activate.fish -r-xr-xr-x 1 root root 302 Aug 31 18:21 chardetect -r-xr-xr-x 1 root root 304 Aug 31 18:21 dotenv -r-xr-xr-x 1 root root 308 Aug 31 18:21 f2py -r-xr-xr-x 1 root root 299 Aug 31 18:21 idna -r-xr-xr-x 1 root root 297 Aug 31 18:21 isympy -r-xr-xr-x 1 root root 305 Aug 31 18:21 jsonschema -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupyter -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupyter-migrate -r-xr-xr-x 1 root root 316 Aug 31 18:21 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Aug 31 18:21 jupyter-trust -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupytext -r-xr-xr-x 1 root root 315 Aug 31 18:21 jupytext-config -r-xr-xr-x 1 root root 301 Aug 31 18:21 kaggle -r-xr-xr-x 1 root root 312 Aug 31 18:21 markdown-it -r-xr-xr-x 1 root root 325 Aug 31 18:21 normalizer -r-xr-xr-x 1 root root 308 Aug 31 18:21 numpy-config -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip3 -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip3.12 lrwxrwxrwx 1 root root 21 Aug 31 18:21 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 18:21 python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 18:21 python3.12 -> python -r-xr-xr-x 1 root root 307 Aug 31 18:21 slugify -r-xr-xr-x 1 root root 321 Aug 31 18:21 torchfrtrace -r-xr-xr-x 1 root root 312 Aug 31 18:21 torchrun -r-xr-xr-x 1 root root 299 Aug 31 18:21 tqdm ok 2.8.0+cpu 2.3.3 18.1.0 2.2.6 candidate_catalog.parquet (30000, 4) items= 30000 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} target_users.parquet (8456, 3) uids= 8456 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} test_preference_state.parquet (484776, 3) uids= 7092 items= 28863 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} train_dislikes.parquet (101075, 4) uids= 5732 items= 50876 ts=[75,24786580] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} train_likes.parquet (814831, 4) uids= 8144 items= 172303 ts=[80,24786790] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} train_listens.parquet (43484159, 6) uids= 9238 items= 845395 ts=[0,24786795] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} train_undislikes.parquet (19960, 4) uids= 2769 items= 14725 ts=[15950,24785395] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} train_unlikes.parquet (292665, 4) uids= 6197 items= 113072 ts=[250,24786570] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} val_dislikes.parquet (3563, 4) uids= 955 items= 3135 ts=[24789920,25393280] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} val_likes.parquet (37795, 4) uids= 4114 items= 22902 ts=[24788815,25393390] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} val_listens.parquet (1501697, 6) uids= 8196 items= 191747 ts=[24788600,25393395] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} val_undislikes.parquet (606, 4) uids= 205 items= 579 ts=[24810715,25393035] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} val_unlikes.parquet (9346, 4) uids= 1313 items= 8080 ts=[24789005,25393385] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} validation_preference_state.parquet (469124, 3) uids= 7038 items= 28814 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible min/median/mean/max 24552 29220.0 29041.3 30000 validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible min/median/mean/max 24657 29242.0 29067.3 30000 ## Step 14 · agent · 2026-08-31T18:26:46.535Z ### Thinking unavailable — no text recorded ## Step 15 · agent · 2026-08-31T18:27:05.221Z ### Thinking unavailable — no text recorded ## Step 16 · agent · 2026-08-31T18:27:12.781Z ### Thinking unavailable — no text recorded The retrieval boundary is broad (median ≈29.2k of 30k tracks per user), so the model—not eligibility—must do nearly all of the top-10 work. I’m measuring the public label density and a popularity-only floor next; this also validates the submission writer before neural training. The neural design will use rolling temporal snapshots, history pooling in the user tower, and sampled/in-batch negatives—no global interaction matrix. ### exec · 2026-08-31T18:27:48.747Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Write an eligibility-correct popularity baseline for either cutoff.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+\\n+\\n+def write(stage: str, output: Path) -> None:\\n+ with np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n+ chunks = []\\n+ for row, uid in enumerate(uids):\\n+ ok = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ # Archive/catalog columns are already ordered by training popularity.\\n+ top = items[ok][:100]\\n+ chunks.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": top,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64)}))\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.concat(chunks, ignore_index=True).to_parquet(output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ ap.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = ap.parse_args()\\n+ write(args.stage, args.output)\\n*** End Patch\";\nconst a = await tools.apply_patch(patch); text(a);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/baseline.py --stage validation --output /app/solution/validation_popularity.parquet\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\nt=pd.read_parquet(D+'target_users.parquet').uid\\nwith np.load(D+'validation_eligible_candidates.npz') as z:\\n urow={int(u):i for i,u in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\\n rel={}\\n L=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n L=L[L.uid.isin(t)&(L.is_organic==1)&(L.played_ratio_pct>=80)].drop_duplicates(['uid','item_id'])\\n K=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n K=K[K.uid.isin(t)&(K.is_organic==1)].drop_duplicates(['uid','item_id'])\\n for grade,df in [(1,L),(3,K)]:\\n for u,x in df[['uid','item_id']].itertuples(index=False,name=None):\\n r=urow.get(int(u)); c=icol.get(int(x))\\n if r is not None and c is not None and ((int(p[r,c//8])>>(c%8))&1): rel.setdefault(int(u),{})[int(x)]=grade\\nprint('scored users',len(rel),'relevant pairs',sum(map(len,rel.values())),'likes',sum(sum(g==3 for g in d.values()) for d in rel.values()),'median rel',np.median([len(x) for x in rel.values()]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-08-31T18:27:57.032Z Script completed Wall time 8.3 seconds Output: {} { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } scored users 4263 relevant pairs 37769 likes 4417 median rel 4.0 ## Step 17 · agent · 2026-08-31T18:28:09.490Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:17.380Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd\\np='/task/data/train_listens.parquet'; f=pq.ParquetFile(p)\\nprint('rowgroups',f.num_row_groups)\\nfor i in [0,1,f.num_row_groups-2,f.num_row_groups-1]:\\n rg=f.metadata.row_group(i)\\n print(i,rg.num_rows,[(rg.column(j).path_in_schema,rg.column(j).statistics.min,rg.column(j).statistics.max) for j in [0,1,2,3,4]])\\nfor n in ['train_listens','train_likes','train_dislikes']:\\n d=pd.read_parquet('/task/data/'+n+'.parquet').head(10); print(n); print(d.to_string(index=False))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-08-31T18:28:19.643Z Script completed Wall time 2.3 seconds Output: rowgroups 42 0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)] 1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)] 40 1048576 [('uid', 965500, 989500), ('timestamp', 230, 24786710), ('item_id', 50, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)] 41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)] train_listens uid timestamp item_id is_organic played_ratio_pct track_length_seconds 100 39420 8326270 0 100 170 100 39420 1441281 0 100 105 100 39625 286361 0 100 185 100 40110 732449 0 100 240 100 40360 3397170 0 46 130 100 40380 7849270 0 100 205 100 40640 1449307 0 100 205 100 40785 6887232 0 100 145 100 40885 2996197 0 100 95 100 41130 6474571 0 100 245 train_likes uid timestamp item_id is_organic 100 44755 732449 1 100 1155860 6568592 0 100 1259125 5411243 1 100 1260005 7371186 0 100 1263935 4943655 0 100 2055015 410868 1 100 2494620 1730273 1 100 3939350 2621343 0 100 3940755 6399268 0 100 6902675 3982388 0 train_dislikes uid timestamp item_id is_organic 100 3087560 9170134 1 100 3936560 8661238 1 100 20432825 4927727 1 400 2980800 399811 1 400 3157520 1575108 1 400 3417600 9229946 1 400 4968885 6593949 1 400 4968905 9198404 1 400 5519800 999732 1 400 5754475 1254798 1 ## Step 18 · agent · 2026-08-31T18:28:39.564Z ### Thinking unavailable — no text recorded ## Step 19 · agent · 2026-08-31T18:28:58.213Z ### Thinking unavailable — no text recorded ## Step 20 · agent · 2026-08-31T18:29:16.856Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T18:29:35.547Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-08-31T18:29:51.351Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:32:57.473Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based PyTorch two-tower ranker.\\n+\\n+The trainer uses rolling, cutoff-correct user snapshots and sampled pairwise\\n+negatives. It never builds a user-item matrix. At serving time the item\\n+tower is evaluated once for the catalog and each request evaluates one user\\n+tower vector followed by inner-product retrieval.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+WEEK = 7 * 24 * 3600\\n+GAP = 1800\\n+HASH_SIZE = 131_071\\n+HIST_N = 128\\n+PREF_N = 64\\n+\\n+\\n+def seed_all(seed: int) -> None:\\n+ random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\\n+\\n+\\n+def hash_items(values: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Stable multiplicative hash; zero is reserved for padding.