yambda-discovery-ranking-two-tower-base / run-002-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:26:19.812Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-08-31T18:26:22.257Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-08-31T18:26:22.257Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-08-31T18:26:22.257Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-08-31T18:26:22.257Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a05912-801b-7bc1-8f16-cb796ce900c4", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-08-31", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-08-31T18:26:22.267Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-08-31T18:26:25.885Z ### Thinking unavailable — no text recorded I’ll build and benchmark this end to end: first audit the data and evaluator, then implement a scalable sampled-event two-tower, tune it against the public window, refit with validation as history, and keep a valid final submission on disk throughout. I’ll keep all generated files under `/app/solution/` and use CPU-only PyTorch. ### exec · 2026-08-31T18:26:36.489Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Audit schemas, sizes, eligibility format, and public evaluator\",status:\"in_progress\"},\n {step:\"Implement scalable history-based PyTorch two-tower and ranking pipeline\",status:\"pending\"},\n {step:\"Train and tune variants on the public validation window\",status:\"pending\"},\n {step:\"Refit selected model on train + validation history\",status:\"pending\"},\n {step:\"Generate and validate final 100-per-user submission and README\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task | sort && du -h /task/data/* && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,280p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:36.562Z Script completed Wall time 0.1 seconds Output: /app /task/data/candidate_catalog.parquet /task/data/target_users.parquet /task/data/test_eligible_candidates.npz /task/data/test_preference_state.parquet /task/data/train_dislikes.parquet /task/data/train_likes.parquet /task/data/train_listens.parquet /task/data/train_undislikes.parquet /task/data/train_unlikes.parquet /task/data/val_dislikes.parquet /task/data/val_likes.parquet /task/data/val_listens.parquet /task/data/val_undislikes.parquet /task/data/val_unlikes.parquet /task/data/validation_eligible_candidates.npz /task/data/validation_preference_state.parquet /task/evaluate_public.py /task/ranking_evaluation.py /task/requirements.txt 344K /task/data/candidate_catalog.parquet 84K /task/data/target_users.parquet 7.4M /task/data/test_eligible_candidates.npz 1.1M /task/data/test_preference_state.parquet 840K /task/data/train_dislikes.parquet 5.2M /task/data/train_likes.parquet 305M /task/data/train_listens.parquet 156K /task/data/train_undislikes.parquet 1.8M /task/data/train_unlikes.parquet 36K /task/data/val_dislikes.parquet 292K /task/data/val_likes.parquet 9.5M /task/data/val_listens.parquet 8.0K /task/data/val_undislikes.parquet 72K /task/data/val_unlikes.parquet 7.3M /task/data/validation_eligible_candidates.npz 1.1M /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 · 2026-08-31T18:26:43.784Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:55.803Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'schema',pf.schema_arrow)\\n if p.name in ('candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet'):\\n x=pd.read_parquet(p)\\n print(x.head(), x.dtypes.to_dict(), 'nunique',x.nunique().to_dict())\\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\\n z=np.load(p)\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n m=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('eligible stats',np.quantile(m.sum(1),[0,.1,.5,.9,1]),'mean',m.sum(1).mean(),'catalog same head',z['item_ids'][:5])\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:55.942Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pyarrow' ## Step 9 · agent · 2026-08-31T18:26:59.042Z ### Thinking unavailable — no text recorded The default shell’s `python` is not the benchmark environment described by the task—it lacks even the declared Parquet engine. I’m locating the task’s pinned runtime now; this is an environment-path issue, not a data issue. ### exec · 2026-08-31T18:27:06.167Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\npython --version\\npython -m pip list 2>/dev/null | sed -n '1,120p'\\nfind /app /opt /usr/local -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -50\\nfind / -maxdepth 4 -type d -name 'site-packages' 2>/dev/null | head -50\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:06.950Z Script completed Wall time 0.8 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 Package Version ------- ------- pip 25.0.1 ## Step 10 · agent · 2026-08-31T18:27:10.884Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:14.853Z { "code": "const r = await tools.exec_command({cmd:\"env | sort | sed -n '1,160p'\\nfind / -type d \\\\( -name 'torch' -o -name 'pyarrow' \\\\) 2>/dev/null | head -50\\nls -la /app /task /usr/local/lib/python3.12/site-packages | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:15.118Z Script completed Wall time 0.3 seconds Output: CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05912-8007-7c33-a5cb-b744f166f479 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=d6d0a2b141b6 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex /opt/task-venv/lib/python3.12/site-packages/pyarrow /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch /app: total 12 drwxr-xr-x 3 agent agent 4096 Aug 31 18:23 . drwxr-xr-x 1 root root 4096 Aug 31 18:26 .. drwxr-xr-x 2 agent agent 4096 Aug 31 18:23 solution /task: total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:23 . drwxr-xr-x 1 root root 4096 Aug 31 18:26 .. -r--r--r-- 1 root root 0 Aug 31 18:09 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:10 data -r--r--r-- 1 root root 5774 Aug 31 18:09 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:09 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:09 requirements.txt /usr/local/lib/python3.12/site-packages: total 20 drwxr-xr-x 4 root root 4096 Aug 5 01:13 . drwxr-xr-x 38 root root 4096 Aug 5 01:13 .. -rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt drwxr-xr-x 5 root root 4096 Aug 5 01:13 pip drwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info ## Step 11 · agent · 2026-08-31T18:27:17.202Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:30.433Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /opt/task-venv/bin/python* && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print('\\\\n',p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema_arrow.names)\\n if p.name in ('candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet'):\\n x=pd.read_parquet(p)\\n print(x.head().to_string(index=False), x.dtypes.to_dict(), 'nunique',x.nunique().to_dict())\\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\\n z=np.load(p)\\n print('\\\\n',p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n m=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('eligible stats',np.quantile(m.sum(1),[0,.1,.5,.9,1]),'mean',m.sum(1).mean(),'head',z['item_ids'][:5])\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:33.016Z Script completed Wall time 2.6 seconds Output: lrwxrwxrwx 1 root root 21 Aug 31 18:23 /opt/task-venv/bin/python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 18:23 /opt/task-venv/bin/python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 18:23 /opt/task-venv/bin/python3.12 -> python candidate_catalog.parquet rows 30000 groups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] item_id positive_listens organic_positive_listens popularity_rank 5862961 26615 20620 1 6901374 25536 17347 2 5635052 24691 17194 3 3542184 24075 17699 4 9378983 24036 18195 5 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} nunique {'item_id': 30000, 'positive_listens': 3049, 'organic_positive_listens': 2019, 'popularity_rank': 30000} target_users.parquet rows 8456 groups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] uid recent_positive_listens history_positive_listens 100 844.0 2604 200 50.0 286 300 168.0 359 600 1985.0 2423 700 1776.0 6114 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} nunique {'uid': 8456, 'recent_positive_listens': 3100, 'history_positive_listens': 4880} test_preference_state.parquet rows 484776 groups 1 cols ['uid', 'item_id', 'state'] uid item_id state 100 410868 liked 100 732449 liked 100 982903 liked 100 1730273 liked 100 2621343 liked {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} nunique {'uid': 7092, 'item_id': 28863, 'state': 2} train_dislikes.parquet rows 101075 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet rows 814831 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet rows 43484159 groups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_undislikes.parquet rows 19960 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet rows 292665 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet rows 3563 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet rows 37795 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet rows 1501697 groups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_undislikes.parquet rows 606 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet rows 9346 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] validation_preference_state.parquet rows 469124 groups 1 cols ['uid', 'item_id', 'state'] uid item_id state 100 410868 liked 100 732449 liked 100 982903 liked 100 1730273 liked 100 2621343 liked {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} nunique {'uid': 7038, 'item_id': 28814, 'state': 2} validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible stats [24657. 28038.5 29242. 29852. 30000. ] mean 29067.306291390727 head [5862961 6901374 5635052 3542184 9378983] test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible stats [24552. 28001. 29220. 29835. 30000.] mean 29041.3178807947 head [5862961 6901374 5635052 3542184 9378983] ## Step 12 · agent · 2026-08-31T18:27:51.589Z ### Thinking unavailable — no text recorded The benchmark has 8,456 target users and 30,000 catalog items, with roughly 29,000 eligible items per user. I’m now measuring the temporal/repeat structure of validation relevance before fixing the training examples; that determines whether the strongest history-derived signal is recency, long-term affinity, explicit likes, or item-to-item generalization. The implementation will remain event-stream based—no global interaction matrix. ### exec · 2026-08-31T18:28:06.368Z { "code": "const r = await tools.exec_command({cmd:\"free -h && /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n for split in ['train','val']:\\n p=D/f'{split}_{fam}.parquet'; cols=['uid','timestamp','item_id','is_organic']\\n if fam=='listens': cols += ['played_ratio_pct','track_length_seconds']\\n x=pd.read_parquet(p,columns=cols)\\n print(split,fam,'rows',len(x),'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',int(x.timestamp.min()),int(x.timestamp.max()),'organic',round(x.is_organic.mean(),3))\\n if fam=='listens':\\n print(' ratios',x.played_ratio_pct.quantile([0,.25,.5,.8,.9,.99,1]).to_dict(),'pos50',float((x.played_ratio_pct>=50).mean()),'strong80',float((x.played_ratio_pct>=80).mean()))\\n del x\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:28:06.485Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: free: not found ## Step 13 · agent · 2026-08-31T18:28:09.353Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:21.057Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,6p' /proc/meminfo\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n for split in ['train','val']:\\n p=D/f'{split}_{fam}.parquet'; cols=['uid','timestamp','item_id','is_organic']\\n if fam=='listens': cols += ['played_ratio_pct','track_length_seconds']\\n x=pd.read_parquet(p,columns=cols)\\n print(split,fam,'rows',len(x),'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',int(x.timestamp.min()),int(x.timestamp.max()),'organic',round(x.is_organic.mean(),3))\\n if fam=='listens':\\n print(' ratios',x.played_ratio_pct.quantile([0,.25,.5,.8,.9,.99,1]).to_dict(),'pos50',round(float((x.played_ratio_pct>=50).mean()),3),'strong80',round(float((x.played_ratio_pct>=80).mean()),3))\\n del x\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:28:24.419Z Script completed Wall time 3.4 seconds Output: MemTotal: 15988980 kB MemFree: 1889232 kB MemAvailable: 14900856 kB Buffers: 227480 kB Cached: 12492928 kB SwapCached: 0 kB train listens rows 43484159 uids 9238 items 845395 time 0 24786795 organic 0.517 ratios {0.0: 0.0, 0.25: 7.0, 0.5: 100.0, 0.8: 100.0, 0.9: 100.0, 0.99: 100.0, 1.0: 159.0} pos50 0.632 strong80 0.592 val listens rows 1501697 uids 8196 items 191747 time 24788600 25393395 organic 0.517 ratios {0.0: 0.0, 0.25: 9.0, 0.5: 100.0, 0.8: 100.0, 0.9: 100.0, 0.99: 100.0, 1.0: 159.0} pos50 0.643 strong80 0.602 train likes rows 814831 uids 8144 items 172303 time 80 24786790 organic 0.567 val likes rows 37795 uids 4114 items 22902 time 24788815 25393390 organic 0.666 train dislikes rows 101075 uids 5732 items 50876 time 75 24786580 organic 0.517 val dislikes rows 3563 uids 955 items 3135 time 24789920 25393280 organic 0.515 train unlikes rows 292665 uids 6197 items 113072 time 250 24786570 organic 0.947 val unlikes rows 9346 uids 1313 items 8080 time 24789005 25393385 organic 0.953 train undislikes rows 19960 uids 2769 items 14725 time 15950 24785395 organic 0.889 val undislikes rows 606 uids 205 items 579 time 24810715 25393035 organic 0.87 ## Step 14 · agent · 2026-08-31T18:28:39.760Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:55.270Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/make_popularity_baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create a structurally valid eligibility-filtered popularity ranking.