\\\"\\\"\\\"\\n+ x = values.astype(np.uint64, copy=False)\\n+ return ((x * np.uint64(11400714819323198485) % np.uint64(HASH_SIZE)) + 1).astype(np.int32)\\n+\\n+\\n+def read_listens(include_val: bool) -> pd.DataFrame:\\n+ cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ frames = [pd.read_parquet(DATA / \\\"train_listens.parquet\\\", columns=cols)]\\n+ if include_val:\\n+ frames.append(pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=cols))\\n+ x = pd.concat(frames, ignore_index=True)\\n+ # Conventional positive history. Very short/skipped plays are deliberately omitted.\\n+ x = x[x.played_ratio_pct >= 50].copy()\\n+ x.sort_values([\\\"uid\\\", \\\"timestamp\\\"], inplace=True, kind=\\\"stable\\\")\\n+ return x\\n+\\n+\\n+def read_actions(family: str, include_val: bool) -> pd.DataFrame:\\n+ opposite = \\\"un\\\" + family\\n+ cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"]\\n+ frames = []\\n+ for prefix in [\\\"train\\\"] + ([\\\"val\\\"] if include_val else []):\\n+ a = pd.read_parquet(DATA / f\\\"{prefix}_{family}.parquet\\\", columns=cols)\\n+ a[\\\"active\\\"] = np.uint8(1); frames.append(a)\\n+ a = pd.read_parquet(DATA / f\\\"{prefix}_{opposite}.parquet\\\", columns=cols)\\n+ a[\\\"active\\\"] = np.uint8(0); frames.append(a)\\n+ x = pd.concat(frames, ignore_index=True)\\n+ x.sort_values([\\\"uid\\\", \\\"timestamp\\\"], inplace=True, kind=\\\"stable\\\")\\n+ return x\\n+\\n+\\n+def grouped_arrays(df: pd.DataFrame, columns: list[str], targets: set[int]) -> dict[int, tuple[np.ndarray, ...]]:\\n+ df = df[df.uid.isin(targets)]\\n+ ans = {}\\n+ for uid, g in df.groupby(\\\"uid\\\", sort=False):\\n+ ans[int(uid)] = tuple(g[c].to_numpy(copy=True) for c in columns)\\n+ return ans\\n+\\n+\\n+def latest_distinct(ids: np.ndarray, ts: np.ndarray, end: int, n: int,\\n+ extra_weight: np.ndarray | None = None) -> tuple[np.ndarray, np.ndarray]:\\n+ k = int(np.searchsorted(ts, end, side=\\\"right\\\"))\\n+ # Inspect a generous recent suffix; repeated tracks are deduplicated.\\n+ lo = max(0, k - n * 8)\\n+ seen: set[int] = set(); chosen: list[int] = []; weights: list[float] = []\\n+ for j in range(k - 1, lo - 1, -1):\\n+ item = int(ids[j])\\n+ if item in seen: continue\\n+ seen.add(item); chosen.append(item)\\n+ age = max(0, end - int(ts[j]))\\n+ recency = math.exp(-age / (45.0 * 86400.0))\\n+ weights.append(recency * (float(extra_weight[j]) if extra_weight is not None else 1.0))\\n+ if len(chosen) == n: break\\n+ if not chosen:\\n+ return np.zeros(n, np.int32), np.zeros(n, np.float32)\\n+ h = hash_items(np.asarray(chosen, dtype=np.uint64))\\n+ out = np.zeros(n, np.int32); w = np.zeros(n, np.float32)\\n+ out[:len(h)] = h; w[:len(h)] = np.asarray(weights, np.float32)\\n+ return out, w\\n+\\n+\\n+def active_distinct(events: tuple[np.ndarray, ...] | None, end: int, n: int) -> tuple[np.ndarray, np.ndarray]:\\n+ if events is None:\\n+ return np.zeros(n, np.int32), np.zeros(n, np.float32)\\n+ ts, ids, active = events\\n+ k = int(np.searchsorted(ts, end, side=\\\"right\\\"))\\n+ seen: set[int] = set(); chosen: list[int] = []; weights: list[float] = []\\n+ for j in range(k - 1, -1, -1):\\n+ item = int(ids[j])\\n+ if item in seen: continue\\n+ seen.add(item)\\n+ if int(active[j]):\\n+ chosen.append(item)\\n+ weights.append(math.exp(-max(0, end-int(ts[j])) / (180.0*86400.0)))\\n+ if len(chosen) == n: break\\n+ h = hash_items(np.asarray(chosen, dtype=np.uint64)) if chosen else np.empty(0, np.int32)\\n+ out = np.zeros(n, np.int32); w = np.zeros(n, np.float32)\\n+ out[:len(h)] = h; w[:len(h)] = np.asarray(weights, np.float32)\\n+ return out, w\\n+\\n+\\n+@dataclass\\n+class Prepared:\\n+ uids: np.ndarray\\n+ hist: np.ndarray\\n+ hist_w: np.ndarray\\n+ likes: np.ndarray\\n+ likes_w: np.ndarray\\n+ dislikes: np.ndarray\\n+ dislikes_w: np.ndarray\\n+ numeric: np.ndarray\\n+ sample_snapshot: np.ndarray\\n+ sample_item: np.ndarray\\n+ sample_weight: np.ndarray\\n+ inference_rows: np.ndarray\\n+\\n+\\n+def prepare(refit: bool, snapshots: int, cache: Path) -> Prepared:\\n+ if cache.exists():\\n+ z = np.load(cache)\\n+ return Prepared(**{k: z[k] for k in Prepared.__annotations__})\\n+ targets_df = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ uids = targets_df.uid.to_numpy(np.int64); targets = set(map(int, uids))\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ item_to_idx = {int(x): i for i, x in enumerate(catalog.item_id)}\\n+ listens = read_listens(include_val=refit)\\n+ # Per-event history strength preserves organic intent and completion.\\n+ listens[\\\"strength\\\"] = (0.55 + 0.45*np.minimum(listens.played_ratio_pct,100)/100.0) * (1.0 + 0.35*listens.is_organic)\\n+ lg = grouped_arrays(listens, [\\\"timestamp\\\", \\\"item_id\\\", \\\"strength\\\", \\\"is_organic\\\"], targets)\\n+ del listens\\n+ likes = grouped_arrays(read_actions(\\\"likes\\\", refit), [\\\"timestamp\\\", \\\"item_id\\\", \\\"active\\\"], targets)\\n+ dislikes = grouped_arrays(read_actions(\\\"dislikes\\\", refit), [\\\"timestamp\\\", \\\"item_id\\\", \\\"active\\\"], targets)\\n+\\n+ # Rolling train-only windows, plus validation as a clean label window when refitting.\\n+ cutoffs = [TRAIN_END - (snapshots-i)*(WEEK+GAP) for i in range(snapshots)]\\n+ if refit:\\n+ cutoffs.append(TRAIN_END)\\n+ infer_end = TRAIN_END if not refit else int(pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=[\\\"timestamp\\\"]).timestamp.max())\\n+ all_cutoffs = cutoffs + [infer_end]\\n+ nrows = len(all_cutoffs)*len(uids)\\n+ hist=np.zeros((nrows,HIST_N),np.int32); hist_w=np.zeros((nrows,HIST_N),np.float32)\\n+ li=np.zeros((nrows,PREF_N),np.int32); li_w=np.zeros((nrows,PREF_N),np.float32)\\n+ di=np.zeros((nrows,PREF_N),np.int32); di_w=np.zeros((nrows,PREF_N),np.float32)\\n+ numeric=np.zeros((nrows,6),np.float32)\\n+ for ci, cutoff in enumerate(all_cutoffs):\\n+ for ui, uid in enumerate(uids):\\n+ r=ci*len(uids)+ui; ev=lg.get(int(uid))\\n+ if ev is not None:\\n+ ts, ids, strength, organic = ev\\n+ hist[r],hist_w[r]=latest_distinct(ids,ts,cutoff,HIST_N,strength)\\n+ k=int(np.searchsorted(ts,cutoff,\\\"right\\\")); lo=max(0,k-512)\\n+ numeric[r,0]=math.log1p(k)\\n+ numeric[r,1]=math.log1p(max(0,k-lo))\\n+ numeric[r,2]=float(np.mean(organic[lo:k])) if k>lo else 0\\n+ numeric[r,3]=float(np.mean(strength[lo:k])) if k>lo else 0\\n+ li[r],li_w[r]=active_distinct(likes.get(int(uid)),cutoff,PREF_N)\\n+ di[r],di_w[r]=active_distinct(dislikes.get(int(uid)),cutoff,PREF_N)\\n+ numeric[r,4]=math.log1p(float((li_w[r]>0).sum()))\\n+ numeric[r,5]=math.log1p(float((di_w[r]>0).sum()))\\n+\\n+ # Labels are organic >=80% listens and organic likes in the post-gap week.\\n+ train_l = pd.read_parquet(DATA/\\\"train_listens.parquet\\\",columns=[\\\"uid\\\",\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\",\\\"played_ratio_pct\\\"])\\n+ train_k = pd.read_parquet(DATA/\\\"train_likes.parquet\\\",columns=[\\\"uid\\\",\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\"])\\n+ if refit:\\n+ train_l=pd.concat([train_l,pd.read_parquet(DATA/\\\"val_listens.parquet\\\",columns=train_l.columns)],ignore_index=True)\\n+ train_k=pd.concat([train_k,pd.read_parquet(DATA/\\\"val_likes.parquet\\\",columns=train_k.columns)],ignore_index=True)\\n+ train_l=train_l[(train_l.uid.isin(targets))&(train_l.is_organic==1)&(train_l.played_ratio_pct>=80)]\\n+ train_k=train_k[(train_k.uid.isin(targets))&(train_k.is_organic==1)]\\n+ uid_to_pos={int(u):i for i,u in enumerate(uids)}\\n+ ss=[]; si=[]; sw=[]\\n+ for ci,cutoff in enumerate(cutoffs):\\n+ hi=cutoff+GAP+WEEK\\n+ a=train_l[(train_l.timestamp>=cutoff+GAP)&(train_l.timestamp=cutoff+GAP)&(train_k.timestamp torch.Tensor:\\n+ x=np.column_stack([np.log1p(catalog.positive_listens),np.log1p(catalog.organic_positive_listens),-np.log1p(catalog.popularity_rank)]).astype(np.float32)\\n+ x=(x-x.mean(0))/x.std(0).clip(1e-5)\\n+ return torch.from_numpy(x)\\n+\\n+\\n+def train_model(prep: Prepared, epochs:int, seed:int, checkpoint:Path) -> TwoTower:\\n+ seed_all(seed); torch.set_num_threads(min(12,torch.get_num_threads()))\\n+ catalog=pd.read_parquet(DATA/\\\"candidate_catalog.parquet\\\")\\n+ model=TwoTower(item_features(catalog)); opt=torch.optim.AdamW(model.parameters(),lr=2.5e-3,weight_decay=2e-5)\\n+ # A smoothed popularity sampler yields both hard head negatives and tail coverage.\\n+ prob=torch.from_numpy(np.power(catalog.positive_listens.to_numpy(np.float64)+20,.55)); prob=prob/prob.sum()\\n+ A={k:torch.from_numpy(getattr(prep,k)) for k in [\\\"hist\\\",\\\"hist_w\\\",\\\"likes\\\",\\\"likes_w\\\",\\\"dislikes\\\",\\\"dislikes_w\\\",\\\"numeric\\\"]}\\n+ snap=torch.from_numpy(prep.sample_snapshot.astype(np.int64)); pos=torch.from_numpy(prep.sample_item.astype(np.int64)); wt=torch.from_numpy(prep.sample_weight)\\n+ batch=384; neg_n=24\\n+ for ep in range(epochs):\\n+ order=torch.randperm(len(pos)); total=0.; seen=0\\n+ model.train()\\n+ for st in range(0,len(order),batch):\\n+ q=order[st:st+batch]; r=snap[q]; p=pos[q]\\n+ u=model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r])\\n+ pv=model.item_tower(p)\\n+ neg=torch.multinomial(prob,len(q)*neg_n,replacement=True).reshape(len(q),neg_n)\\n+ nv=model.item_tower(neg.flatten()).reshape(len(q),neg_n,-1)\\n+ ps=(u*pv).sum(1,keepdim=True); ns=torch.einsum('bd,bnd->bn',u,nv)\\n+ loss=(F.softplus((ns-ps)/.09).mean(1)*wt[q]).sum()/wt[q].sum().clamp_min(1)\\n+ opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\\n+ total+=float(loss)*len(q);seen+=len(q)\\n+ print(f\\\"epoch {ep+1}/{epochs} loss={total/seen:.5f}\\\")\\n+ torch.save({\\\"state_dict\\\":model.state_dict(),\\\"seed\\\":seed},checkpoint)\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(model:TwoTower, prep:Prepared, stage:str, output:Path) -> None:\\n+ model.eval(); rows=torch.from_numpy(prep.inference_rows.astype(np.int64))\\n+ A={k:torch.from_numpy(getattr(prep,k)) for k in [\\\"hist\\\",\\\"hist_w\\\",\\\"likes\\\",\\\"likes_w\\\",\\\"dislikes\\\",\\\"dislikes_w\\\",\\\"numeric\\\"]}\\n+ user=[]\\n+ for st in range(0,len(rows),256):\\n+ r=rows[st:st+256]; user.