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def make(stage: str) -> Path:\\n+ z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ item_ids = z[\\\"item_ids\\\"].astype(np.int64)\\n+ rows = []\\n+ for r, uid in enumerate(z[\\\"uids\\\"]):\\n+ ok = np.unpackbits(\\n+ z[\\\"packed_eligible\\\"][r], count=len(item_ids), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = item_ids[ok][:100]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.full(100, int(uid), dtype=np.int64),\\n+ \\\"item_id\\\": chosen,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ frame = pd.concat(rows, ignore_index=True)\\n+ name = \\\"validation_popularity.parquet\\\" if stage == \\\"validation\\\" else \\\"submission.parquet\\\"\\n+ path = OUT / name\\n+ frame.to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ print(make(\\\"validation\\\"))\\n+ # Maintain a valid final artifact from the beginning of the attempt.\\n+ print(make(\\\"test\\\"))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:28:55.307Z Script completed Wall time 0.0 seconds Output: {} ## Step 15 · agent · 2026-08-31T18:28:58.362Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:01.527Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/make_popularity_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:29:12.548Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-08-31T18:29:14.504Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:15.303Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:29:31.709Z Script completed Wall time 16.4 seconds Output: ## Step 17 · agent · 2026-08-31T18:29:33.899Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:37.179Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution && /opt/task-venv/bin/python -u /app/solution/make_popularity_baseline.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:29:48.200Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 18 · agent · 2026-08-31T18:29:50.386Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:51.161Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:30:07.360Z Script completed Wall time 16.2 seconds Output: total 4.0K -rw-r--r-- 1 agent agent 1.2K Aug 31 18:28 make_popularity_baseline.py SESSION_ID=73301 ## Step 19 · agent · 2026-08-31T18:30:12.668Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:14.465Z { "code": "const r = await tools.write_stdin({session_id:73301,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:30:25.485Z Script running with cell ID 11 Wall time 11.0 seconds Output: ## Step 20 · agent · 2026-08-31T18:30:28.602Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:30:29.401Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:30:44.488Z Script completed Wall time 15.1 seconds Output: SESSION_ID=73301 ## Step 21 · agent · 2026-08-31T18:30:47.318Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:48.822Z { "code": "const r = await tools.write_stdin({session_id:73301,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:48.904Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "/app/solution/make_popularity_baseline.py", line 39, in print(make("validation")) ^^^^^^^^^^^^^^^^^^ File "/app/solution/make_popularity_baseline.py", line 19, in make z["packed_eligible"][r], count=len(item_ids), bitorder="little" ~^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1 data = self._decompressor.decompress(data, n) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ## Step 22 · agent · 2026-08-31T18:30:50.538Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:01.311Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/make_popularity_baseline.py\\n@@\\n def make(stage: str) -> Path:\\n- z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n- item_ids = z[\\\"item_ids\\\"].astype(np.int64)\\n+ with np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ item_ids = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ eligible = np.unpackbits(\\n+ packed, axis=1, count=len(item_ids), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n rows = []\\n- for r, uid in enumerate(z[\\\"uids\\\"]):\\n- ok = np.unpackbits(\\n- z[\\\"packed_eligible\\\"][r], count=len(item_ids), bitorder=\\\"little\\\"\\n- ).astype(bool)\\n- chosen = item_ids[ok][:100]\\n+ for r, uid in enumerate(uids):\\n+ chosen = item_ids[eligible[r]][:100]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:31:01.382Z Script completed Wall time 0.0 seconds Output: {} ## Step 23 · agent · 2026-08-31T18:31:02.999Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:06.777Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/make_popularity_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:31:14.530Z Script completed Wall time 7.7 seconds Output: /app/solution/validation_popularity.parquet /app/solution/submission.parquet { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } total 1.5M -rw-r--r-- 1 agent agent 1.4K Aug 31 18:31 make_popularity_baseline.py -rw-r--r-- 1 agent agent 719K Aug 31 18:31 submission.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:31 validation_popularity.parquet ## Step 24 · agent · 2026-08-31T18:31:35.073Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T18:31:46.870Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:32:20.332Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nfrom pathlib import Path\\nD=Path('/task/data'); t=time.time()\\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy()\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy()\\nprint('load keys',time.time()-t)\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\nprint('loaded',len(x),round(time.time()-t,1),'memMB',round(x.memory_usage(deep=True).sum()/1e6))\\nx=x[x.uid.isin(targets)&x.item_id.isin(cat)]\\nprint('filtered target+cat',len(x),'users',x.uid.nunique(),'items',x.item_id.nunique(),'mem',round(x.memory_usage(deep=True).sum()/1e6),round(time.time()-t,1))\\npos=x[x.played_ratio_pct>=50]\\nstrongorg=x[(x.played_ratio_pct>=80)&(x.is_organic==1)]\\nprint('positive',len(pos),'strongorg',len(strongorg),'recent pos 7d',len(pos[pos.timestamp>=24786800-7*86400]),'30d',len(pos[pos.timestamp>=24786800-30*86400]),'90d',len(pos[pos.timestamp>=24786800-90*86400]))\\n# validation gold eligible\\nz=np.load(D/'validation_eligible_candidates.npz'); zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy(); z.close()\\nuidrow={int(v):i for i,v in enumerate(zu)}; itemcol={int(v):i for i,v in enumerate(zi)}\\nelig=np.unpackbits(packed,axis=1,count=len(zi),bitorder='little').astype(bool)\\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targets)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\nvk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[(vk.is_organic==1)&vk.uid.isin(targets)&vk.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\ng=pd.concat([vl.assign(grade=1),vk.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\ng=g[[elig[uidrow[int(u)],itemcol[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]]\\nprint('gold',len(g),'users',g.uid.nunique(),'likes',sum(g.grade==3),'strong',sum(g.grade==1))\\nfor name,h in [('any',x),('pos',pos),('strongorg',strongorg)]:\\n pairs=h[['uid','item_id']].drop_duplicates()\\n m=g.merge(pairs,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\\n print(name,'gold pair repeat',m.mean(),'like repeat',m[g.grade.eq(3)].mean(),'users any hit',g.assign(hit=m.values).groupby('uid').hit.any().mean())\\nfor days in [1,3,7,14,30,60,90,180]:\\n h=pos[pos.timestamp>=24786800-days*86400][['uid','item_id']].drop_duplicates()\\n m=g.merge(h,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\\n print('recent',days,'repeat',round(m.mean(),4),'usershit',round(g.assign(hit=m.values).groupby('uid').hit.any().mean(),4))\\nprint('done',round(time.time()-t,1))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:32:29.170Z Script completed Wall time 8.8 seconds Output: load keys 0.05421781539916992 loaded 43484159 0.6 memMB 652 filtered target+cat 31062540 users 8452 items 30000 mem 714 2.2 positive 19959853 strongorg 8661020 recent pos 7d 673983 30d 2787729 90d 7752815 gold 37769 users 4263 likes 4417 strong 33352 Traceback (most recent call last): File "", line 26, in File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py", line 1161, in __getitem__ key = check_bool_indexer(self.index, key) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 2698, in check_bool_indexer raise IndexingError( pandas.errors.IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match). ## Step 26 · agent · 2026-08-31T18:32:31.384Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:03.378Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nfrom pathlib import Path\\nD=Path('/task/data'); t=time.time(); targets=pd.read_parquet(D/'target_users.parquet').uid; cat=pd.read_parquet(D/'candidate_catalog.parquet').item_id\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[x.uid.isin(targets)&x.item_id.isin(cat)]; pos=x[x.played_ratio_pct>=50]; strongorg=x[(x.played_ratio_pct>=80)&(x.is_organic==1)]\\nz=np.load(D/'validation_eligible_candidates.npz'); zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy(); z.close(); uidrow={int(v):i for i,v in enumerate(zu)}; itemcol={int(v):i for i,v in enumerate(zi)}; elig=np.unpackbits(packed,axis=1,count=len(zi),bitorder='little').astype(bool)\\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targets)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); vk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[(vk.is_organic==1)&vk.uid.isin(targets)&vk.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); g=pd.concat([vl.assign(grade=1),vk.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); g=g[[elig[uidrow[int(u)],itemcol[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]].reset_index(drop=True)\\nprint('events targetcat',len(x),'pos',len(pos),'strongorg',len(strongorg),'gold',len(g),'users',g.uid.nunique(),'likes',sum(g.grade==3))\\nfor name,h in [('any',x),('pos',pos),('strongorg',strongorg)]:\\n pairs=h[['uid','item_id']].drop_duplicates(); mg=g.merge(pairs,on=['uid','item_id'],how='left',indicator=True); m=mg._merge.eq('both').to_numpy(); gm=g.grade.to_numpy()\\n print(name,'repeat',round(m.mean(),4),'like',round(m[gm==3].mean(),4),'usershit',round(pd.DataFrame({'uid':g.uid.to_numpy(),'hit':m}).groupby('uid').hit.any().mean(),4))\\nfor days in [1,3,7,14,30,60,90,180]:\\n h=pos[pos.timestamp>=24786800-days*86400][['uid','item_id']].drop_duplicates(); mg=g.merge(h,on=['uid','item_id'],how='left',indicator=True); m=mg._merge.eq('both').to_numpy()\\n print('recent',days,'repeat',round(m.mean(),4),'usershit',round(pd.DataFrame({'uid':g.uid.to_numpy(),'hit':m}).groupby('uid').hit.any().mean(),4))\\n# active preference overlap\\npr=pd.read_parquet(D/'validation_preference_state.parquet');\\nfor st in ['liked','disliked']:\\n h=pr[pr.state.eq(st)][['uid','item_id']]; mg=g.merge(h,on=['uid','item_id'],how='left',indicator=True);m=mg._merge.eq('both').to_numpy();print(st,'gold overlap',round(m.mean(),4),'like gold',round(m[g.grade.to_numpy()==3].mean(),4))\\nprint('sec',round(time.time()-t,1))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:33:14.400Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 27 · agent · 2026-08-31T18:33:16.367Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:33:17.157Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:33:17.909Z Script completed Wall time 0.8 seconds Output: events targetcat 31062540 pos 19959853 strongorg 8661020 gold 37769 users 4263 likes 4417 any repeat 0.0 like 0.0 usershit 0.0 pos repeat 0.0 like 0.0 usershit 0.0 strongorg repeat 0.0 like 0.0 usershit 0.0 recent 1 repeat 0.0 usershit 0.0 recent 3 repeat 0.0 usershit 0.0 recent 7 repeat 0.0 usershit 0.0 recent 14 repeat 0.0 usershit 0.0 recent 30 repeat 0.0 usershit 0.0 recent 60 repeat 0.0 usershit 0.0 recent 90 repeat 0.0 usershit 0.0 recent 180 repeat 0.0 usershit 0.0 liked gold overlap 0.0 like gold 0.0 disliked gold overlap 0.0 like gold 0.0 sec 12.9 ## Step 28 · agent · 2026-08-31T18:33:37.274Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:33:55.938Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:34:14.576Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:34:33.242Z ### Thinking unavailable — no text recorded ## Step 32 · agent · 2026-08-31T18:34:51.868Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-08-31T18:34:53.224Z ### Thinking unavailable — no text recorded The audit found an important boundary behavior: every publicly relevant validation item is unseen in the user’s training interactions and explicit state. So repeat-item ranking cannot score here; this is a true discovery task. I’m shaping training around rolling “predict a newly discovered organic track from the prior positive-history bag” examples, which mirrors the evaluator while preserving the exact user-vector/item-vector decomposition. ### exec · 2026-08-31T18:35:16.681Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data'; END=24786800; WEEK=604800;t=time.time(); users=pd.read_parquet(f'{D}/target_users.parquet').uid; cat=pd.read_parquet(f'{D}/candidate_catalog.parquet').item_id\\nx=pd.read_parquet(f'{D}/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);x=x[x.uid.isin(users)&x.item_id.isin(cat)]\\nfirst=x.groupby(['uid','item_id'],sort=False).timestamp.min().rename('first').reset_index(); s=x[(x.is_organic==1)&(x.played_ratio_pct>=80)][['uid','timestamp','item_id']].merge(first,on=['uid','item_id']);\\nlikes=pd.read_parquet(f'{D}/train_likes.parquet');likes=likes[(likes.uid.isin(users))&(likes.item_id.isin(cat))&(likes.is_organic==1)][['uid','timestamp','item_id']].merge(first,on=['uid','item_id'],how='left');likes['first']=likes['first'].fillna(likes.timestamp)\\nprint('unique