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]))\\n+ user=torch.cat(user).numpy(); nitems=len(model.item_tower.features); item=[]\\n+ for st in range(0,nitems,2048): item.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\\n+ item=np.concatenate(item)\\n+ with np.load(DATA/f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ euids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\n+ assert np.array_equal(euids,prep.uids)\\n+ frames=[]\\n+ for st in range(0,len(euids),128):\\n+ en=min(st+128,len(euids)); scores=user[st:en]@item.T\\n+ ok=np.unpackbits(packed[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ scores[~ok]=-np.inf\\n+ ix=np.argpartition(scores,-100,axis=1)[:,-100:]\\n+ ix=ix[np.arange(en-st)[:,None],np.argsort(-scores[np.arange(en-st)[:,None],ix],axis=1)]\\n+ for j in range(en-st):\\n+ frames.append(pd.DataFrame({'uid':euids[st+j],'item_id':items[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\\n+ pd.concat(frames,ignore_index=True).to_parquet(output,index=False)\\n+ print(f\\\"wrote {output}\\\")\\n+\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\\n+ ap.add_argument('--snapshots',type=int,default=10);ap.add_argument('--epochs',type=int,default=5);ap.add_argument('--seed',type=int,default=17)\\n+ ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\\n+ a=ap.parse_args();OUT.mkdir(exist_ok=True)\\n+ refit=a.stage=='test'; cache=OUT/f\\\"prepared_{'refit' if refit else 'validation'}_{a.snapshots}.npz\\\"\\n+ if a.rebuild_cache and cache.exists(): cache.unlink()\\n+ prep=prepare(refit,a.snapshots,cache); ckpt=OUT/f\\\"model_{a.stage}.pt\\\"\\n+ model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\\n+ (OUT/f\\\"run_{a.stage}.json\\\").write_text(json.dumps(vars(a),default=str,indent=2))\\n+\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch); text(result);" } ### Tool output · 2026-08-31T18:32:57.501Z Script completed Wall time 0.0 seconds Output: {} ## Step 23 · agent · 2026-08-31T18:32:59.571Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:10.020Z { "code": "const p = await tools.update_plan({explanation:\"Data and evaluator audit are complete; the popularity floor is 0.010710 NDCG@10 over 4,263 scored users. The scalable two-tower implementation is now in place and I’m beginning temporal training.\",\"plan\":[\n {step:\"Inspect schemas, data scale, eligibility, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history features and PyTorch two-tower training\",status:\"in_progress\"},\n {step:\"Tune on validation and save the best valid ranking\",status:\"pending\"},\n {step:\"Refit with validation history and generate final test submission\",status:\"pending\"},\n {step:\"Verify artifacts and document architecture, score, and command\",status:\"pending\"}\n]}); text(p);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/baseline.py\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --snapshots 10 --epochs 4 --seed 17 --output /app/solution/validation_twotower.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:33:11.203Z Script completed Wall time 1.2 seconds Output: {} SESSION_ID=35831 ## Step 24 · agent · 2026-08-31T18:33:13.180Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:14.507Z { "code": "const r = await tools.write_stdin({session_id:35831,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-08-31T18:33:25.526Z Script running with cell ID 12 Wall time 11.0 seconds Output: ## Step 25 · agent · 2026-08-31T18:33:27.979Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:33:28.743Z { "cell_id": "12", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:33:44.530Z Script completed Wall time 15.8 seconds Output: ## Step 26 · agent · 2026-08-31T18:33:46.658Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:48.298Z { "code": "const r = await tools.write_stdin({session_id:35831,chars:\"\",yield_time_ms:1000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:33:53.318Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"5d69f0","wall_time_seconds":5.000996939,"session_id":35831,"original_token_count":0,"output":""} ## Step 27 · agent · 2026-08-31T18:33:54.491Z ### Thinking unavailable — no text recorded The implementation has passed syntax checks and is building ten weekly, cutoff-correct training snapshots. This preprocessing is a one-time cached step; subsequent hyperparameter runs reuse it. The baseline artifact remains saved while the first neural run trains. ### exec · 2026-08-31T18:33:57.980Z { "code": "const r = await tools.write_stdin({session_id:35831,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:34:09.001Z Script running with cell ID 14 Wall time 11.0 seconds Output: ## Step 28 · agent · 2026-08-31T18:34:10.815Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:34:11.607Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:34:28.005Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"93161e","wall_time_seconds":30.00120954,"session_id":35831,"original_token_count":92,"output":"prepared 10 snapshots, 1,651,352 positive pairs\r\n/app/solution/train_ranker.py:264: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n total+=float(loss)*len(q);seen+=len(q)\r\n"} ## Step 29 · agent · 2026-08-31T18:34:31.353Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:34:32.773Z { "code": "const r = await tools.write_stdin({session_id:35831,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:34:43.813Z Script running with cell ID 15 Wall time 11.0 seconds Output: ## Step 30 · agent · 2026-08-31T18:34:45.154Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:34:45.952Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:35:02.821Z Script completed Wall time 16.9 seconds Output: {"chunk_id":"beac5e","wall_time_seconds":30.001837851,"session_id":35831,"original_token_count":0,"output":""} ## Step 31 · agent · 2026-08-31T18:35:13.630Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:35:15.154Z { "code": "const r = await tools.write_stdin({session_id:35831,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:35:15.701Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"2884b9","wall_time_seconds":0.411952634,"exit_code":1,"original_token_count":402,"output":"^CTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 308, in \r\n if __name__=='__main__': main()\r\n ^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 304, in main\r\n model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 263, in train_model\r\n opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\r\n ^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 516, in wrapper\r\n out = func(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 81, in _use_grad\r\n ret = func(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 247, in step\r\n adam(\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 149, in maybe_fallback\r\n return func(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 949, in adam\r\n func(\r\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 533, in _single_tensor_adam\r\n denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)\r\n ^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n"} ## Step 32 · agent · 2026-08-31T18:35:35.238Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-08-31T18:35:53.890Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-08-31T18:36:00.348Z ### Thinking unavailable — no text recorded The first cache exposed an important modeling issue: raw rolling labels contain about 1.65M repeat-play pairs, while the evaluation only rewards tracks that survive platform eligibility. I stopped that run before it could overwrite the saved valid baseline. I’m tightening snapshot labels to tracks not previously consumed at that cutoff and switching the large embedding tables to sparse optimization, which better matches both the discovery target and production-scale training. ### exec · 2026-08-31T18:36:39.049Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- listens = read_listens(include_val=refit)\\n+ listens = read_listens(include_val=refit)\\n+ # Approximate the platform's historical-consumption exclusion at each old\\n+ # snapshot. Only catalog tracks need this compact first-seen lookup.\\n+ first_seen = (listens[listens.item_id.isin(item_to_idx)]\\n+ .groupby([\\\"uid\\\", \\\"item_id\\\"], sort=False).timestamp.min())\\n@@\\n- for (u,x),g in grades.items():\\n- ss.append(ci*len(uids)+uid_to_pos[u]); si.append(item_to_idx[x]); sw.append(3.5 if g==3 else 1.0)\\n+ if grades:\\n+ keys=list(grades)\\n+ seen_at=first_seen.reindex(pd.MultiIndex.from_tuples(keys,names=[\\\"uid\\\",\\\"item_id\\\"])).to_numpy()\\n+ for (u,x),seen in zip(keys,seen_at):\\n+ # Discovery labels must be novel as of the snapshot cutoff.\\n+ if not np.isnan(seen) and seen <= cutoff:\\n+ continue\\n+ g=grades[(u,x)]\\n+ ss.append(ci*len(uids)+uid_to_pos[u]); si.append(item_to_idx[x]); sw.append(3.5 if g==3 else 1.0)\\n@@\\n- self.history_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\\n- self.like_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\\n- self.dislike_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\\n+ self.history_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\\n+ self.like_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\\n+ self.dislike_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\\n@@\\n- super().__init__(); self.embedding=nn.Embedding(len(features),dim); self.register_buffer(\\\"features\\\",features)\\n+ super().__init__(); self.embedding=nn.Embedding(len(features),dim,sparse=True); self.register_buffer(\\\"features\\\",features)\\n@@\\n- model=TwoTower(item_features(catalog)); opt=torch.optim.AdamW(model.parameters(),lr=2.5e-3,weight_decay=2e-5)\\n+ model=TwoTower(item_features(catalog))\\n+ sparse=[model.user_tower.history_embedding.weight,model.user_tower.like_embedding.weight,\\n+ model.user_tower.dislike_embedding.weight,model.item_tower.embedding.weight]\\n+ sparse_ids={id(p) for p in sparse}; dense=[p for p in model.parameters() if id(p) not in sparse_ids]\\n+ dense_opt=torch.optim.AdamW(dense,lr=2.5e-3,weight_decay=2e-5)\\n+ sparse_opt=torch.optim.SparseAdam(sparse,lr=6e-3)\\n@@\\n- batch=384; neg_n=24\\n+ batch=512; neg_n=16\\n@@\\n- opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\\n- total+=float(loss)*len(q);seen+=len(q)\\n+ dense_opt.zero_grad(set_to_none=True); sparse_opt.zero_grad(set_to_none=True)\\n+ loss.backward(); dense_opt.step(); sparse_opt.step()\\n+ total+=float(loss.detach())*len(q);seen+=len(q)\\n*** End Patch\";\nconst r = await tools.apply_patch(patch); text(r);\nconst e = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --snapshots 10 --epochs 4 --seed 17 --rebuild-cache --output /app/solution/validation_twotower.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(e.output); if(e.session_id) text(`SESSION_ID=${e.session_id}`);\n" } ### Tool output · 2026-08-31T18:36:40.232Z Script completed Wall time 1.2 seconds Output: {} SESSION_ID=90209 ## Step 35 · agent · 2026-08-31T18:36:41.740Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:43.116Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:36:54.143Z Script running with cell ID 18 Wall time 11.0 seconds Output: ## Step 36 · agent · 2026-08-31T18:36:55.617Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:36:56.444Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:37:13.145Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"779594","wall_time_seconds":30.000779315,"session_id":90209,"original_token_count":0,"output":""} ## Step 37 · agent · 2026-08-31T18:37:14.569Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:15.978Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:37:26.996Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 38 · agent · 2026-08-31T18:37:28.421Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:29.198Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:37:46.001Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"474f87","wall_time_seconds":30.001527641,"session_id":90209,"original_token_count":12,"output":"prepared 10 snapshots, 429,825 positive pairs\r\n"} ## Step 39 · agent · 2026-08-31T18:37:47.355Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:48.736Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:37:59.764Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T18:38:01.244Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:02.023Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:38:18.768Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"e1a340","wall_time_seconds":30.001606743,"session_id":90209,"original_token_count":0,"output":""} ## Step 41 · agent · 2026-08-31T18:38:20.196Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:21.588Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:38:32.614Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 42 · agent · 2026-08-31T18:38:34.295Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:35.062Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:38:51.617Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"ecefdd","wall_time_seconds":30.000411758,"session_id":90209,"original_token_count":6,"output":"epoch 1/4 loss=0.52608\r\n"} ## Step 43 · agent · 2026-08-31T18:38:53.380Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:54.786Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:39:05.813Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T18:39:07.309Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:08.079Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:39:24.817Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"5c4056","wall_time_seconds":30.000317509,"session_id":90209,"original_token_count":6,"output":"epoch 2/4 loss=0.41360\r\n"} ## Step 45 · agent · 2026-08-31T18:39:25.924Z ### Thinking unavailable — no text recorded The revised dataset now has 429,825 novel discovery pairs across ten weeks—much closer to the public window’s cutoff-correct density. Sparse training is progressing normally: pairwise loss fell from 0.526 to 0.414 by epoch 2. Once this run scores, I’ll use the result to decide whether a short second seed/epoch variant is worth the remaining time. ### exec · 2026-08-31T18:39:30.638Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:39:41.667Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T18:39:43.187Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:43.999Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:40:00.673Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"89f64c","wall_time_seconds":30.001121858,"session_id":90209,"original_token_count":6,"output":"epoch 3/4 loss=0.33438\r\n"} ## Step 47 · agent · 2026-08-31T18:40:02.105Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:03.458Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:40:14.483Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 48 · agent · 2026-08-31T18:40:15.874Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:16.646Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:40:33.487Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"7e2228","wall_time_seconds":30.001005755,"session_id":90209,"original_token_count":0,"output":""} ## Step 49 · agent · 2026-08-31T18:40:34.846Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:36.223Z { "code": "const r = await tools.write_stdin({session_id:90209,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:40:41.103Z Script completed Wall time 4.9 seconds Output: {"chunk_id":"b23e9c","wall_time_seconds":4.854972228,"exit_code":0,"original_token_count":19,"output":"epoch 4/4 loss=0.27620\r\nwrote /app/solution/validation_twotower.parquet\r\n"} ## Step 50 · agent · 2026-08-31T18:40:42.460Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:45.008Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower.parquet\\nls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-08-31T18:40:48.433Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation_twotower.parquet", "scores": { "quality_ndcg@10": 0.014333, "quality_users": 4263 } } total 182M drwxr-xr-x 2 agent agent 4.0K Aug 31 18:36 __pycache__ -rw-r--r-- 1 agent agent 1.3K Aug 31 18:27 baseline.py -rw-r--r-- 1 agent agent 104M Aug 31 18:40 model_validation.pt -rw-r--r-- 1 agent agent 76M Aug 31 18:37 prepared_validation_10.npz -rw-r--r-- 1 agent agent 157 Aug 31 18:40 run_validation.json -rw-r--r-- 1 agent agent 17K Aug 31 18:36 train_ranker.py -rw-r--r-- 1 agent agent 712K Aug 31 18:27 validation_popularity.parquet -rw-r--r-- 1 agent agent 1.8M Aug 31 18:40 validation_twotower.parquet ## Step 51 · agent · 2026-08-31T18:41:08.418Z ### Thinking unavailable — no text recorded ## Step 52 · agent · 2026-08-31T18:41:13.711Z ### Thinking unavailable — no text recorded The first neural ranker scores 0.014333 NDCG@10, a 33.8% lift over popularity’s 0.010710, and it is saved intact. I’m now calibrating a small catalog-popularity coordinate alongside the learned 64-D taste vector. This remains an exact dot product: the user tower emits a constant gate coordinate and the item tower emits the corresponding popularity feature, so offline item precomputation and one-vector request-time retrieval are preserved. ### exec · 2026-08-31T18:41:41.728Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-@torch.inference_mode()\\n-def rank(model:TwoTower, prep:Prepared, stage:str, output:Path) -> None:\\n+@torch.inference_mode()\\n+def rank(model:TwoTower, prep:Prepared, stage:str, output:Path, pop_blend:float=0.0) -> None:\\n@@\\n- item=np.concatenate(item)\\n+ item=np.concatenate(item)\\n+ # This calibrated prior is still an exact inner product: conceptually append\\n+ # sqrt(alpha) to every user vector and sqrt(alpha)*popularity to each item.\\n+ # Centering has no effect on ranks but keeps scales numerically convenient.\\n+ pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32)); pop=(pop-pop.mean())/pop.std()\\n@@\\n- en=min(st+128,len(euids)); scores=user[st:en]@item.T\\n+ en=min(st+128,len(euids)); scores=user[st:en]@item.T + pop_blend*pop[None,:]\\n@@\\n- ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\\n+ ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\\n+ ap.add_argument('--pop-blend',type=float,default=0.0)\\n@@\\n- model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\\n+ model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output,a.pop_blend)\\n*** End Patch\";\nconst a=await tools.apply_patch(patch); text(a);\nconst r=await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport torch, pandas as pd\\nfrom train_ranker import *\\nprep=prepare(False,10,OUT/'prepared_validation_10.npz')\\ncat=pd.read_parquet(DATA/'candidate_catalog.parquet')\\nmodel=TwoTower(item_features(cat));model.load_state_dict(torch.load(OUT/'model_validation.pt',weights_only=True)['state_dict'])\\nfor alpha in [.01,.02,.04,.07,.10,.15,.25]:\\n rank(model,prep,'validation',OUT/f'validation_blend_{alpha:.2f}.parquet',alpha)\\nPY\\nfor f in /app/solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:41:42.910Z Script completed Wall time 1.2 seconds Output: {} SESSION_ID=13333 ## Step 53 · agent · 2026-08-31T18:41:44.215Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:45.610Z { "code": "const r = await tools.write_stdin({session_id:13333,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:41:56.633Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 54 · agent · 2026-08-31T18:41:58.321Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:59.101Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:42:15.639Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"a2c3f8","wall_time_seconds":30.001642831,"session_id":13333,"original_token_count":195,"output":"wrote /app/solution/validation_blend_0.01.parquet\r\nwrote /app/solution/validation_blend_0.02.parquet\r\nwrote /app/solution/validation_blend_0.04.parquet\r\nwrote /app/solution/validation_blend_0.07.parquet\r\nwrote /app/solution/validation_blend_0.10.parquet\r\nwrote /app/solution/validation_blend_0.15.parquet\r\nwrote /app/solution/validation_blend_0.25.parquet\r\n{ \"model\": \"/app/solution/validation_blend_0.01.