pairs',len(first),'strong events',len(s),'organic likes',len(likes),'secs',round(time.time()-t,1))\\nallpairs=[]\\nfor w in range(1,17):\\n c=END-w*WEEK; e=c+WEEK\\n a=s[(s.timestamp>=c)&(s.timestamp=c)][['uid','item_id']].drop_duplicates(); b=likes[(likes.timestamp>=c)&(likes.timestamp=c)][['uid','item_id']].drop_duplicates(); y=pd.concat([a,b]).drop_duplicates()\\n allpairs.append(y.assign(window=w))\\n print(w,'cutoff',c,'pairs',len(y),'users',y.uid.nunique(),'items',y.item_id.nunique(),'likepairs',len(b))\\ny=pd.concat(allpairs);print('total',len(y),'unique useritem',len(y.drop_duplicates(['uid','item_id'])),'items',y.item_id.nunique(),'items>=2',sum(y.groupby('item_id').size()>=2),'users',y.uid.nunique())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:35:26.921Z Script completed Wall time 10.2 seconds Output: unique pairs 7868664 strong events 8661020 organic likes 223046 secs 8.2 1 cutoff 24182000 pairs 41723 users 4295 items 14364 likepairs 5330 2 cutoff 23577200 pairs 40500 users 4254 items 14467 likepairs 4680 3 cutoff 22972400 pairs 40317 users 4109 items 14504 likepairs 5152 4 cutoff 22367600 pairs 38201 users 4050 items 13894 likepairs 5177 5 cutoff 21762800 pairs 38195 users 4008 items 13848 likepairs 4938 6 cutoff 21158000 pairs 34994 users 3856 items 12930 likepairs 4974 7 cutoff 20553200 pairs 36847 users 3824 items 13494 likepairs 7344 8 cutoff 19948400 pairs 33765 users 3750 items 12579 likepairs 4366 9 cutoff 19343600 pairs 32117 users 3608 items 12288 likepairs 5023 10 cutoff 18738800 pairs 32552 users 3446 items 12541 likepairs 4197 11 cutoff 18134000 pairs 31397 users 3392 items 11900 likepairs 4083 12 cutoff 17529200 pairs 30916 users 3350 items 11723 likepairs 3946 13 cutoff 16924400 pairs 30776 users 3284 items 12148 likepairs 4382 14 cutoff 16319600 pairs 29741 users 3216 items 11914 likepairs 3983 15 cutoff 15714800 pairs 28758 users 3159 items 11586 likepairs 3597 16 cutoff 15110000 pairs 27458 users 3164 items 11450 likepairs 3510 total 548257 unique useritem 544901 items 28499 items>=2 26366 users 7879 ## Step 34 · agent · 2026-08-31T18:35:46.586Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-08-31T18:36:05.362Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-08-31T18:36:23.884Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-08-31T18:36:42.525Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-08-31T18:37:01.181Z ### Thinking unavailable — no text recorded ## Step 39 · agent · 2026-08-31T18:37:19.838Z ### Thinking unavailable — no text recorded ## Step 40 · agent · 2026-08-31T18:37:37.280Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:42.713Z { "code": "const p = await tools.update_plan({explanation:\"Audit complete: relevant outcomes are strictly novel per user, so the training design uses rolling temporal discovery examples rather than repeat-item reconstruction.\",plan:[\n {step:\"Audit schemas, sizes, eligibility format, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-based PyTorch two-tower and ranking pipeline\",status:\"in_progress\"},\n {step:\"Train and tune variants on the public validation window\",status:\"pending\"},\n {step:\"Refit selected model on train + validation history\",status:\"pending\"},\n {step:\"Generate and validate final 100-per-user submission and README\",status:\"pending\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T18:37:42.811Z Script completed Wall time 0.0 seconds Output: {} ## Step 41 · agent · 2026-08-31T18:37:45.283Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:35.973Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply an event-stream PyTorch two-tower discovery ranker.\\n+\\n+The program never builds a user-item matrix. It creates rolling temporal\\n+examples directly from event lists: a user tower receives three bounded\\n+history sequences and predicts organic, newly discovered tracks in the next\\n+week. The item tower can therefore be run once for the catalog, while the\\n+user tower is run once per request.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+import time\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+WEEK = 7 * 86_400\\n+GAP = 1_800\\n+K_POS = 96\\n+K_LIKE = 64\\n+K_DISLIKE = 24\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+ torch.set_num_threads(max(1, min(12, torch.get_num_threads())))\\n+\\n+\\n+def map_ids(values: np.ndarray, sorted_ids: np.ndarray, sorted_to_index: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Map known IDs to dense indices without a Python-sized interaction map.\\\"\\\"\\\"\\n+ return sorted_to_index[np.searchsorted(sorted_ids, values)]\\n+\\n+\\n+@dataclass\\n+class EventIndex:\\n+ \\\"\\\"\\\"Per-user timestamp-sorted arrays; this is an event index, not a matrix.\\\"\\\"\\\"\\n+\\n+ timestamps: list[np.ndarray]\\n+ items: list[np.ndarray]\\n+ states: list[np.ndarray] | None = None\\n+\\n+\\n+def make_event_index(\\n+ user_idx: np.ndarray,\\n+ timestamps: np.ndarray,\\n+ item_idx: np.ndarray,\\n+ n_users: int,\\n+ states: np.ndarray | None = None,\\n+) -> EventIndex:\\n+ order = np.lexsort((timestamps, user_idx))\\n+ u = user_idx[order]\\n+ ts = timestamps[order].astype(np.int64, copy=False)\\n+ it = item_idx[order].astype(np.int32, copy=False)\\n+ st = states[order].astype(np.int8, copy=False) if states is not None else None\\n+ bounds = np.searchsorted(u, np.arange(n_users + 1))\\n+ return EventIndex(\\n+ [ts[bounds[i] : bounds[i + 1]] for i in range(n_users)],\\n+ [it[bounds[i] : bounds[i + 1]] for i in range(n_users)],\\n+ [st[bounds[i] : bounds[i + 1]] for i in range(n_users)] if st is not None else None,\\n+ )\\n+\\n+\\n+def recent_unique(index: EventIndex, u: int, cutoff: int, limit: int, pad: int) -> np.ndarray:\\n+ ts, items = index.timestamps[u], index.items[u]\\n+ end = int(np.searchsorted(ts, cutoff, side=\\\"left\\\"))\\n+ found: list[int] = []\\n+ used: set[int] = set()\\n+ for item in items[:end][::-1]:\\n+ value = int(item)\\n+ if value not in used:\\n+ used.add(value)\\n+ found.append(value)\\n+ if len(found) == limit:\\n+ break\\n+ result = np.full(limit, pad, dtype=np.int32)\\n+ result[: len(found)] = found\\n+ return result\\n+\\n+\\n+def recent_active(index: EventIndex, u: int, cutoff: int, limit: int, pad: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Return latest active items from a like/unlike or dislike/undislike stream.\\\"\\\"\\\"\\n+ assert index.states is not None\\n+ ts, items, states = index.timestamps[u], index.items[u], index.states[u]\\n+ end = int(np.searchsorted(ts, cutoff, side=\\\"left\\\"))\\n+ found: list[int] = []\\n+ resolved: set[int] = set()\\n+ for item, state in zip(items[:end][::-1], states[:end][::-1]):\\n+ value = int(item)\\n+ if value in resolved:\\n+ continue\\n+ resolved.add(value)\\n+ if int(state) > 0:\\n+ found.append(value)\\n+ if len(found) == limit:\\n+ break\\n+ result = np.full(limit, pad, dtype=np.int32)\\n+ result[: len(found)] = found\\n+ return result\\n+\\n+\\n+@dataclass\\n+class Prepared:\\n+ uids: np.ndarray\\n+ item_ids: np.ndarray\\n+ popularity: np.ndarray\\n+ pos: np.ndarray\\n+ liked: np.ndarray\\n+ disliked: np.ndarray\\n+ target_offsets: np.ndarray\\n+ target_items: np.ndarray\\n+ target_weights: np.ndarray\\n+ inference_pos: np.ndarray\\n+ inference_liked: np.ndarray\\n+ inference_disliked: np.ndarray\\n+\\n+\\n+def load_family(name: str, include_val: bool, columns: list[str] | None = None) -> pd.DataFrame:\\n+ parts = [pd.read_parquet(DATA / f\\\"train_{name}.parquet\\\", columns=columns)]\\n+ if include_val:\\n+ parts.append(pd.read_parquet(DATA / f\\\"val_{name}.parquet\\\", columns=columns))\\n+ return pd.concat(parts, ignore_index=True)\\n+\\n+\\n+def prepare(stage: str, windows: int) -> Prepared:\\n+ \\\"\\\"\\\"Materialize bounded histories and rolling labels from event tables.\\\"\\\"\\\"\\n+ start = time.time()\\n+ include_val = stage == \\\"final\\\"\\n+ target_frame = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ uids = target_frame.uid.to_numpy(np.int64)\\n+ item_ids = catalog.item_id.to_numpy(np.int64)\\n+ n_users, n_items = len(uids), len(item_ids)\\n+ su_order, si_order = np.argsort(uids), np.argsort(item_ids)\\n+ sorted_uids, sorted_items = uids[su_order], item_ids[si_order]\\n+\\n+ listens = load_family(\\n+ \\\"listens\\\", include_val, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ )\\n+ keep = listens.uid.isin(uids) & listens.item_id.isin(item_ids)\\n+ listens = listens.loc[keep].copy()\\n+ lu = map_ids(listens.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\\n+ li = map_ids(listens.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\\n+ lt = listens.timestamp.to_numpy(np.int64)\\n+\\n+ # Positive histories include conventional >=50% listens. Explicit active\\n+ # channels are supplied separately below.\\n+ positive = listens.played_ratio_pct.to_numpy() >= 50\\n+ pos_index = make_event_index(lu[positive], lt[positive], li[positive], n_users)\\n+\\n+ action_indexes: dict[str, EventIndex] = {}\\n+ action_frames: dict[str, pd.DataFrame] = {}\\n+ for channel, on_name, off_name in (\\n+ (\\\"liked\\\", \\\"likes\\\", \\\"unlikes\\\"),\\n+ (\\\"disliked\\\", \\\"dislikes\\\", \\\"undislikes\\\"),\\n+ ):\\n+ on = load_family(on_name, include_val, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ off = load_family(off_name, include_val, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ action_frames[on_name] = on\\n+ both = pd.concat([on.assign(_state=1), off.assign(_state=-1)], ignore_index=True)\\n+ both = both[both.uid.isin(uids) & both.item_id.isin(item_ids)]\\n+ au = map_ids(both.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\\n+ ai = map_ids(both.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\\n+ action_indexes[channel] = make_event_index(\\n+ au, both.timestamp.to_numpy(np.int64), ai, n_users, both._state.to_numpy(np.int8)\\n+ )\\n+\\n+ # First listen time is a conservative, scalable proxy for eligibility at\\n+ # each historical cutoff. Exact supplied eligibility is used at ranking.\\n+ first = (\\n+ pd.DataFrame({\\\"u\\\": lu, \\\"i\\\": li, \\\"timestamp\\\": lt})\\n+ .groupby([\\\"u\\\", \\\"i\\\"], sort=False, observed=True)\\n+ .timestamp.min()\\n+ .rename(\\\"first\\\")\\n+ .reset_index()\\n+ )\\n+ strong_mask = (listens.is_organic.to_numpy() == 1) & (\\n+ listens.played_ratio_pct.to_numpy() >= 80\\n+ )\\n+ strong = pd.DataFrame(\\n+ {\\\"u\\\": lu[strong_mask], \\\"i\\\": li[strong_mask], \\\"timestamp\\\": lt[strong_mask], \\\"grade\\\": 1.0}\\n+ ).merge(first, on=[\\\"u\\\", \\\"i\\\"], how=\\\"left\\\")\\n+\\n+ like = action_frames[\\\"likes\\\"]\\n+ like = like[\\n+ like.uid.isin(uids) & like.item_id.isin(item_ids) & (like.is_organic == 1)\\n+ ].copy()\\n+ kui = map_ids(like.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\\n+ kii = map_ids(like.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\\n+ like_targets = pd.DataFrame(\\n+ {\\\"u\\\": kui, \\\"i\\\": kii, \\\"timestamp\\\": like.timestamp.to_numpy(np.int64), \\\"grade\\\": 3.0}\\n+ ).merge(first, on=[\\\"u\\\", \\\"i\\\"], how=\\\"left\\\")\\n+ like_targets[\\\"first\\\"] = like_targets[\\\"first\\\"].fillna(like_targets.timestamp)\\n+\\n+ specifications = [(TRAIN_END - w * WEEK, TRAIN_END - (w - 1) * WEEK) for w in range(1, windows + 1)]\\n+ if stage == \\\"final\\\":\\n+ specifications.insert(0, (TRAIN_END, TRAIN_END + GAP + WEEK))\\n+\\n+ contexts_pos: list[np.ndarray] = []\\n+ contexts_like: list[np.ndarray] = []\\n+ contexts_dislike: list[np.ndarray] = []\\n+ target_chunks: list[np.ndarray] = []\\n+ weight_chunks: list[np.ndarray] = []\\n+ offsets = [0]\\n+ pad = n_items\\n+\\n+ for cutoff, outcome_end in specifications:\\n+ ys = strong[\\n+ (strong.timestamp >= cutoff)\\n+ & (strong.timestamp < outcome_end)\\n+ & (strong[\\\"first\\\"] >= cutoff)\\n+ ][[\\\"u\\\", \\\"i\\\", \\\"grade\\\"]]\\n+ yl = like_targets[\\n+ (like_targets.timestamp >= cutoff)\\n+ & (like_targets.timestamp < outcome_end)\\n+ & (like_targets[\\\"first\\\"] >= cutoff)\\n+ ][[\\\"u\\\", \\\"i\\\", \\\"grade\\\"]]\\n+ labels = (\\n+ pd.concat([ys, yl], ignore_index=True)\\n+ .groupby([\\\"u\\\", \\\"i\\\"], sort=False, observed=True)\\n+ .grade.max().reset_index()\\n+ )\\n+ for u, group in labels.groupby(\\\"u\\\", sort=False):\\n+ user = int(u)\\n+ contexts_pos.append(recent_unique(pos_index, user, cutoff, K_POS, pad))\\n+ contexts_like.append(recent_active(action_indexes[\\\"liked\\\"], user, cutoff, K_LIKE, pad))\\n+ contexts_dislike.append(\\n+ recent_active(action_indexes[\\\"disliked\\\"], user, cutoff, K_DISLIKE, pad)\\n+ )\\n+ # Gains, mildly compressed, determine sampling frequency.\\n+ target_chunks.append(group.i.to_numpy(np.int32))\\n+ weight_chunks.append(np.where(group.grade.to_numpy() >= 3, 3.0, 1.0).astype(np.float32))\\n+ offsets.append(offsets[-1] + len(group))\\n+ print(\\n+ f\\\"cutoff={cutoff} contexts={labels.u.nunique()} labels={len(labels)}\\\",\\n+ flush=True,\\n+ )\\n+\\n+ inference_cutoff = int(listens.timestamp.max()) + 1 if include_val else TRAIN_END\\n+ inference_pos = np.stack(\\n+ [recent_unique(pos_index, u, inference_cutoff, K_POS, pad) for u in range(n_users)]\\n+ )\\n+\\n+ # Cutoff-specific supplied state is authoritative at inference. Sort active\\n+ # items by their latest corresponding positive/negative action where known.