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016628, \"quality_users\": 4263 } } \r\n{ \"model\": \"/app/solution/validation_blend_0.02.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019111, \"quality_users\": 4263 } } \r\n{ \"model\": \"/app/solution/validation_blend_0.04.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019848, \"quality_users\": 4263 } } \r\n"} ## Step 55 · agent · 2026-08-31T18:42:17.163Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:18.553Z { "code": "const r = await tools.write_stdin({session_id:13333,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:42:28.781Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"0b275f","wall_time_seconds":10.208251013,"exit_code":0,"original_token_count":141,"output":"{ \"model\": \"/app/solution/validation_blend_0.07.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018106, \"quality_users\": 4263 } } \r\n{ \"model\": \"/app/solution/validation_blend_0.10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01585, \"quality_users\": 4263 } } \r\n{ \"model\": \"/app/solution/validation_blend_0.15.parquet\", \"scores\": { \"quality_ndcg@10\": 0.013763, \"quality_users\": 4263 } } \r\n{ \"model\": \"/app/solution/validation_blend_0.25.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01249, \"quality_users\": 4263 } } \r\n"} ## Step 56 · agent · 2026-08-31T18:42:37.973Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:47.378Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom train_ranker import *\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nprep=prepare(False,10,OUT/'prepared_validation_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet')\\nm=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/'model_validation.pt',weights_only=True)['state_dict'])\\nfor a in [.028,.034,.040,.046,.052]:rank(m,prep,'validation',OUT/f'validation_blend_{a:.3f}.parquet',a)\\nPY\\nfor f in /app/solution/validation_blend_0.0??.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tail -n 4 | head -n 1 | tr '\\\\n' ' '; echo \\\" $f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:42:48.555Z Script completed Wall time 1.2 seconds Output: SESSION_ID=22818 ## Step 57 · agent · 2026-08-31T18:42:50.120Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:51.553Z { "code": "const r = await tools.write_stdin({session_id:22818,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:02.578Z Script running with cell ID 31 Wall time 11.0 seconds Output: ## Step 58 · agent · 2026-08-31T18:43:04.151Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:04.887Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:43:21.576Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"0f0169","wall_time_seconds":30.001366132,"session_id":22818,"original_token_count":145,"output":"wrote /app/solution/validation_blend_0.028.parquet\r\nwrote /app/solution/validation_blend_0.034.parquet\r\nwrote /app/solution/validation_blend_0.040.parquet\r\nwrote /app/solution/validation_blend_0.046.parquet\r\nwrote /app/solution/validation_blend_0.052.parquet\r\n \"quality_ndcg@10\": 0.020288, /app/solution/validation_blend_0.028.parquet\r\n \"quality_ndcg@10\": 0.020108, /app/solution/validation_blend_0.034.parquet\r\n \"quality_ndcg@10\": 0.019848, /app/solution/validation_blend_0.040.parquet\r\n \"quality_ndcg@10\": 0.01979, /app/solution/validation_blend_0.046.parquet\r\n"} ## Step 59 · agent · 2026-08-31T18:43:23.587Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:24.978Z { "code": "const r = await tools.write_stdin({session_id:22818,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:25.014Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"225e2a","wall_time_seconds":0.00000246,"exit_code":0,"original_token_count":20,"output":" \"quality_ndcg@10\": 0.019272, /app/solution/validation_blend_0.052.parquet\r\n"} ## Step 60 · agent · 2026-08-31T18:43:30.561Z ### Thinking unavailable — no text recorded The calibrated dot-product model reaches 0.020288 at a popularity-coordinate weight of 0.028—about 89% above the floor and 42% above the uncalibrated tower. I’m testing one independent initialization next; if averaging the two learned vector spaces is better, the final serving vector will simply concatenate the two tower outputs (plus the scalar popularity coordinate), still allowing one exact MIPS query. ### exec · 2026-08-31T18:43:38.471Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom train_ranker import *\\nprep=prepare(False,10,OUT/'prepared_validation_10.npz')\\ntrain_model(prep,4,29,OUT/'model_validation_seed29.pt')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:43:39.648Z Script completed Wall time 1.2 seconds Output: SESSION_ID=15548 ## Step 61 · agent · 2026-08-31T18:43:41.212Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:42.629Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:53.647Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 62 · agent · 2026-08-31T18:43:55.144Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:55.939Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:44:12.653Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"efc962","wall_time_seconds":30.001552879,"session_id":15548,"original_token_count":0,"output":""} ## Step 63 · agent · 2026-08-31T18:44:13.980Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:15.373Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:44:26.397Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 64 · agent · 2026-08-31T18:44:27.890Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:28.655Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:44:45.401Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"be6264","wall_time_seconds":30.001009003,"session_id":15548,"original_token_count":6,"output":"epoch 1/4 loss=0.52253\r\n"} ## Step 65 · agent · 2026-08-31T18:44:46.848Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:48.318Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:44:59.345Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 66 · agent · 2026-08-31T18:45:00.918Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:01.731Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:45:18.349Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"299da0","wall_time_seconds":30.001089428,"session_id":15548,"original_token_count":6,"output":"epoch 2/4 loss=0.41007\r\n"} ## Step 67 · agent · 2026-08-31T18:45:19.794Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:21.201Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:45:32.227Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 68 · agent · 2026-08-31T18:45:33.767Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:34.531Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:45:51.235Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"b89f0a","wall_time_seconds":30.004557047,"session_id":15548,"original_token_count":0,"output":""} ## Step 69 · agent · 2026-08-31T18:45:52.597Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:53.981Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:46:05.008Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 70 · agent · 2026-08-31T18:46:06.557Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:07.325Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:46:24.012Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"d8b613","wall_time_seconds":30.000392921,"session_id":15548,"original_token_count":6,"output":"epoch 3/4 loss=0.33473\r\n"} ## Step 71 · agent · 2026-08-31T18:46:25.563Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:26.870Z { "code": "const r = await tools.write_stdin({session_id:15548,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:46:37.896Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 72 · agent · 2026-08-31T18:46:39.501Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:40.336Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:46:43.398Z Script completed Wall time 3.1 seconds Output: {"chunk_id":"c51ec2","wall_time_seconds":16.498065902,"exit_code":0,"original_token_count":6,"output":"epoch 4/4 loss=0.27702\r\n"} ## Step 73 · agent · 2026-08-31T18:46:54.090Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:31.311Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n @torch.inference_mode()\\n def rank(model:TwoTower, prep:Prepared, stage:str, output:Path, pop_blend:float=0.0) -> None:\\n@@\\n print(f\\\"wrote {output}\\\")\\n+\\n+\\n+@torch.inference_mode()\\n+def rank_ensemble(models:list[TwoTower], prep:Prepared, stage:str, output:Path,\\n+ pop_blend:float=0.0) -> None:\\n+ \\\"\\\"\\\"Rank by a concatenated retrieval vector from independent tower seeds.\\\"\\\"\\\"\\n+ A={k:torch.from_numpy(getattr(prep,k)) for k in [\\\"hist\\\",\\\"hist_w\\\",\\\"likes\\\",\\\"likes_w\\\",\\\"dislikes\\\",\\\"dislikes_w\\\",\\\"numeric\\\"]}\\n+ rows=torch.from_numpy(prep.inference_rows.astype(np.int64)); users=[]; items_all=[]\\n+ for model in models:\\n+ model.eval(); ub=[]\\n+ for st in range(0,len(rows),256):\\n+ r=rows[st:st+256];ub.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\\n+ users.append(np.concatenate(ub)/math.sqrt(len(models)))\\n+ ib=[];nitems=len(model.item_tower.features)\\n+ for st in range(0,nitems,2048):ib.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\\n+ items_all.append(np.concatenate(ib)/math.sqrt(len(models)))\\n+ user=np.concatenate(users,axis=1);item=np.concatenate(items_all,axis=1)\\n+ pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32));pop=(pop-pop.mean())/pop.std()\\n+ with np.load(DATA/f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ euids=z['uids'].astype(np.int64);catalog_items=z['item_ids'].astype(np.int64);packed=z['packed_eligible'];frames=[]\\n+ assert np.array_equal(euids,prep.uids)\\n+ for st in range(0,len(euids),128):\\n+ en=min(st+128,len(euids));scores=user[st:en]@item.T+pop_blend*pop[None,:]\\n+ ok=np.unpackbits(packed[st:en],axis=1,count=nitems,bitorder='little').astype(bool);scores[~ok]=-np.inf\\n+ ix=np.argpartition(scores,-100,axis=1)[:,-100:]\\n+ ix=ix[np.arange(en-st)[:,None],np.argsort(-scores[np.arange(en-st)[:,None],ix],axis=1)]\\n+ for j in range(en-st):frames.append(pd.DataFrame({'uid':euids[st+j],'item_id':catalog_items[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\\n+ pd.concat(frames,ignore_index=True).to_parquet(output,index=False);print(f\\\"wrote {output}\\\")\\n*** End Patch\";\nconst a=await tools.apply_patch(patch); text(a);\nconst r=await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom train_ranker import *\\nimport torch,pandas as pd\\nprep=prepare(False,10,OUT/'prepared_validation_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet');models=[]\\nfor f in ['model_validation.pt','model_validation_seed29.pt']:\\n m=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/f,weights_only=True)['state_dict']);models.append(m)\\nfor a in [.018,.024,.028,.034,.040]:rank_ensemble(models,prep,'validation',OUT/f'validation_ensemble_{a:.3f}.parquet',a)\\nPY\\nfor f in /app/solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tail -n 4 | head -n 1 | tr '\\\\n' ' '; echo \\\" $f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:47:32.492Z Script completed Wall time 1.2 seconds Output: {} SESSION_ID=21377 ## Step 74 · agent · 2026-08-31T18:47:33.910Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:35.330Z { "code": "const r = await tools.write_stdin({session_id:21377,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:47:46.357Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 75 · agent · 2026-08-31T18:47:48.027Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:48.788Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:48:05.356Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"1a49d7","wall_time_seconds":30.000728567,"session_id":21377,"original_token_count":152,"output":"wrote /app/solution/validation_ensemble_0.018.parquet\r\nwrote /app/solution/validation_ensemble_0.024.parquet\r\nwrote /app/solution/validation_ensemble_0.028.parquet\r\nwrote /app/solution/validation_ensemble_0.034.parquet\r\nwrote /app/solution/validation_ensemble_0.040.parquet\r\n \"quality_ndcg@10\": 0.023698, /app/solution/validation_ensemble_0.018.parquet\r\n \"quality_ndcg@10\": 0.024197, /app/solution/validation_ensemble_0.024.parquet\r\n \"quality_ndcg@10\": 0.024346, /app/solution/validation_ensemble_0.028.parquet\r\n \"quality_ndcg@10\": 0.024173, /app/solution/validation_ensemble_0.034.parquet\r\n"} ## Step 76 · agent · 2026-08-31T18:48:07.048Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:08.426Z { "code": "const r = await tools.write_stdin({session_id:21377,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:48:08.463Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"cdbe39","wall_time_seconds":0.00000213,"exit_code":0,"original_token_count":21,"output":" \"quality_ndcg@10\": 0.024254, /app/solution/validation_ensemble_0.040.parquet\r\n"} ## Step 77 · agent · 2026-08-31T18:48:24.706Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:44.769Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected two-seed model and write the hidden-test ranking.\\\"\\\"\\\"\\n+import pandas as pd\\n+from train_ranker import DATA, OUT, TwoTower, item_features, prepare, rank_ensemble, train_model\\n+\\n+\\n+def main() -> None:\\n+ snapshots, epochs, alpha = 10, 4, 0.028\\n+ prep = prepare(True, snapshots, OUT / \\\"prepared_refit_10.npz\\\")\\n+ models = []\\n+ for seed in (17, 29):\\n+ models.append(train_model(prep, epochs, seed, OUT / f\\\"model_test_seed{seed}.pt\\\"))\\n+ rank_ensemble(models, prep, \\\"test\\\", OUT / \\\"submission.parquet\\\", alpha)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst a=await tools.apply_patch(patch); text(a);\nconst p=await tools.update_plan({explanation:\"The selected two-seed concatenated tower plus dot-product popularity coordinate scores 0.024346 public NDCG@10. I’m preserving a structurally valid test fallback, then refitting the selected model with validation as observed history and labels.\",\"plan\":[\n {step:\"Inspect schemas, data scale, eligibility, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history features and PyTorch two-tower training\",status:\"completed\"},\n {step:\"Tune on validation and save the best valid ranking\",status:\"completed\"},\n {step:\"Refit with validation history and generate final test submission\",status:\"in_progress\"},\n {step:\"Verify artifacts and document architecture, score, and command\",status:\"pending\"}\n]}); text(p);\nconst r=await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/baseline.py --stage test --output /app/solution/submission.parquet\\n/opt/task-venv/bin/python -m py_compile /app/solution/build_submission.py\\n/opt/task-venv/bin/python /app/solution/build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:48:45.950Z Script completed Wall time 1.2 seconds Output: {} {} SESSION_ID=60523 ## Step 78 · agent · 2026-08-31T18:48:47.537Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:48.959Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:48:59.978Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:49:01.495Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:02.277Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:49:18.979Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"dd7aab","wall_time_seconds":30.000513583,"session_id":60523,"original_token_count":0,"output":""} ## Step 80 · agent · 2026-08-31T18:49:20.910Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:22.256Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:49:33.276Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T18:49:34.922Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:35.692Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:49:52.280Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"fa934d","wall_time_seconds":30.001703822,"session_id":60523,"original_token_count":12,"output":"prepared 11 snapshots, 474,619 positive pairs\r\n"} ## Step 82 · agent · 2026-08-31T18:49:54.442Z ### Thinking unavailable — no text recorded The selected public model is 0.024346 NDCG@10 (127% above popularity). Final refit preparation is complete with 474,619 cutoff-correct pairs across the ten historical windows plus the public validation window. A valid popularity-based `submission.parquet` is already in place as a fallback while the two final seeds train. ### exec · 2026-08-31T18:49:58.763Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:09.790Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 83 · agent · 2026-08-31T18:50:11.689Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:12.483Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:50:28.794Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"20935b","wall_time_seconds":30.000782588,"session_id":60523,"original_token_count":0,"output":""} ## Step 84 · agent · 2026-08-31T18:50:30.342Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:31.757Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:42.784Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T18:50:44.508Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:45.095Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:51:01.788Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"a4a799","wall_time_seconds":30.000296883,"session_id":60523,"original_token_count":6,"output":"epoch 1/4 loss=0.52378\r\n"} ## Step 86 · agent · 2026-08-31T18:51:03.110Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:04.496Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:15.522Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T18:51:19.204Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:19.985Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:51:34.531Z Script completed Wall time 14.5 seconds Output: {"chunk_id":"402fbd","wall_time_seconds":30.003642902,"session_id":60523,"original_token_count":6,"output":"epoch 2/4 loss=0.40924\r\n"} ## Step 88 · agent · 2026-08-31T18:51:36.032Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:37.470Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:48.497Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T18:51:50.098Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:50.917Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:52:07.500Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"4ef7d8","wall_time_seconds":30.000507496,"session_id":60523,"original_token_count":0,"output":""} ## Step 90 · agent · 