\\n+ preference_name = \\\"test_preference_state.parquet\\\" if stage == \\\"final\\\" else \\\"validation_preference_state.parquet\\\"\\n+ preference = pd.read_parquet(DATA / preference_name)\\n+ inference_channels = []\\n+ for state, limit, action_index in (\\n+ (\\\"liked\\\", K_LIKE, action_indexes[\\\"liked\\\"]),\\n+ (\\\"disliked\\\", K_DISLIKE, action_indexes[\\\"disliked\\\"]),\\n+ ):\\n+ by_user: dict[int, set[int]] = {}\\n+ active = preference[\\n+ preference.state.eq(state) & preference.uid.isin(uids) & preference.item_id.isin(item_ids)\\n+ ]\\n+ if len(active):\\n+ au = map_ids(active.uid.to_numpy(np.int64), sorted_uids, su_order)\\n+ ai = map_ids(active.item_id.to_numpy(np.int64), sorted_items, si_order)\\n+ for u, i in zip(au, ai):\\n+ by_user.setdefault(int(u), set()).add(int(i))\\n+ array = np.full((n_users, limit), pad, dtype=np.int32)\\n+ for u in range(n_users):\\n+ allowed = by_user.get(u, set())\\n+ if not allowed:\\n+ continue\\n+ # Reverse action order supplies useful recency while membership\\n+ # comes solely from the evaluator-provided state.\\n+ ordered: list[int] = []\\n+ seen: set[int] = set()\\n+ for item in action_index.items[u][::-1]:\\n+ value = int(item)\\n+ if value in allowed and value not in seen:\\n+ ordered.append(value)\\n+ seen.add(value)\\n+ if len(ordered) == limit:\\n+ break\\n+ if len(ordered) < limit:\\n+ ordered.extend(sorted(allowed - seen)[: limit - len(ordered)])\\n+ array[u, : len(ordered)] = ordered\\n+ inference_channels.append(array)\\n+\\n+ pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ pop = ((pop - pop.mean()) / (pop.std() + 1e-6)).astype(np.float32)\\n+ result = Prepared(\\n+ uids=uids,\\n+ item_ids=item_ids,\\n+ popularity=pop,\\n+ pos=np.stack(contexts_pos),\\n+ liked=np.stack(contexts_like),\\n+ disliked=np.stack(contexts_dislike),\\n+ target_offsets=np.asarray(offsets, dtype=np.int64),\\n+ target_items=np.concatenate(target_chunks).astype(np.int32),\\n+ target_weights=np.concatenate(weight_chunks).astype(np.float32),\\n+ inference_pos=inference_pos,\\n+ inference_liked=inference_channels[0],\\n+ inference_disliked=inference_channels[1],\\n+ )\\n+ print(\\n+ f\\\"prepared {len(result.pos)} contexts, {len(result.target_items)} labels in {time.time()-start:.1f}s\\\",\\n+ flush=True,\\n+ )\\n+ return result\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only tower: bounded positive/like/dislike bags -> request vector.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.pad = n_items\\n+ self.embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.pos_logits = nn.Parameter(torch.linspace(1.0, -1.0, K_POS))\\n+ self.like_logits = nn.Parameter(torch.zeros(K_LIKE))\\n+ self.dislike_logits = nn.Parameter(torch.zeros(K_DISLIKE))\\n+ self.network = nn.Sequential(\\n+ nn.LayerNorm(3 * dim + 3),\\n+ nn.Linear(3 * dim + 3, 2 * dim),\\n+ nn.GELU(),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+\\n+ def pool(self, ids: torch.Tensor, logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\\n+ valid = ids.ne(self.pad)\\n+ attention = logits.unsqueeze(0).expand_as(ids).masked_fill(~valid, -1e4)\\n+ attention = torch.softmax(attention, dim=1) * valid\\n+ attention = attention / attention.sum(1, keepdim=True).clamp_min(1e-6)\\n+ pooled = (self.embedding(ids) * attention.unsqueeze(-1)).sum(1)\\n+ amount = torch.log1p(valid.sum(1).float()).unsqueeze(1) / 5.0\\n+ return pooled, amount\\n+\\n+ def forward(self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor) -> torch.Tensor:\\n+ p, pn = self.pool(pos, self.pos_logits)\\n+ l, ln = self.pool(liked, self.like_logits)\\n+ d, dn = self.pool(disliked, self.dislike_logits)\\n+ return self.network(torch.cat([p, l, d, pn, ln, dn], dim=1))\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently executable catalog tower.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int, popularity: np.ndarray):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(n_items, dim)\\n+ self.register_buffer(\\\"popularity\\\", torch.from_numpy(popularity).unsqueeze(1))\\n+ self.network = nn.Sequential(\\n+ nn.LayerNorm(dim + 1),\\n+ nn.Linear(dim + 1, 2 * dim),\\n+ nn.GELU(),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+\\n+ def forward(self, ids: torch.Tensor) -> torch.Tensor:\\n+ return self.network(torch.cat([self.embedding(ids), self.popularity[ids]], dim=1))\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, popularity: np.ndarray):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, dim, popularity)\\n+ self.logit_scale = nn.Parameter(torch.tensor(0.0))\\n+\\n+ def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\\n+ return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\\n+\\n+\\n+def train_model(data: Prepared, args: argparse.Namespace) -> TwoTower:\\n+ n_contexts, n_items = len(data.pos), len(data.item_ids)\\n+ model = TwoTower(n_items, args.dim, data.popularity)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=2e-5)\\n+ rng = np.random.default_rng(args.seed)\\n+ batch_size = args.batch_size\\n+ pos_all = torch.from_numpy(data.pos).long()\\n+ like_all = torch.from_numpy(data.liked).long()\\n+ dislike_all = torch.from_numpy(data.disliked).long()\\n+ for epoch in range(args.epochs):\\n+ model.train()\\n+ chosen = np.empty(n_contexts, dtype=np.int64)\\n+ sample_weight = np.empty(n_contexts, dtype=np.float32)\\n+ for c in range(n_contexts):\\n+ lo, hi = data.target_offsets[c : c + 2]\\n+ weights = data.target_weights[lo:hi]\\n+ j = int(rng.choice(hi - lo, p=weights / weights.sum()))\\n+ chosen[c] = data.target_items[lo + j]\\n+ sample_weight[c] = weights[j]\\n+ order = rng.permutation(n_contexts)\\n+ total_loss = 0.0\\n+ seen = 0\\n+ for start in range(0, n_contexts, batch_size):\\n+ c = order[start : start + batch_size]\\n+ targets = chosen[c]\\n+ # Unique batch positives plus uniform catalog negatives preserve a\\n+ # learnable popularity prior while keeping complexity sampled.\\n+ candidates = np.unique(\\n+ np.concatenate([targets, rng.integers(0, n_items, args.negatives, dtype=np.int64)])\\n+ )\\n+ lookup = {int(v): j for j, v in enumerate(candidates)}\\n+ labels = torch.tensor([lookup[int(v)] for v in targets], dtype=torch.long)\\n+ ct = torch.from_numpy(candidates).long()\\n+ users = model.user_tower(pos_all[c], like_all[c], dislike_all[c])\\n+ items = model.item_tower(ct)\\n+ logits = model.score(users, items)\\n+ losses = nn.functional.cross_entropy(logits, labels, reduction=\\\"none\\\")\\n+ weights = torch.from_numpy(np.sqrt(sample_weight[c]))\\n+ loss = (losses * weights).sum() / weights.sum()\\n+ optimizer.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ total_loss += float(loss) * len(c)\\n+ seen += len(c)\\n+ print(\\n+ f\\\"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}\\\",\\n+ flush=True,\\n+ )\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float) -> Path:\\n+ model.eval()\\n+ n_users, n_items = len(data.uids), len(data.item_ids)\\n+ all_items = model.item_tower(torch.arange(n_items)).float()\\n+ # A dot-product-compatible popularity coordinate, selected on public\\n+ # validation, is appended to both independently computed vectors.\\n+ pop_coordinate = torch.from_numpy(data.popularity).float()\\n+ eligibility_name = \\\"test_eligible_candidates.npz\\\" if stage == \\\"final\\\" else \\\"validation_eligible_candidates.npz\\\"\\n+ with np.load(DATA / eligibility_name) as z:\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ rows: list[pd.DataFrame] = []\\n+ pos = torch.from_numpy(data.inference_pos).long()\\n+ liked = torch.from_numpy(data.inference_liked).long()\\n+ disliked = torch.from_numpy(data.inference_disliked).long()\\n+ for start in range(0, n_users, 64):\\n+ end = min(start + 64, n_users)\\n+ users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\\n+ scores = users @ all_items.T\\n+ scores += popularity_mix * pop_coordinate.unsqueeze(0)\\n+ eligible = np.unpackbits(\\n+ packed[start:end], axis=1, count=n_items, bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\\n+ top = torch.topk(scores, k=100, dim=1, largest=True, sorted=True).indices.numpy()\\n+ for local, chosen in enumerate(top):\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.full(100, data.uids[start + local], dtype=np.int64),\\n+ \\\"item_id\\\": data.item_ids[chosen].astype(np.int64),\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ frame = pd.concat(rows, ignore_index=True)\\n+ path = OUT / (\\\"submission.parquet\\\" if stage == \\\"final\\\" else \\\"validation.parquet\\\")\\n+ frame.to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ parser.add_argument(\\\"--windows\\\", type=int, default=16)\\n+ parser.add_argument(\\\"--dim\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=10)\\n+ parser.add_argument(\\\"--batch-size\\\", type=int, default=384)\\n+ parser.add_argument(\\\"--negatives\\\", type=int, default=256)\\n+ parser.add_argument(\\\"--lr\\\", type=float, default=0.002)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ parser.add_argument(\\\"--popularity-mix\\\", type=float, default=0.0)\\n+ args = parser.parse_args()\\n+ seed_everything(args.seed)\\n+ prepared = prepare(args.stage, args.windows)\\n+ model = train_model(prepared, args)\\n+ checkpoint = OUT / f\\\"two_tower_{args.stage}.pt\\\"\\n+ torch.save({\\\"model\\\": model.state_dict(), \\\"args\\\": vars(args)}, checkpoint)\\n+ path = rank(model, prepared, args.stage, args.popularity_mix)\\n+ metadata = {\\n+ \\\"stage\\\": args.stage,\\n+ \\\"architecture\\\": \\\"history pooling user tower + independent item MLP tower\\\",\\n+ \\\"args\\\": vars(args),\\n+ \\\"contexts\\\": len(prepared.pos),\\n+ \\\"labels\\\": len(prepared.target_items),\\n+ \\\"ranking\\\": str(path),\\n+ }\\n+ (OUT / f\\\"run_{args.stage}.json\\\").write_text(json.dumps(metadata, indent=2) + \\\"\\\\n\\\")\\n+ print(path)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:41:36.006Z Script completed Wall time 0.0 seconds Output: {} ## Step 42 · agent · 2026-08-31T18:41:46.772Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:51.444Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 8 --dim 48 --batch-size 384 --negatives 256 --lr 0.002 --popularity-mix 0.0\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:42:02.463Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 43 · agent · 2026-08-31T18:42:05.689Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:06.407Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T18:42:21.625Z Script completed Wall time 15.2 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 25.4s SESSION_ID=77337 ## Step 44 · agent · 2026-08-31T18:42:22.794Z ### Thinking unavailable — no text recorded The rolling dataset is built: 58,765 cutoff-specific history contexts and 548,257 novel organic labels across 16 weekly backtests. Training is now running on sampled candidates; once it finishes I’ll score several dot-product-compatible popularity calibrations against the public window rather than committing to the first neural output. ### exec · 2026-08-31T18:42:27.345Z { "code": "const r = await tools.write_stdin({session_id:77337,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:42:38.380Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 45 · agent · 2026-08-31T18:42:39.958Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:40.791Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T18:42:57.384Z Script completed Wall time 16.6 seconds Output: /app/solution/train_ranker.py:424: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) total_loss += float(loss) * len(c) epoch 01/8 loss=6.3829 scale=0.853 epoch 02/8 loss=6.3462 scale=0.822 epoch 03/8 loss=6.3164 scale=0.806 epoch 04/8 loss=6.2814 scale=0.773 epoch 05/8 loss=6.2337 scale=0.752 epoch 06/8 loss=6.1732 scale=0.728 epoch 07/8 loss=6.0928 scale=0.704 SESSION_ID=77337 ## Step 46 · agent · 2026-08-31T18:42:59.246Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:01.096Z { "code": "const r = await tools.write_stdin({session_id:77337,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:43:02.469Z Script completed Wall time 1.4 seconds Output: epoch 08/8 loss=6.0150 scale=0.686 /app/solution/validation.parquet ## Step 47 · agent · 2026-08-31T18:43:03.807Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:06.723Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution/two_tower_validation.pt /app/solution/validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:43:10.214Z Script completed Wall time 3.5 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.003206, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 12M Aug 31 18:42 /app/solution/two_tower_validation.pt -rw-r--r-- 1 agent agent 1.8M Aug 31 18:43 /app/solution/validation.parquet ## Step 48 · agent · 2026-08-31T18:43:25.584Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:47.386Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float) -> Path:\\n+def rank(\\n+ model: TwoTower,\\n+ data: Prepared,\\n+ stage: str,\\n+ popularity_mix: float,\\n+ output_name: str | None = None,\\n+) -> Path:\\n@@\\n- path = OUT / (\\\"submission.parquet\\\" if stage == \\\"final\\\" else \\\"validation.parquet\\\")\\n+ path = OUT / (\\n+ output_name\\n+ or (\\\"submission.parquet\\\" if stage == \\\"final\\\" else \\\"validation.parquet\\\")\\n+ )\\n@@\\n parser.add_argument(\\\"--popularity-mix\\\", type=float, default=0.0)\\n+ parser.add_argument(\\\"--rank-only\\\", action=\\\"store_true\\\")\\n+ parser.add_argument(\\\"--popularity-grid\\\", default=\\\"\\\")\\n@@\\n- model = train_model(prepared, args)\\n checkpoint = OUT / f\\\"two_tower_{args.stage}.pt\\\"\\n- torch.save({\\\"model\\\": model.state_dict(), \\\"args\\\": vars(args)}, checkpoint)\\n- path = rank(model, prepared, args.stage, args.popularity_mix)\\n+ if args.rank_only:\\n+ saved = torch.load(checkpoint, map_location=\\\"cpu\\\", weights_only=True)\\n+ saved_args = saved[\\\"args\\\"]\\n+ model = TwoTower(len(prepared.item_ids), int(saved_args[\\\"dim\\\"]), prepared.popularity)\\n+ model.load_state_dict(saved[\\\"model\\\"])\\n+ else:\\n+ model = train_model(prepared, args)\\n+ torch.save({\\\"model\\\": model.state_dict(), \\\"args\\\": vars(args)}, checkpoint)\\n+ mixes = (\\n+ [float(value) for value in args.popularity_grid.split(\\\",\\\")]\\n+ if args.popularity_grid\\n+ else [args.popularity_mix]\\n+ )\\n+ path = None\\n+ for mix in mixes:\\n+ token = str(mix).replace(\\\"-\\\", \\\"m\\\").replace(\\\".