2026-08-31T18:52:09.252Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:10.608Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:52:21.635Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 91 · agent · 2026-08-31T18:52:24.034Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:24.833Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:52:40.642Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"6f638b","wall_time_seconds":30.004452874,"session_id":60523,"original_token_count":6,"output":"epoch 3/4 loss=0.32919\r\n"} ## Step 92 · agent · 2026-08-31T18:52:42.020Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:43.395Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:52:54.421Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 93 · agent · 2026-08-31T18:52:56.081Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:56.835Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:53:13.425Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"45da10","wall_time_seconds":30.000683923,"session_id":60523,"original_token_count":6,"output":"epoch 4/4 loss=0.27473\r\n"} ## Step 94 · agent · 2026-08-31T18:53:15.077Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:16.524Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:53:27.548Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 95 · agent · 2026-08-31T18:53:29.217Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:30.009Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:53:46.552Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"7c65fb","wall_time_seconds":30.000858457,"session_id":60523,"original_token_count":0,"output":""} ## Step 96 · agent · 2026-08-31T18:53:47.927Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:49.350Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:54:00.374Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 97 · agent · 2026-08-31T18:54:01.919Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:02.704Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:54:19.378Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"8cb1b1","wall_time_seconds":30.000323916,"session_id":60523,"original_token_count":6,"output":"epoch 1/4 loss=0.52018\r\n"} ## Step 98 · agent · 2026-08-31T18:54:20.681Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:22.072Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:54:33.097Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 99 · agent · 2026-08-31T18:54:34.940Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:35.744Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:54:52.101Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"70bd1b","wall_time_seconds":30.001329793,"session_id":60523,"original_token_count":6,"output":"epoch 2/4 loss=0.40396\r\n"} ## Step 100 · agent · 2026-08-31T18:54:55.720Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:57.107Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:55:08.135Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 101 · agent · 2026-08-31T18:55:09.578Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:10.523Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:55:27.138Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"97433f","wall_time_seconds":30.000873149,"session_id":60523,"original_token_count":0,"output":""} ## Step 102 · agent · 2026-08-31T18:55:28.509Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:29.896Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:55:40.925Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 103 · agent · 2026-08-31T18:55:42.420Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:43.190Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:55:59.929Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"a172c0","wall_time_seconds":30.001199924,"session_id":60523,"original_token_count":6,"output":"epoch 3/4 loss=0.32787\r\n"} ## Step 104 · agent · 2026-08-31T18:56:01.281Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:02.665Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:56:13.692Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 105 · agent · 2026-08-31T18:56:15.204Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:16.016Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:56:32.702Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"1a1f4c","wall_time_seconds":30.000992509,"session_id":60523,"original_token_count":6,"output":"epoch 4/4 loss=0.27493\r\n"} ## Step 106 · agent · 2026-08-31T18:56:34.332Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:35.714Z { "code": "const r = await tools.write_stdin({session_id:60523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:56:35.764Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"d7b5cc","wall_time_seconds":0.000002449,"exit_code":0,"original_token_count":10,"output":"wrote /app/solution/submission.parquet\r\n"} ## Step 107 · agent · 2026-08-31T18:56:56.108Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-08-31T18:56:58.032Z ### Thinking unavailable — no text recorded Final refit and test ranking are complete. Both seeds converged consistently (final losses 0.2747 and 0.2749), and the neural ranking has replaced the fallback submission. I’m doing the last contract audit now: exact 100×8,456 shape, integer ranks, uniqueness, per-user eligibility, and an explicit serving wrapper whose returned 129-D user/item vectors reproduce the ranking by dot product alone. ### exec · 2026-08-31T18:57:59.283Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n def __init__(self, features,dim=64): super().__init__(); self.user_tower=UserTower(dim); self.item_tower=ItemTower(features,dim)\\n+\\n+\\n+class ServingUserTower(nn.Module):\\n+ \\\"\\\"\\\"Concatenate seed towers and the calibrated global-prior coordinate.\\\"\\\"\\\"\\n+ def __init__(self, models:list[TwoTower], pop_blend:float):\\n+ super().__init__();self.towers=nn.ModuleList([m.user_tower for m in models])\\n+ self.register_buffer(\\\"gate\\\",torch.tensor(math.sqrt(pop_blend),dtype=torch.float32))\\n+ def forward(self,h,hw,l,lw,d,dw,n):\\n+ scale=math.sqrt(len(self.towers))\\n+ parts=[tower(h,hw,l,lw,d,dw,n)/scale for tower in self.towers]\\n+ return torch.cat(parts+[self.gate.expand(len(h),1)],dim=1)\\n+\\n+\\n+class ServingItemTower(nn.Module):\\n+ \\\"\\\"\\\"Return independently precomputable item vectors matching ServingUserTower.\\\"\\\"\\\"\\n+ def __init__(self, models:list[TwoTower], pop_blend:float):\\n+ super().__init__();self.towers=nn.ModuleList([m.item_tower for m in models])\\n+ n=len(models[0].item_tower.features)\\n+ pop=-torch.log1p(torch.arange(1,n+1,dtype=torch.float32));pop=(pop-pop.mean())/pop.std()\\n+ self.register_buffer(\\\"pop_coordinate\\\",math.sqrt(pop_blend)*pop)\\n+ def forward(self,idx):\\n+ scale=math.sqrt(len(self.towers));parts=[tower(idx)/scale for tower in self.towers]\\n+ return torch.cat(parts+[self.pop_coordinate[idx,None]],dim=1)\\n@@\\n def rank_ensemble(models:list[TwoTower], prep:Prepared, stage:str, output:Path,\\n pop_blend:float=0.0) -> None:\\n \\\"\\\"\\\"Rank by a concatenated retrieval vector from independent tower seeds.\\\"\\\"\\\"\\n@@\\n- for model in models:\\n- model.eval(); ub=[]\\n- for st in range(0,len(rows),256):\\n- r=rows[st:st+256];ub.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\\n- users.append(np.concatenate(ub)/math.sqrt(len(models)))\\n- ib=[];nitems=len(model.item_tower.features)\\n- for st in range(0,nitems,2048):ib.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\\n- items_all.append(np.concatenate(ib)/math.sqrt(len(models)))\\n- user=np.concatenate(users,axis=1);item=np.concatenate(items_all,axis=1)\\n- pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32));pop=(pop-pop.mean())/pop.std()\\n+ user_tower=ServingUserTower(models,pop_blend).eval();item_tower=ServingItemTower(models,pop_blend).eval()\\n+ ub=[]\\n+ for st in range(0,len(rows),256):\\n+ r=rows[st:st+256];ub.append(user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\\n+ user=np.concatenate(ub);nitems=len(models[0].item_tower.features);ib=[]\\n+ for st in range(0,nitems,2048):ib.append(item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\\n+ item=np.concatenate(ib)\\n@@\\n- en=min(st+128,len(euids));scores=user[st:en]@item.T+pop_blend*pop[None,:]\\n+ en=min(st+128,len(euids));scores=user[st:en]@item.T\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower discovery ranker\\n+\\n+## Model\\n+\\n+The request-time representation is a 129-dimensional vector returned by\\n+`ServingUserTower`; the independently precomputable catalog representation is\\n+the same size and is returned by `ServingItemTower`. The relevance score used\\n+to produce the submission is exactly `user_vector @ item_vector`.\\n+\\n+Each of two seeded 64-D user towers pools a user's latest 128 positive-listen\\n+history tokens plus up to 64 active likes and 64 active dislikes. Raw item IDs\\n+are multiplicatively hashed into sparse trainable history embeddings; event\\n+strength, organic intent, recency, and six activity statistics feed a\\n+192-unit GELU MLP. There is deliberately no user-ID embedding. Each matching\\n+item tower combines a trainable 30,000-track catalog embedding with log listen,\\n+organic-listen, and popularity features in a 128-unit GELU MLP. Seed outputs\\n+are normalized and concatenated; the final coordinate is the validation-tuned\\n+catalog-popularity prior (weight 0.028). This coordinate is emitted by both\\n+PyTorch serving towers, so it does not require post-retrieval reranking.