\\\", \\\"p\\\")\\n+ output_name = None if len(mixes) == 1 else f\\\"validation_mix_{token}.parquet\\\"\\n+ if stage := args.stage == \\\"final\\\":\\n+ output_name = None\\n+ path = rank(model, prepared, args.stage, mix, output_name)\\n+ print(path)\\n@@\\n- \\\"ranking\\\": str(path),\\n+ \\\"ranking\\\": str(path),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:43:47.417Z Script completed Wall time 0.0 seconds Output: {} ## Step 49 · agent · 2026-08-31T18:43:53.581Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:00.166Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if stage := args.stage == \\\"final\\\":\\n+ if args.stage == \\\"final\\\":\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 48 --rank-only --popularity-grid 0.1,0.3,1,3,10,30\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:44:11.188Z Script running with cell ID 25 Wall time 11.0 seconds Output: {} ## Step 50 · agent · 2026-08-31T18:44:13.059Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:13.895Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T18:44:30.354Z Script completed Wall time 16.5 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 25.2s SESSION_ID=25752 ## Step 51 · agent · 2026-08-31T18:44:32.413Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:34.246Z { "code": "const r = await tools.write_stdin({session_id:25752,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:44:41.872Z Script completed Wall time 7.6 seconds Output: /app/solution/validation_mix_0p1.parquet /app/solution/validation_mix_0p3.parquet /app/solution/validation_mix_1p0.parquet /app/solution/validation_mix_3p0.parquet /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_30p0.parquet /app/solution/validation_mix_30p0.parquet ## Step 52 · agent · 2026-08-31T18:44:44.007Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:47.562Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_mix_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:44:58.582Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 53 · agent · 2026-08-31T18:45:00.245Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:00.984Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T18:45:07.955Z Script completed Wall time 7.0 seconds Output: { "model": "/app/solution/validation_mix_0p1.parquet", "scores": { "quality_ndcg@10": 0.003731, "quality_users": 4263 } } { "model": "/app/solution/validation_mix_0p3.parquet", "scores": { "quality_ndcg@10": 0.005177, "quality_users": 4263 } } { "model": "/app/solution/validation_mix_10p0.parquet", "scores": { "quality_ndcg@10": 0.01069, "quality_users": 4263 } } { "model": "/app/solution/validation_mix_1p0.parquet", "scores": { "quality_ndcg@10": 0.008717, "quality_users": 4263 } } { "model": "/app/solution/validation_mix_30p0.parquet", "scores": { "quality_ndcg@10": 0.010811, "quality_users": 4263 } } { "model": "/app/solution/validation_mix_3p0.parquet", "scores": { "quality_ndcg@10": 0.010472, "quality_users": 4263 } } ## Step 54 · agent · 2026-08-31T18:45:27.270Z ### Thinking unavailable — no text recorded The first neural model by itself is too weak, but its best calibrated blend slightly improves the catalog-popularity baseline (0.010811 vs. 0.010710). I’m not stopping there: I’m testing objective-aligned, time-decayed organic popularity as an additional item-tower coordinate, then I’ll retrain the personalized component longer only if it adds lift on top of the best global coordinate. ### exec · 2026-08-31T18:46:11.480Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_item_prior.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune global item features; each is a valid constant user-tower coordinate.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+END = 24_786_800\\n+DAY = 86_400\\n+\\n+\\n+def ranking(order: np.ndarray, name: str) -> Path:\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ eligible = np.unpackbits(\\n+ z[\\\"packed_eligible\\\"], axis=1, count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chunks = []\\n+ for r, uid in enumerate(uids):\\n+ chosen = order[eligible[r, order]][:100]\\n+ chunks.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.full(100, uid, dtype=np.int64),\\n+ \\\"item_id\\\": items[chosen],\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ path = OUT / f\\\"validation_prior_{name}.parquet\\\"\\n+ pd.concat(chunks, ignore_index=True).to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+def main() -> None:\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ ids = catalog.item_id.to_numpy(np.int64)\\n+ mapper = pd.Series(np.arange(len(ids), dtype=np.int32), index=ids)\\n+ listens = pd.read_parquet(\\n+ DATA / \\\"train_listens.parquet\\\",\\n+ columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ listens = listens[\\n+ listens.item_id.isin(ids)\\n+ & (listens.is_organic == 1)\\n+ & (listens.played_ratio_pct >= 80)\\n+ ].copy()\\n+ listens[\\\"i\\\"] = listens.item_id.map(mapper).astype(np.int32)\\n+ likes = pd.read_parquet(DATA / \\\"train_likes.parquet\\\")\\n+ likes = likes[likes.item_id.isin(ids) & (likes.is_organic == 1)].copy()\\n+ likes[\\\"i\\\"] = likes.item_id.map(mapper).astype(np.int32)\\n+ features: dict[str, np.ndarray] = {\\n+ \\\"catalog_all\\\": np.log1p(catalog.positive_listens.to_numpy(float)),\\n+ \\\"catalog_org\\\": np.log1p(catalog.organic_positive_listens.to_numpy(float)),\\n+ }\\n+ for days in (7, 14, 30, 60, 90, 180):\\n+ lc = np.bincount(\\n+ listens.loc[listens.timestamp >= END - days * DAY, \\\"i\\\"], minlength=len(ids)\\n+ ).astype(float)\\n+ kc = np.bincount(\\n+ likes.loc[likes.timestamp >= END - days * DAY, \\\"i\\\"], minlength=len(ids)\\n+ ).astype(float)\\n+ features[f\\\"listen_{days}d\\\"] = np.log1p(lc)\\n+ features[f\\\"objective_{days}d\\\"] = np.log1p(lc + 7 * kc)\\n+ # Normalize before blended priors.\\n+ z = {k: (v - v.mean()) / (v.std() + 1e-9) for k, v in features.items()}\\n+ features[\\\"blend_30\\\"] = z[\\\"catalog_org\\\"] + z[\\\"objective_30d\\\"]\\n+ features[\\\"blend_60\\\"] = z[\\\"catalog_org\\\"] + z[\\\"objective_60d\\\"]\\n+ features[\\\"blend_recent\\\"] = z[\\\"objective_14d\\\"] + z[\\\"objective_60d\\\"]\\n+ for name, values in features.items():\\n+ order = np.argsort(-values, kind=\\\"stable\\\")\\n+ print(ranking(order, name), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/tune_item_prior.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:46:22.501Z Script running with cell ID 28 Wall time 11.0 seconds Output: {} ## Step 55 · agent · 2026-08-31T18:46:24.368Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:25.157Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T18:46:41.666Z Script completed Wall time 16.5 seconds Output: /app/solution/validation_prior_catalog_all.parquet /app/solution/validation_prior_catalog_org.parquet /app/solution/validation_prior_listen_7d.parquet /app/solution/validation_prior_objective_7d.parquet /app/solution/validation_prior_listen_14d.parquet /app/solution/validation_prior_objective_14d.parquet /app/solution/validation_prior_listen_30d.parquet /app/solution/validation_prior_objective_30d.parquet /app/solution/validation_prior_listen_60d.parquet /app/solution/validation_prior_objective_60d.parquet /app/solution/validation_prior_listen_90d.parquet /app/solution/validation_prior_objective_90d.parquet /app/solution/validation_prior_listen_180d.parquet SESSION_ID=86100 ## Step 56 · agent · 2026-08-31T18:46:43.215Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:46.422Z { "code": "const r = await tools.write_stdin({session_id:86100,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:46:47.943Z Script completed Wall time 1.5 seconds Output: /app/solution/validation_prior_objective_180d.parquet /app/solution/validation_prior_blend_30.parquet /app/solution/validation_prior_blend_60.parquet /app/solution/validation_prior_blend_recent.parquet ## Step 57 · agent · 2026-08-31T18:46:50.055Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:54.391Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_prior_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | awk -v f=\\\"$(basename \\\"$f\\\")\\\" '/quality_ndcg/{gsub(\\\",\\\",\\\"\\\"); print f,$2}'; done | sort -k2,2nr\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:47:05.411Z Script running with cell ID 30 Wall time 11.0 seconds Output: ## Step 58 · agent · 2026-08-31T18:47:06.980Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:07.712Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T18:47:24.571Z Script completed Wall time 16.9 seconds Output: SESSION_ID=84586 ## Step 59 · agent · 2026-08-31T18:47:26.032Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:27.902Z { "code": "const r = await tools.write_stdin({session_id:84586,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:47:38.920Z Script running with cell ID 31 Wall time 11.0 seconds Output: ## Step 60 · agent · 2026-08-31T18:47:40.571Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:41.375Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T18:47:51.461Z Script completed Wall time 10.1 seconds Output: validation_prior_objective_7d.parquet 0.028941 validation_prior_objective_14d.parquet 0.02845 validation_prior_listen_7d.parquet 0.028131 validation_prior_listen_14d.parquet 0.022878 validation_prior_blend_recent.parquet 0.02002 validation_prior_objective_60d.parquet 0.016611 validation_prior_objective_90d.parquet 0.01607 validation_prior_listen_90d.parquet 0.015822 validation_prior_listen_60d.parquet 0.015802 validation_prior_objective_30d.parquet 0.015531 validation_prior_blend_60.parquet 0.014933 validation_prior_blend_30.parquet 0.01362 validation_prior_listen_30d.parquet 0.013314 validation_prior_objective_180d.parquet 0.012563 validation_prior_listen_180d.parquet 0.011908 validation_prior_catalog_all.parquet 0.01071 validation_prior_catalog_org.parquet 0.010577 ## Step 61 · agent · 2026-08-31T18:48:11.575Z ### Thinking unavailable — no text recorded ## Step 62 · agent · 2026-08-31T18:48:13.745Z ### Thinking unavailable — no text recorded A much stronger signal emerged: seven-day organic completion plus organic-like velocity reaches 0.028941 NDCG@10—nearly 3× the original baseline. This is fully compatible with the architecture as an offline item-tower feature paired with a constant user-tower coordinate. I’m now retraining the neural residual around that feature so personalization can add to, rather than displace, the strong time-local prior. ### exec · 2026-08-31T18:48:31.295Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ # Objective-aligned velocity is substantially more predictive than the\\n+ # all-time catalog order and is independently precomputable offline.\\n+ feature_cutoff = int(listens.timestamp.max()) + 1 if include_val else TRAIN_END\\n+ recent_strong = strong[\\n+ (strong.timestamp >= feature_cutoff - WEEK) & (strong.timestamp < feature_cutoff)\\n+ ]\\n+ recent_like = like_targets[\\n+ (like_targets.timestamp >= feature_cutoff - WEEK)\\n+ & (like_targets.timestamp < feature_cutoff)\\n+ ]\\n+ listen_counts = np.bincount(recent_strong.i.to_numpy(np.int32), minlength=n_items)\\n+ like_counts = np.bincount(recent_like.i.to_numpy(np.int32), minlength=n_items)\\n+ pop = np.log1p(listen_counts + 7.0 * like_counts).astype(np.float32)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 18 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 3\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:48:42.315Z Script running with cell ID 32 Wall time 11.0 seconds Output: {} ## Step 63 · agent · 2026-08-31T18:48:43.828Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:44.633Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:49:01.480Z Script completed Wall time 16.8 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 24.6s SESSION_ID=48518 ## Step 64 · agent · 2026-08-31T18:49:02.839Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:05.227Z { "code": "const r = await tools.write_stdin({session_id:48518,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:49:16.259Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:49:17.854Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:18.571Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:49:35.263Z Script completed Wall time 16.7 seconds Output: /app/solution/train_ranker.py:436: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) total_loss += float(loss) * len(c) epoch 01/18 loss=6.5128 scale=0.878 epoch 02/18 loss=6.4635 scale=0.861 epoch 03/18 loss=6.4310 scale=0.852 epoch 04/18 loss=6.3922 scale=0.840 epoch 05/18 loss=6.3451 scale=0.829 epoch 06/18 loss=6.2779 scale=0.819 SESSION_ID=48518 ## Step 66 · agent · 2026-08-31T18:49:36.890Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:38.726Z { "code": "const r = await tools.write_stdin({session_id:48518,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:49:49.757Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:49:51.388Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:52.162Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:50:08.768Z Script completed Wall time 16.6 seconds Output: epoch 07/18 loss=6.2044 scale=0.815 epoch 08/18 loss=6.1102 scale=0.804 epoch 09/18 loss=6.0217 scale=0.794 epoch 10/18 loss=5.9189 scale=0.781 epoch 11/18 loss=5.8207 scale=0.771 epoch 12/18 loss=5.7099 scale=0.757 SESSION_ID=48518 ## Step 68 · agent · 2026-08-31T18:50:10.557Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:12.412Z { "code": "const r = await tools.write_stdin({session_id:48518,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:50:23.431Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 69 · agent · 2026-08-31T18:50:25.244Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:26.071Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:50:41.215Z Script completed Wall time 15.1 seconds Output: epoch 13/18 loss=5.6203 scale=0.747 epoch 14/18 loss=5.5238 scale=0.731 epoch 15/18 loss=5.4216 scale=0.721 epoch 16/18 loss=5.3368 scale=0.708 epoch 17/18 loss=5.2565 scale=0.692 epoch 18/18 loss=5.1812 scale=0.678 /app/solution/validation.parquet /app/solution/validation.parquet ## Step 70 · agent · 2026-08-31T18:50:42.841Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:44.939Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:50:48.368Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.024339, "quality_users": 4263 } } ## Step 71 · agent · 2026-08-31T18:50:52.810Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:56.355Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 3,5,10,20,40,80\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:51:07.377Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 72 · agent · 2026-08-31T18:51:09.131Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:09.889Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:51:26.539Z Script completed Wall time 16.6 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 25.1s SESSION_ID=65478 ## Step 73 · agent · 2026-08-31T18:51:27.999Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:29.890Z { "code": "const r = await tools.write_stdin({session_id:65478,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:51:39.333Z Script completed Wall time 9.4 seconds Output: /app/solution/validation_mix_3p0.parquet /app/solution/validation_mix_5p0.parquet /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet /app/solution/validation_mix_80p0.parquet ## Step 74 · agent · 2026-08-31T18:51:41.160Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:48.548Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_mix_3p0.parquet /app/solution/validation_mix_5p0.parquet /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | awk -v f=\\\"$(basename \\\"$f\\\")\\\" '/quality_ndcg/{gsub(\\\",\\\",\\\"\\\"); print f,$2}'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:51:59.570Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 75 · agent · 2026-08-31T18:52:01.464Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:02.250Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:52:09.045Z Script completed Wall time 6.8 seconds Output: validation_mix_3p0.parquet 0.024339 validation_mix_5p0.parquet 0.028582 validation_mix_10p0.parquet 0.029405 validation_mix_20p0.parquet 0.0293 validation_mix_40p0.parquet 0.029379 validation_mix_80p0.parquet 0.029025 ## Step 76 · agent · 2026-08-31T18:52:29.549Z ### Thinking unavailable — no text recorded The personalized residual now helps: the best exact-dot-product blend scores 0.029405, above the 0.028941 item prior alone. I’m doing one final compact search over prior half-life and organic-like weight; this uses validation only for model selection, never as validation input features. Then I’ll freeze those settings and refit on train plus validation history for the hidden window. ### exec · 2026-08-31T18:53:26.888Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/search_prior.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fast public-only selection of objective-aligned item-prior settings.\\\"\\\"\\\"\\n+import sys\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+sys.path.insert(0, \\\"/task\\\")\\n+from ranking_evaluation import ndcg_at_k # noqa: E402\\n+\\n+\\n+D = Path(\\\"/task/data\\\")\\n+END = 24_786_800\\n+DAY = 86_400\\n+\\n+\\n+def main() -> None:\\n+ cat = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+ ids = cat.item_id.to_numpy(np.int64)\\n+ id_to_i = pd.Series(np.arange(len(ids), dtype=np.int32), index=ids)\\n+ targets = set(pd.read_parquet(D / \\\"target_users.parquet\\\").uid.astype(int))\\n+ with np.load(D / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ zu, zi, packed = z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+ eligible = np.unpackbits(packed, axis=1, count=len(zi), bitorder=\\\"little\\\").astype(bool)\\n+ uidrow = {int(v): i for i, v in enumerate(zu)}\\n+ itemcol = {int(v): i for i, v in enumerate(zi)}\\n+ vl = pd.read_parquet(D / \\\"val_listens.parquet\\\")\\n+ vl = vl[(vl.uid.isin(targets)) & (vl.item_id.isin(ids)) & (vl.is_organic == 1) & (vl.played_ratio_pct >= 80)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().assign(grade=1)\\n+ vk = pd.read_parquet(D / \\\"val_likes.parquet\\\")\\n+ vk = vk[(vk.uid.isin(targets)) & (vk.item_id.isin(ids)) & (vk.is_organic == 1)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().assign(grade=3)\\n+ gold_frame = pd.concat([vl, vk]).sort_values(\\\"grade\\\").drop_duplicates([\\\"uid\\\", \\\"item_id\\\"], keep=\\\"last\\\")\\n+ gold_frame = gold_frame[[eligible[uidrow[int(u)], itemcol[int(i)]] for u, i in gold_frame[[\\\"uid\\\", \\\"item_id\\\"]].itertuples(index=False, name=None)]]\\n+ gold = {int(u): dict(zip(g.item_id.astype(int), g.grade.astype(int))) for u, g in gold_frame.groupby(\\\"uid\\\")}\\n+\\n+ x = pd.read_parquet(D / \\\"train_listens.parquet\\\", columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ x = x[x.item_id.isin(ids) & (x.is_organic == 1) & (x.played_ratio_pct >= 80)].copy()\\n+ x[\\\"i\\\"] = x.item_id.map(id_to_i).astype(np.int32)\\n+ k = pd.read_parquet(D / \\\"train_likes.parquet\\\")\\n+ k = k[k.item_id.isin(ids) & (k.is_organic == 1)].copy()\\n+ k[\\\"i\\\"] = k.item_id.map(id_to_i).astype(np.int32)\\n+\\n+ def score(values: np.ndarray) -> float:\\n+ order = np.argsort(-values, kind=\\\"stable\\\")\\n+ results = []\\n+ for uid, truth in gold.items():\\n+ chosen = order[eligible[uidrow[uid], order]][:10]\\n+ results.append(ndcg_at_k(ids[chosen].tolist(), truth, 10))\\n+ return float(np.mean(results))\\n+\\n+ results = []\\n+ for days in (3, 4, 5, 6, 7, 8, 9, 10, 12, 14):\\n+ lm = x.timestamp >= END - days * DAY\\n+ km = k.timestamp >= END - days * DAY\\n+ lc = np.bincount(x.loc[lm, \\\"i\\\"], minlength=len(ids)).astype(float)\\n+ kc = np.bincount(k.loc[km, \\\"i\\\"], minlength=len(ids)).astype(float)\\n+ for mult in (2, 4, 7, 10, 15, 25):\\n+ results.append((score(np.log1p(lc + mult * kc)), f\\\"window={days} like={mult}\\\"))\\n+ # Unique-listener velocity is less sensitive to a few heavy listeners.\\n+ uc = np.bincount(x.loc[lm].drop_duplicates([\\\"uid\\\", \\\"i\\\"]).i, minlength=len(ids)).astype(float)\\n+ uk = np.bincount(k.loc[km].drop_duplicates([\\\"uid\\\", \\\"i\\\"]).i, minlength=len(ids)).astype(float)\\n+ for mult in (2, 7, 15):\\n+ results.append((score(np.log1p(uc + mult * uk)), f\\\"unique_window={days} like={mult}\\\"))\\n+ age_l = (END - x.timestamp.to_numpy()) / DAY\\n+ age_k = (END - k.timestamp.to_numpy()) / DAY\\n+ for half in (1, 2, 3, 4, 5, 7, 10, 14):\\n+ lc = np.bincount(x.i, weights=np.exp2(-age_l / half), minlength=len(ids))\\n+ kc = np.bincount(k.i, weights=np.exp2(-age_k / half), minlength=len(ids))\\n+ for mult in (2, 4, 7, 10, 15):\\n+ results.append((score(np.log1p(lc + mult * kc)), f\\\"halflife={half} like={mult}\\\"))\\n+ for value, name in sorted(results, reverse=True)[:20]:\\n+ print(f\\\"{value:.6f} {name}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/search_prior.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:53:37.916Z Script running with cell ID 40 Wall time 11.0 seconds Output: {} ## Step 77 · agent · 2026-08-31T18:53:39.592Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:40.355Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:53:57.084Z Script completed Wall time 16.7 seconds Output: SESSION_ID=21412 ## Step 78 · agent · 2026-08-31T18:53:58.460Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:00.348Z { "code": "const r = await tools.write_stdin({session_id:21412,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:54:11.367Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:54:12.906Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:13.671Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:54:30.372Z Script completed Wall time 16.7 seconds Output: SESSION_ID=21412 ## Step 80 · agent · 2026-08-31T18:54:33.513Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:34.321Z { "code": "const r = await tools.write_stdin({session_id:21412,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:54:40.066Z Script completed Wall time 5.7 seconds Output: 0.034450 unique_window=3 like=7 0.034030 unique_window=3 like=15 0.033737 unique_window=6 like=15 0.033568 unique_window=5 like=15 0.033528 unique_window=5 like=7 0.033440 unique_window=4 like=7 0.033349 unique_window=6 like=7 0.033298 unique_window=3 like=2 0.033146 unique_window=4 like=15 0.032748 unique_window=10 like=15 0.032533 unique_window=5 like=2 0.032478 unique_window=10 like=7 0.032468 unique_window=8 like=7 0.032346 unique_window=4 like=2 0.032309 unique_window=9 like=15 0.032159 unique_window=7 like=7 0.032091 unique_window=8 like=15 0.032024 unique_window=6 like=2 0.031903 unique_window=12 like=15 0.031864 unique_window=9 like=7 ## Step 81 · agent · 2026-08-31T18:54:51.393Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:02.511Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- recent_strong = strong[\\n- (strong.timestamp >= feature_cutoff - WEEK) & (strong.timestamp < feature_cutoff)\\n- ]\\n+ prior_window = 3 * 86_400\\n+ recent_strong = strong[\\n+ (strong.timestamp >= feature_cutoff - prior_window)\\n+ & (strong.timestamp < feature_cutoff)\\n+ ].drop_duplicates([\\\"u\\\", \\\"i\\\"])\\n recent_like = like_targets[\\n- (like_targets.timestamp >= feature_cutoff - WEEK)\\n+ (like_targets.timestamp >= feature_cutoff - prior_window)\\n & (like_targets.timestamp < feature_cutoff)\\n- ]\\n+ ].drop_duplicates([\\\"u\\\", \\\"i\\\"])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 15 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 10\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:55:13.532Z Script running with cell ID 43 Wall time 11.0 seconds Output: {} ## Step 82 · agent · 2026-08-31T18:55:15.447Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:16.228Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:55:32.697Z Script completed Wall time 16.5 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 25.1s SESSION_ID=52123 ## Step 83 · agent · 2026-08-31T18:55:34.465Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:36.320Z { "code": "const r = await tools.write_stdin({session_id:52123,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:55:47.353Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 84 · agent · 2026-08-31T18:55:49.022Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:49.822Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:56:06.360Z Script completed Wall time 16.5 seconds Output: /app/solution/train_ranker.py:438: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) total_loss += float(loss) * len(c) epoch 01/15 loss=6.5220 scale=0.878 epoch 02/15 loss=6.4758 scale=0.860 epoch 03/15 loss=6.4427 scale=0.853 epoch 04/15 loss=6.4049 scale=0.839 epoch 05/15 loss=6.3554 scale=0.830 epoch 06/15 loss=6.2873 scale=0.819 SESSION_ID=52123 ## Step 85 · agent · 2026-08-31T18:56:07.974Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:09.810Z { "code": "const r = await tools.write_stdin({session_id:52123,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:56:20.842Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 86 · agent · 2026-08-31T18:56:22.666Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:23.492Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 7000 } ### Tool output · 2026-08-31T18:56:39.847Z Script completed Wall time 16.4 seconds Output: epoch 07/15 loss=6.2116 scale=0.816 epoch 08/15 loss=6.1162 scale=0.804 epoch 09/15 loss=6.0257 scale=0.795 epoch 10/15 loss=5.9202 scale=0.782 epoch 11/15 loss=5.8198 scale=0.772 epoch 12/15 loss=5.7096 scale=0.758 epoch 13/15 loss=5.6202 scale=0.747 SESSION_ID=52123 ## Step 87 · agent · 2026-08-31T18:56:41.620Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:43.522Z { "code": "const r = await tools.write_stdin({session_id:52123,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:56:48.185Z Script completed Wall time 4.7 seconds Output: epoch 14/15 loss=5.5267 scale=0.732 epoch 15/15 loss=5.4185 scale=0.722 /app/solution/validation.parquet /app/solution/validation.parquet ## Step 88 · agent · 2026-08-31T18:56:50.089Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:52.284Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:56:55.687Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.034038, "quality_users": 4263 } } ## Step 89 · agent · 2026-08-31T18:57:02.029Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:06.293Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 10,20,40,80,160\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:57:17.310Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 90 · agent · 2026-08-31T18:57:18.998Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:19.828Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:57:36.474Z Script completed Wall time 16.6 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 24.3s /app/solution/validation_mix_10p0.parquet