\\n+\\n+## Training\\n+\\n+Training uses ten rolling seven-day temporal targets, each separated from its\\n+history by the required 30-minute gap. Targets are organic >=80% listens and\\n+organic likes; likes receive 3.5x loss weight. Previously consumed targets are\\n+removed to approximate the cutoff candidate boundary. The sampled pairwise\\n+softplus objective uses 16 popularity-smoothed negatives per positive. Sparse\\n+embedding parameters use `SparseAdam`; dense MLP parameters use `AdamW`.\\n+This event/snapshot pipeline does not construct or factorize a global\\n+user-item interaction matrix.\\n+\\n+The selected two-seed ensemble scored **0.024346 graded NDCG@10** on 4,263\\n+public validation users (popularity baseline: 0.010710). The final model is\\n+refit with validation interactions as observed history and its outcomes as the\\n+last training window before applying test-cutoff eligibility.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app/solution` with the supplied read-only task environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_submission.py\\n+```\\n+\\n+This writes `/app/solution/submission.parquet` with columns `uid`, `item_id`,\\n+and `rank`.\\n*** End Patch\";\nconst r=await tools.apply_patch(patch); text(r);\n" } ### Tool output · 2026-08-31T18:57:59.333Z Script completed Wall time 0.0 seconds Output: {} ## Step 109 · agent · 2026-08-31T18:58:19.085Z ### Thinking unavailable — no text recorded ## Step 110 · agent · 2026-08-31T18:58:20.953Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:40.031Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/*.py\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\np=Path('/app/solution/submission.parquet');df=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\\nn=validate_ranking(df,targets);cs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\\nelig=sum(cs.is_eligible(int(u),int(x)) for u,x in n[['uid','item_id']].itertuples(index=False,name=None))\\nprint('shape',n.shape,'users',n.uid.nunique(),'items',n.item_id.nunique(),'eligible',elig,'expected',len(n))\\nprint('dtypes',n.dtypes.to_dict(),'bytes',p.stat().st_size)\\n# Check that event-derived active state covers the authoritative supplied state.\\nz=np.load('/app/solution/prepared_refit_10.npz');rows=z['inference_rows'];uids=z['uids'];state=pd.read_parquet('/task/data/test_preference_state.parquet')\\nHASH=131071\\ndef h(a): return ((a.astype(np.uint64)*np.uint64(11400714819323198485)%np.uint64(HASH))+1).astype(np.int32)\\nby={(int(u),s):set(h(g.item_id.to_numpy())) for (u,s),g in state.groupby(['uid','state'])}\\nfor typ,key in [('liked','likes'),('disliked','dislikes')]:\\n overlaps=[]\\n for j,u in enumerate(uids):\\n got=set(z[key][rows[j]]);got.discard(0);truth=by.get((int(u),typ),set())\\n overlaps.append(len(got&truth)/max(1,len(got)))\\n print(typ,'derived tokens in authoritative state mean',round(float(np.mean(overlaps)),4))\\nPY\\nsha256sum /app/solution/submission.parquet\\nls -lh /app/solution/submission.parquet /app/solution/model_test_seed*.pt /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-08-31T18:58:51.057Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 111 · agent · 2026-08-31T18:58:52.697Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:53.451Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:59:10.223Z Script completed Wall time 16.8 seconds Output: ## Step 112 · agent · 2026-08-31T18:59:14.775Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:24.909Z { "code": "const r = await tools.exec_command({cmd:\"ps -ef | rg 'python|sha256' | rg -v 'rg|server' || true\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\np=Path('/app/solution/submission.parquet');df=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));n=validate_ranking(df,targets);cs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\\nbad=[(int(u),int(x)) for u,x in n[['uid','item_id']].itertuples(index=False,name=None) if not cs.is_eligible(int(u),int(x))]\\nprint('shape',n.shape,'users',n.uid.nunique(),'bad eligibility',len(bad));print(n.dtypes.to_dict())\\nPY\\nsha256sum /app/solution/submission.parquet\\nls -lh /app/solution/submission.parquet /app/solution/model_test_seed*.pt /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-08-31T18:59:27.809Z Script completed Wall time 2.9 seconds Output: /bin/sh: 1: ps: not found shape (845600, 3) users 8456 bad eligibility 0 {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')} a887ec7180971bda190d3b0eb5f09b6375db6f638112b7f3e6201e349502c3aa /app/solution/submission.parquet -rw-r--r-- 1 agent agent 2.2K Aug 31 18:57 /app/solution/README.md -rw-r--r-- 1 agent agent 104M Aug 31 18:53 /app/solution/model_test_seed17.pt -rw-r--r-- 1 agent agent 104M Aug 31 18:56 /app/solution/model_test_seed29.pt -rw-r--r-- 1 agent agent 1.6M Aug 31 18:56 /app/solution/submission.parquet ## Step 113 · agent · 2026-08-31T18:59:34.106Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:47.975Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport torch,pandas as pd\\nfrom train_ranker import *\\nprep=prepare(True,10,OUT/'prepared_refit_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet');models=[]\\nfor seed in (17,29):\\n m=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/f'model_test_seed{seed}.pt',weights_only=True)['state_dict']);models.append(m)\\nrank_ensemble(models,prep,'test',OUT/'submission.parquet',0.028)\\nPY\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\np=Path('/app/solution/submission.parquet');d=validate_ranking(pd.read_parquet(p),set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)));c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\\nassert all(c.is_eligible(u,x) for u,x in d[['uid','item_id']].itertuples(index=False,name=None))\\nassert not d.duplicated(['uid','item_id']).any()\\nprint(f'VALID rows={len(d)} users={d.uid.nunique()} ranks={d.groupby(\\\"uid\\\").size().unique().tolist()}')\\nPY\\nsha256sum /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000,\"tty\":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:59:49.155Z Script completed Wall time 1.2 seconds Output: SESSION_ID=92378 ## Step 114 · agent · 2026-08-31T18:59:50.731Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:52.122Z { "code": "const r = await tools.write_stdin({session_id:92378,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:59:59.328Z Script completed Wall time 7.2 seconds Output: {"chunk_id":"2164b1","wall_time_seconds":7.18348442,"exit_code":0,"original_token_count":46,"output":"wrote /app/solution/submission.parquet\r\nVALID rows=845600 users=8456 ranks=[100]\r\n7e565127afb62e2e3b0db808b53e2eeada6bea05114ce3605f5929f25fd52796 /app/solution/submission.parquet\r\n"} ## Step 115 · agent · 2026-08-31T19:00:04.190Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:13.262Z { "code": "const p = await tools.update_plan({explanation:\"The final two-seed model was refit on train plus validation history, its serving-vector path regenerated the test ranking, and the submission passed all structural and eligibility checks.\",\"plan\":[\n {step:\"Inspect schemas, data scale, eligibility, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history features and PyTorch two-tower training\",status:\"completed\"},\n {step:\"Tune on validation and save the best valid ranking\",status:\"completed\"},\n {step:\"Refit with validation history and generate final test submission\",status:\"completed\"},\n {step:\"Verify artifacts and document architecture, score, and command\",status:\"completed\"}\n]}); text(p);\nconst r=await tools.exec_command({cmd:\"find /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort | sed -n '1,120p'\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_0.028.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-08-31T19:00:16.701Z Script completed Wall time 3.4 seconds Output: {} README.md 2243 bytes baseline.py 1256 bytes build_submission.py 595 bytes model_test_seed17.pt 108983254 bytes model_test_seed29.pt 108983254 bytes model_validation.pt 108983231 bytes model_validation_seed29.pt 108983456 bytes prepared_refit_10.npz 86778697 bytes prepared_validation_10.npz 78787326 bytes run_validation.json 157 bytes submission.parquet 1652916 bytes train_ranker.py 20367 bytes validation_blend_0.01.parquet 1719948 bytes validation_blend_0.02.parquet 1676528 bytes validation_blend_0.028.parquet 1668686 bytes validation_blend_0.034.parquet 1662246 bytes validation_blend_0.04.parquet 1655783 bytes validation_blend_0.040.parquet 1655783 bytes validation_blend_0.046.parquet 1649353 bytes validation_blend_0.052.parquet 1643239 bytes validation_blend_0.07.parquet 1521386 bytes validation_blend_0.10.parquet 1399371 bytes validation_blend_0.15.parquet 1284968 bytes validation_blend_0.25.parquet 1176584 bytes validation_ensemble_0.018.parquet 1667107 bytes validation_ensemble_0.024.parquet 1661907 bytes validation_ensemble_0.028.parquet 1658385 bytes validation_ensemble_0.034.parquet 1652647 bytes validation_ensemble_0.040.parquet 1647202 bytes validation_popularity.parquet 728599 bytes validation_twotower.parquet 1794497 bytes { "model": "/app/solution/validation_ensemble_0.028.parquet", "scores": { "quality_ndcg@10": 0.024346, "quality_users": 4263 } } ## Step 116 · agent · 2026-08-31T19:00:18.844Z ### Thinking unavailable — no text recorded Completed the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/application code: [train_ranker.py](/app/solution/train_ranker.py) - Reproduction command: [build_submission.py](/app/solution/build_submission.py) - Documentation: [README.md](/app/solution/README.md) - Public validation NDCG@10: **0.024346** - Validated: 845,600 rows, 8,456 users, 100 unique eligible items per user - Serving score: exact dot product between independently computed 129-D user and item vectors - Submission SHA-256: `7e565127afb62e2e3b0db808b53e2eeada6bea05114ce3605f5929f25fd52796`