SESSION_ID=81383 ## Step 91 · agent · 2026-08-31T18:57:40.405Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:42.246Z { "code": "const r = await tools.write_stdin({session_id:81383,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:57:43.681Z Script completed Wall time 1.4 seconds Output: /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet /app/solution/validation_mix_160p0.parquet /app/solution/validation_mix_160p0.parquet ## Step 92 · agent · 2026-08-31T18:57:45.262Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:51.299Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet /app/solution/validation_mix_160p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | awk -v f=\\\"$(basename \\\"$f\\\")\\\" '/quality_ndcg/{gsub(\\\",\\\",\\\"\\\"); print f,$2}'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:58:02.319Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 93 · agent · 2026-08-31T18:58:04.222Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:05.025Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:58:08.052Z Script completed Wall time 3.0 seconds Output: validation_mix_10p0.parquet 0.034038 validation_mix_20p0.parquet 0.034415 validation_mix_40p0.parquet 0.034192 validation_mix_80p0.parquet 0.034066 validation_mix_160p0.parquet 0.033931 ## Step 94 · agent · 2026-08-31T18:58:15.947Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:19.456Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 15,18,22,25,30\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:58:30.474Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 95 · agent · 2026-08-31T18:58:32.659Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:33.040Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:58:49.636Z Script completed Wall time 16.6 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 24.3s /app/solution/validation_mix_15p0.parquet SESSION_ID=11212 ## Step 96 · agent · 2026-08-31T18:58:51.234Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:53.126Z { "code": "const r = await tools.write_stdin({session_id:11212,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:58:56.873Z Script completed Wall time 3.7 seconds Output: /app/solution/validation_mix_18p0.parquet /app/solution/validation_mix_22p0.parquet /app/solution/validation_mix_25p0.parquet /app/solution/validation_mix_30p0.parquet /app/solution/validation_mix_30p0.parquet ## Step 97 · agent · 2026-08-31T18:59:00.756Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:06.837Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_mix_15p0.parquet /app/solution/validation_mix_18p0.parquet /app/solution/validation_mix_22p0.parquet /app/solution/validation_mix_25p0.parquet /app/solution/validation_mix_30p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | awk -v f=\\\"$(basename \\\"$f\\\")\\\" '/quality_ndcg/{gsub(\\\",\\\",\\\"\\\"); print f,$2}'; done\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:59:17.859Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T18:59:19.526Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:20.376Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T18:59:23.560Z Script completed Wall time 3.2 seconds Output: validation_mix_15p0.parquet 0.034282 validation_mix_18p0.parquet 0.034598 validation_mix_22p0.parquet 0.034384 validation_mix_25p0.parquet 0.034357 validation_mix_30p0.parquet 0.034366 ## Step 99 · agent · 2026-08-31T18:59:31.755Z ### Thinking unavailable — no text recorded Model selection is frozen: a blend weight of 18 gives the best public score, 0.034598 NDCG@10. I’m now doing the required final refit: the public validation window becomes observed training history/labels, the user histories advance to the test cutoff, and the test eligibility masks are applied when producing exactly 100 items per user. ### exec · 2026-08-31T18:59:44.129Z { "code": "const p = await tools.update_plan({explanation:\"Implemented and tuned the two-tower. The selected validation configuration scores 0.034598 NDCG@10; proceeding with the prescribed train+validation refit.\",plan:[\n {step:\"Audit schemas, sizes, eligibility format, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-based PyTorch two-tower and ranking pipeline\",status:\"completed\"},\n {step:\"Train and tune variants on the public validation window\",status:\"completed\"},\n {step:\"Refit selected model on train + validation history\",status:\"in_progress\"},\n {step:\"Generate and validate final 100-per-user submission and README\",status:\"pending\"}\n]});\ntext(p);\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --windows 16 --epochs 15 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 18\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:59:55.150Z Script running with cell ID 54 Wall time 11.0 seconds Output: {} ## Step 100 · agent · 2026-08-31T18:59:57.058Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:57.818Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T19:00:14.309Z Script completed Wall time 16.5 seconds Output: cutoff=24786800 contexts=4291 labels=37997 cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 63056 contexts, 586254 labels in 26.1s SESSION_ID=95393 ## Step 101 · agent · 2026-08-31T19:00:15.843Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:18.623Z { "code": "const r = await tools.write_stdin({session_id:95393,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:00:29.653Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T19:00:31.673Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:32.458Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T19:00:48.656Z Script completed Wall time 16.2 seconds Output: /app/solution/train_ranker.py:438: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) total_loss += float(loss) * len(c) epoch 01/15 loss=6.5248 scale=0.871 epoch 02/15 loss=6.4760 scale=0.854 epoch 03/15 loss=6.4433 scale=0.839 epoch 04/15 loss=6.4048 scale=0.828 epoch 05/15 loss=6.3529 scale=0.810 epoch 06/15 loss=6.2773 scale=0.800 SESSION_ID=95393 ## Step 103 · agent · 2026-08-31T19:00:50.170Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:52.028Z { "code": "const r = await tools.write_stdin({session_id:95393,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:01:03.062Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T19:01:04.846Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:05.614Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T19:01:22.067Z Script completed Wall time 16.5 seconds Output: epoch 07/15 loss=6.2004 scale=0.792 epoch 08/15 loss=6.1015 scale=0.768 epoch 09/15 loss=6.0146 scale=0.763 epoch 10/15 loss=5.9051 scale=0.753 epoch 11/15 loss=5.7984 scale=0.740 epoch 12/15 loss=5.6956 scale=0.723 SESSION_ID=95393 ## Step 105 · agent · 2026-08-31T19:01:23.546Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:25.441Z { "code": "const r = await tools.write_stdin({session_id:95393,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:01:36.458Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T19:01:38.171Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:39.451Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T19:01:39.575Z Script completed Wall time 0.0 seconds Output: epoch 13/15 loss=5.6007 scale=0.714 epoch 14/15 loss=5.5001 scale=0.695 epoch 15/15 loss=5.4142 scale=0.683 /app/solution/submission.parquet /app/solution/submission.parquet ## Step 107 · agent · 2026-08-31T19:02:00.813Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-08-31T19:02:11.199Z ### Thinking unavailable — no text recorded The final refit completed on 63,056 temporal contexts and 586,254 discovery labels, including validation as observed history. I’m at the artifact-hardening stage now: expressing the calibrated prior explicitly as an appended tower coordinate, regenerating from the saved final checkpoint, and running the same schema/eligibility invariants as the evaluator. ### exec · 2026-08-31T19:02:39.573Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\\n return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\\n+\\n+\\n+class RetrievalUserTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment user tower with a calibrated constant prior coordinate.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: UserTower, popularity_mix: float):\\n+ super().__init__()\\n+ self.base = base\\n+ self.coordinate = math.sqrt(popularity_mix)\\n+\\n+ def forward(self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor) -> torch.Tensor:\\n+ learned = self.base(pos, liked, disliked)\\n+ prior = learned.new_full((len(learned), 1), self.coordinate)\\n+ return torch.cat([learned, prior], dim=1)\\n+\\n+\\n+class RetrievalItemTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment item tower; all outputs are independently precomputable.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: ItemTower, popularity_mix: float):\\n+ super().__init__()\\n+ self.base = base\\n+ self.coordinate = math.sqrt(popularity_mix)\\n+\\n+ def forward(self, ids: torch.Tensor) -> torch.Tensor:\\n+ learned = self.base(ids)\\n+ prior = self.base.popularity[ids] * self.coordinate\\n+ return torch.cat([learned, prior], dim=1)\\n@@\\n def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float, output_name: str | None = None) -> Path:\\n@@\\n- all_items = model.item_tower(torch.arange(n_items)).float()\\n- # A dot-product-compatible popularity coordinate, selected on public\\n- # validation, is appended to both independently computed vectors.\\n- pop_coordinate = torch.from_numpy(data.popularity).float()\\n+ if popularity_mix < 0:\\n+ raise ValueError(\\\"popularity_mix must be nonnegative for dot-product retrieval\\\")\\n+ # The calibrated prior is an explicit extra dimension, so request-time\\n+ # relevance is literally one MIPS dot product with no reranking function.\\n+ user_tower = RetrievalUserTower(model.user_tower, popularity_mix)\\n+ item_tower = RetrievalItemTower(model.item_tower, popularity_mix)\\n+ all_items = item_tower(torch.arange(n_items)).float()\\n@@\\n- users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\\n+ users = user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\\n scores = users @ all_items.T\\n- scores += popularity_mix * pop_coordinate.unsqueeze(0)\\n@@\\n- total_loss += float(loss) * len(c)\\n+ total_loss += float(loss.detach()) * len(c)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:02:39.615Z Script failed Wall time 0.0 seconds Output: Script error: apply_patch verification failed: Failed to find expected lines in /app/solution/train_ranker.py: def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float, output_name: str | None = None) -> Path: ## Step 109 · agent · 2026-08-31T19:02:42.347Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:45.091Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"class TwoTower|def rank|total_loss\\\" /app/solution/train_ranker.py && sed -n '378,475p' /app/solution/train_ranker.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:02:45.198Z Script completed Wall time 0.0 seconds Output: 384:class TwoTower(nn.Module): 415: total_loss = 0.0 438: total_loss += float(loss) * len(c) 441: f"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}", 448:def rank( ) def forward(self, ids: torch.Tensor) -> torch.Tensor: return self.network(torch.cat([self.embedding(ids), self.popularity[ids]], dim=1)) class TwoTower(nn.Module): def __init__(self, n_items: int, dim: int, popularity: np.ndarray): super().__init__() self.user_tower = UserTower(n_items, dim) self.item_tower = ItemTower(n_items, dim, popularity) self.logit_scale = nn.Parameter(torch.tensor(0.0)) def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor: return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0) def train_model(data: Prepared, args: argparse.Namespace) -> TwoTower: n_contexts, n_items = len(data.pos), len(data.item_ids) model = TwoTower(n_items, args.dim, data.popularity) optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=2e-5) rng = np.random.default_rng(args.seed) batch_size = args.batch_size pos_all = torch.from_numpy(data.pos).long() like_all = torch.from_numpy(data.liked).long() dislike_all = torch.from_numpy(data.disliked).long() for epoch in range(args.epochs): model.train() chosen = np.empty(n_contexts, dtype=np.int64) sample_weight = np.empty(n_contexts, dtype=np.float32) for c in range(n_contexts): lo, hi = data.target_offsets[c : c + 2] weights = data.target_weights[lo:hi] j = int(rng.choice(hi - lo, p=weights / weights.sum())) chosen[c] = data.target_items[lo + j] sample_weight[c] = weights[j] order = rng.permutation(n_contexts) total_loss = 0.0 seen = 0 for start in range(0, n_contexts, batch_size): c = order[start : start + batch_size] targets = chosen[c] # Unique batch positives plus uniform catalog negatives preserve a # learnable popularity prior while keeping complexity sampled. candidates = np.unique( np.concatenate([targets, rng.integers(0, n_items, args.negatives, dtype=np.int64)]) ) lookup = {int(v): j for j, v in enumerate(candidates)} labels = torch.tensor([lookup[int(v)] for v in targets], dtype=torch.long) ct = torch.from_numpy(candidates).long() users = model.user_tower(pos_all[c], like_all[c], dislike_all[c]) items = model.item_tower(ct) logits = model.score(users, items) losses = nn.functional.cross_entropy(logits, labels, reduction="none") weights = torch.from_numpy(np.sqrt(sample_weight[c])) loss = (losses * weights).sum() / weights.sum() optimizer.zero_grad(set_to_none=True) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), 5.0) optimizer.step() total_loss += float(loss) * len(c) seen += len(c) print( f"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}", flush=True, ) return model @torch.inference_mode() def rank( model: TwoTower, data: Prepared, stage: str, popularity_mix: float, output_name: str | None = None, ) -> Path: model.eval() n_users, n_items = len(data.uids), len(data.item_ids) all_items = model.item_tower(torch.arange(n_items)).float() # A dot-product-compatible popularity coordinate, selected on public # validation, is appended to both independently computed vectors. pop_coordinate = torch.from_numpy(data.popularity).float() eligibility_name = "test_eligible_candidates.npz" if stage == "final" else "validation_eligible_candidates.npz" with np.load(DATA / eligibility_name) as z: packed = z["packed_eligible"].copy() rows: list[pd.DataFrame] = [] pos = torch.from_numpy(data.inference_pos).long() liked = torch.from_numpy(data.inference_liked).long() disliked = torch.from_numpy(data.inference_disliked).long() for start in range(0, n_users, 64): end = min(start + 64, n_users) users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float() scores = users @ all_items.T scores += popularity_mix * pop_coordinate.unsqueeze(0) eligible = np.unpackbits( packed[start:end], axis=1, count=n_items, bitorder="little" ).astype(bool) ## Step 110 · agent · 2026-08-31T19:02:47.596Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:11.999Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\\n return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\\n+\\n+\\n+class RetrievalUserTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment user tower with a calibrated constant prior coordinate.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: UserTower, popularity_mix: float):\\n+ super().__init__()\\n+ self.base = base\\n+ self.coordinate = math.sqrt(popularity_mix)\\n+\\n+ def forward(\\n+ self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor\\n+ ) -> torch.Tensor:\\n+ learned = self.base(pos, liked, disliked)\\n+ prior = learned.new_full((len(learned), 1), self.coordinate)\\n+ return torch.cat([learned, prior], dim=1)\\n+\\n+\\n+class RetrievalItemTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment item tower; all outputs are independently precomputable.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: ItemTower, popularity_mix: float):\\n+ super().__init__()\\n+ self.base = base\\n+ self.coordinate = math.sqrt(popularity_mix)\\n+\\n+ def forward(self, ids: torch.Tensor) -> torch.Tensor:\\n+ learned = self.base(ids)\\n+ prior = self.base.popularity[ids] * self.coordinate\\n+ return torch.cat([learned, prior], dim=1)\\n@@\\n- total_loss += float(loss) * len(c)\\n+ total_loss += float(loss.detach()) * len(c)\\n@@\\n- all_items = model.item_tower(torch.arange(n_items)).float()\\n- # A dot-product-compatible popularity coordinate, selected on public\\n- # validation, is appended to both independently computed vectors.\\n- pop_coordinate = torch.from_numpy(data.popularity).float()\\n+ if popularity_mix < 0:\\n+ raise ValueError(\\\"popularity_mix must be nonnegative for dot-product retrieval\\\")\\n+ # The prior is an explicit extra dimension: relevance is literally one\\n+ # MIPS dot product with no request-time reranking function.\\n+ user_tower = RetrievalUserTower(model.user_tower, popularity_mix)\\n+ item_tower = RetrievalItemTower(model.item_tower, popularity_mix)\\n+ all_items = item_tower(torch.arange(n_items)).float()\\n@@\\n- users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\\n+ users = user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\\n scores = users @ all_items.T\\n- scores += popularity_mix * pop_coordinate.unsqueeze(0)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:03:12.040Z Script completed Wall time 0.0 seconds Output: {} ## Step 111 · agent · 2026-08-31T19:03:15.277Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:19.287Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --windows 16 --dim 64 --rank-only --popularity-mix 18\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:03:30.309Z Script running with cell ID 61 Wall time 11.0 seconds Output: ## Step 112 · agent · 2026-08-31T19:03:32.188Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:33.000Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T19:03:49.468Z Script completed Wall time 16.5 seconds Output: cutoff=24786800 contexts=4291 labels=37997 cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 63056 contexts, 586254 labels in 26.2s SESSION_ID=88529 ## Step 113 · agent · 2026-08-31T19:03:54.079Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:55.742Z { "code": "const r = await tools.write_stdin({session_id:88529,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:03:55.867Z Script completed Wall time 0.0 seconds Output: /app/solution/submission.parquet /app/solution/submission.parquet ## Step 114 · agent · 2026-08-31T19:04:13.288Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:53.072Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower ranker\\n+\\n+`train_ranker.py` is the complete training and inference pipeline. It processes\\n+event lists directly and never constructs or factorizes a global user-item\\n+matrix.\\n+\\n+## Model\\n+\\n+The user tower has no user-ID embedding. It embeds and attention-pools three\\n+bounded, cutoff-correct catalog histories: the 96 most recent positive listens,\\n+64 active likes, and 24 active dislikes. Their pooled vectors and activity\\n+counts pass through LayerNorm and a two-layer GELU MLP. The item tower uses a\\n+trainable item embedding, a three-day unique-listener organic velocity feature,\\n+LayerNorm, and a two-layer GELU MLP. Both deployment towers emit 65 dimensions:\\n+64 learned dimensions plus one calibrated velocity coordinate. Candidate item\\n+vectors are independently precomputable. The only relevance calculation is\\n+the dot product of the two 65-dimensional outputs, so the model is directly\\n+usable for maximum-inner-product retrieval.\\n+\\n+Training examples are rolling weekly temporal backtests. Each context predicts\\n+organic, previously unseen catalog tracks in its following week. The loss is\\n+sampled softmax cross-entropy with in-batch positive candidates and uniform\\n+catalog negatives; organic likes receive 3x sampling/loss emphasis. This yields\\n+58,765 validation-stage contexts (548,257 labels). The final refit adds the\\n+public window as observed outcomes/history, for 63,056 contexts and 586,254\\n+labels. Eligibility is applied only after retrieval scoring and always comes\\n+from the supplied cutoff-specific packed masks.\\n+\\n+## Validation\\n+\\n+The selected validation ranking is `validation_mix_18p0.parquet` and scores\\n+**0.034598 graded NDCG@10** over 4,263 scored users with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_mix_18p0.parquet\\n+```\\n+\\n+No `val_*` event is used as a feature for that ranking. Validation events are\\n+included only in the final refit, as required.\\n+\\n+## Final command\\n+\\n+To reproduce training and `/app/solution/submission.parquet`:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_ranker.py \\\\\\n+ --stage final --windows 16 --epochs 15 --dim 64 \\\\\\n+ --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 18\\n+```\\n+\\n+The checked-in `two_tower_final.pt` is the fitted CPU checkpoint. To regenerate\\n+only the final ranking from it, use the same command with `--rank-only` (the\\n+saved checkpoint supplies the fitted weights).\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\\nf=pd.read_parquet(P)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(f,targets).sort_values(['uid','rank']).reset_index(drop=True)\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64,copy=True); items=z['item_ids'].astype(np.int64,copy=True); packed=z['packed_eligible'].copy()\\nuidrow={int(v):i for i,v in enumerate(uids)}; itemcol={int(v):i for i,v in enumerate(items)}\\nvalid=all(((int(packed[uidrow[int(u)], itemcol[int(i)]//8]) >> (itemcol[int(i)]%8)) & 1) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'ranks_min':int(f['rank'].min()),'ranks_max':int(f['rank'].max()),'all_test_eligible':valid,'dtypes':{k:str(v) for k,v in f.dtypes.items()},'bytes':P.stat().st_size})\\nPY\\nsha256sum /app/solution/submission.parquet /app/solution/two_tower_final.pt\\nls -lh /app/solution/submission.parquet /app/solution/two_tower_final.pt /app/solution/README.md /app/solution/train_ranker.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:04:55.813Z Script completed Wall time 2.7 seconds Output: {} {'rows': 845600, 'users': 8456, 'items': 478, 'ranks_min': 1, 'ranks_max': 100, 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'bytes': 1068817} ef1295807e7e3fc3bb902870e5d74c412572b97578307a4a42d1bd0620c27906 /app/solution/submission.parquet 3b612d179b4099b9391e42fa3bf3e91876aae827a56d576c6300a05d15736fe2 /app/solution/two_tower_final.pt -rw-r--r-- 1 agent agent 2.4K Aug 31 19:04 /app/solution/README.md -rw-r--r-- 1 agent agent 1.1M Aug 31 19:03 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 24K Aug 31 19:03 /app/solution/train_ranker.py -rw-r--r-- 1 agent agent 15M Aug 31 19:01 /app/solution/two_tower_final.pt ## Step 115 · agent · 2026-08-31T19:05:11.270Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:14.246Z { "code": "const r = await tools.exec_command({cmd:\"cat /app/solution/run_final.json && cat /app/solution/run_validation.json && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_mix_18p0.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:05:17.657Z Script completed Wall time 3.4 seconds Output: { "stage": "final", "architecture": "history pooling user tower + independent item MLP tower", "args": { "stage": "final", "windows": 16, "dim": 64, "epochs": 10, "batch_size": 384, "negatives": 256, "lr": 0.002, "seed": 20260831, "popularity_mix": 18.0, "rank_only": true, "popularity_grid": "" }, "contexts": 63056, "labels": 586254, "ranking": "/app/solution/submission.parquet" } { "stage": "validation", "architecture": "history pooling user tower + independent item MLP tower", "args": { "stage": "validation", "windows": 16, "dim": 64, "epochs": 10, "batch_size": 384, "negatives": 256, "lr": 0.002, "seed": 20260831, "popularity_mix": 0.0, "rank_only": true, "popularity_grid": "15,18,22,25,30" }, "contexts": 58765, "labels": 548257, "ranking": "/app/solution/validation_mix_30p0.parquet" } { "model": "/app/solution/validation_mix_18p0.parquet", "scores": { "quality_ndcg@10": 0.034598, "quality_users": 4263 } } ## Step 116 · agent · 2026-08-31T19:05:32.534Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:36.548Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-mix 18 && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:05:47.567Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 117 · agent · 2026-08-31T19:05:50.094Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:50.891Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T19:06:06.729Z Script completed Wall time 15.8 seconds Output: cutoff=24182000 contexts=4295 labels=41723 cutoff=23577200 contexts=4254 labels=40500 cutoff=22972400 contexts=4109 labels=40317 cutoff=22367600 contexts=4050 labels=38201 cutoff=21762800 contexts=4008 labels=38195 cutoff=21158000 contexts=3856 labels=34994 cutoff=20553200 contexts=3824 labels=36847 cutoff=19948400 contexts=3750 labels=33765 cutoff=19343600 contexts=3608 labels=32117 cutoff=18738800 contexts=3446 labels=32552 cutoff=18134000 contexts=3392 labels=31397 cutoff=17529200 contexts=3350 labels=30916 cutoff=16924400 contexts=3284 labels=30776 cutoff=16319600 contexts=3216 labels=29741 cutoff=15714800 contexts=3159 labels=28758 cutoff=15110000 contexts=3164 labels=27458 prepared 58765 contexts, 548257 labels in 24.7s SESSION_ID=30023 ## Step 118 · agent · 2026-08-31T19:06:08.534Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:10.411Z { "code": "const r = await tools.write_stdin({session_id:30023,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\nif(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:06:10.642Z Script completed Wall time 0.2 seconds Output: /app/solution/validation.parquet /app/solution/validation.parquet { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.034598, "quality_users": 4263 } } ## Step 119 · agent · 2026-08-31T19:06:18.962Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:24.010Z { "code": "const p = await tools.update_plan({explanation:\"Final refit, exact-dot-product regeneration, public-score confirmation, and structural/eligibility validation all completed.\",plan:[\n {step:\"Audit schemas, sizes, eligibility format, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-based PyTorch two-tower and ranking pipeline\",status:\"completed\"},\n {step:\"Train and tune variants on the public validation window\",status:\"completed\"},\n {step:\"Refit selected model on train + validation history\",status:\"completed\"},\n {step:\"Generate and validate final 100-per-user submission and README\",status:\"completed\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T19:06:24.088Z Script completed Wall time 0.0 seconds Output: {} ## Step 120 · agent · 2026-08-31T19:06:26.644Z ### Thinking unavailable — no text recorded Completed the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training pipeline: [train_ranker.py](/app/solution/train_ranker.py) - Documentation: [README.md](/app/solution/README.md) - Final checkpoint: [two_tower_final.pt](/app/solution/two_tower_final.pt) Public validation NDCG@10: **0.034598** over 4,263 users. Final artifact validation passed: - 845,600 rows - 8,456 users - Exactly 100 distinct items per user - Integer ranks 1–100 - Every item is test-eligible - Exact 65-dimensional user/item dot-product retrieval - No user-ID embedding or global user-item matrix